ai content case study

November 30th, 2023

In the whirlwind world of digital marketing, AI in Content Creation has exploded as a groundbreaking tool, reshaping how we produce and distribute content. By leveraging the power of artificial intelligence, brands are crafting narratives with unprecedented speed and precision.

It’s not just the speed, though; it’s the profound impact on quality and relevance that positions AI as an indispensable ally. Through case studies, we uncover the vibrant tapestry of success stories laying out the real-world efficiency of these AI-driven approaches. These stories not only inspire but also serve as beacons guiding us through the evolving terrain of digital content creation, especially as AI and SEO Content become increasingly intertwined.

Understanding the Role of AI in Content Creation

At the core of modern content production, artificial intelligence has become the ingenious architect, streamlining a myriad of processes that once bogged down creators. AI’s role extends beyond simple automation; it’s about enhancing creativity and delivering tailored content to audiences at scale. With the aid of diverse AI for Content Translation tools, language barriers are crumbling, allowing brands to communicate with global audiences as effortlessly as if they were next door neighbors.

Moreover, AI algorithms possess the uncanny ability to sift through data and trends, formulating AI and Content Strategy that resonate with target demographics. This strategic application of AI not only ensures relevance but also personalizes the user experience, leading to higher engagement rates. As technology evolves, the integration of AI in content creation becomes a pivotal element in constructing narratives that are both compelling and conversion-focused, twice affirming its indispensable status in the content creator’s toolkit.

Breaking Down AI Content Creation Case Studies

Peeling back the layers of AI’s involvement in the content realm, we arrive at a fascinating collection of AI Content Creation Case Studies . These narratives are not just stories—they are a testament to innovation and strategic implementation across various industries. From startups to established giants, you’ll find insights into how AI has revolutionized the content creation process, all brought to life through detailed recounting of real-life scenarios.

We’ll explore how brands harness AI for Social Media Content , producing posts that drive user engagement through the roof. These case studies will serve as a blueprint for understanding the transformative power AI wields in crafting targeted and interactive content. So buckle up, as we dive into each case, unpacking the methods and metrics behind AI-fuelled success stories in content creation.

Exploring Success Stories in AI-Driven Content Production

The blossoming era of AI content creation has ushered in numerous success stories that are nothing short of inspiring. Organizations and individuals alike have tapped into the wealth of AI’s capabilities, resulting in phenomenal content outcomes and formidable competitive advantages. Below, we delve into a curated selection of those triumphs, measuring the tangible impact brought by intelligent automation and AI in Copywriting .

Case Study	Outcomes	Key Insights
Global Retail Brand’s AI-Powered Email Campaigns	Increased open rate by 35%, CTR by 20%	Personalization at scale can significantly boost engagement metrics.
AI-Enhanced Blogging Platform	Content production time reduced by 50%, SEO visibility uplifted by 30%	AI speeds up content creation and effectively optimizes for search engines.
Digital News Outlet Using	100+ AI-written articles published monthly, with comparable readability scores to human-written content	The use of AI in writing can maintain quality while significantly increasing content output.

These examples vividly illustrate the potential for AI to not just enhance but revolutionize content creation. Whether boosting user engagement through data-driven personalization or reducing the operational overhead of producing fresh, SEO-friendly content, the success stories above demonstrate that, when executed with a well-devised strategy, AI’s role in content production is indeed a game-changer. These case studies underscore AI’s robustness in creating rich, optimized content while highlighting its effectiveness twice, marking a new dawn for the content industry.

Insights Gained from AI Content Creation Case Studies

Personalization at scale is the new standard.

The first and perhaps most striking insight is the transformative effect of personalization. AI has enabled brands to tailor content to individual preferences and behaviors like never before. This strategy not only meets but exceeds customer expectations, fostering loyalty and driving conversion rates. Through AI in Email Marketing , companies can automate and personalize messaging to an unprecedented degree, which has proven to enhance engagement in case studies examined.

Efficiency Meets Quality in Content Production

Another important insight is the symbiotic relationship between efficiency and quality. AI content creation tools have demonstrated that reducing time spent on content production doesn’t mean sacrificing quality. With advancements in AI, creators can now turn around high-quality, SEO-friendly pieces faster than ever, allowing businesses to keep pace with the demand for fresh, relevant content.

Overcoming the Content Volume Challenge with AI

Facing the immense pressure of producing voluminous high-quality content, many enterprises encounter scalability challenges. AI content creation enables businesses to overcome this hurdle, as evident from the case studies showing how digital news outlets use AI to maintain a steady stream of engaging content. The introduction of AI in News Writing has particularly demonstrated the potential to keep up with the 24/7 news cycle without compromising the richness of editorial content.

Embracing AI and Data-Driven Content Strategy

The need for robust data analysis to guide content strategy is another major lesson from our case studies. Leveraging the predictive analytics and trend-spotting capabilities of AI enables the creation of content that not just resonates with the present audience but also anticipates future shifts. This data-driven approach has been central to the success stories we’ve analyzed, as it powers a deeply insightful content strategy that aligns with user interests and behaviors.

In essence, these insights distilled from AI content creation case studies reflect a growing trend where AI is not a mere luxury but a necessity for competitive content strategy. The lessons learned underscore the need for blending intelligent technology with creative processes to produce content that is both effective and efficient. It’s a journey fraught with challenges, such as maintaining the human touch in automated content and ensuring ethical use of data, but the benefits, as the case studies articulate, are more than worth the pursuit.

Conversation with Experts: Perspectives on AI Content Creation

Gathering insights from the front lines, we spoke with thought leaders and seasoned practitioners about the burgeoning influence of AI on content creation. Dr. Jane Smith, a renowned AI researcher, believes that “AI presents a seismic shift in the content realm. It’s not replacing creativity but augmenting it, allowing us to reach unprecedented heights in personalization and relevance.” Such insights underscore the potential of AI as a powerful ally in content strategy.

Conversations with industry professionals reveal a consensus around AI’s pivotal role. According to Marcus Johnson, a digital strategy consultant, “The implementation of AI tools has become the cornerstone of competitive content marketing. It’s about optimizing the content lifecycle and delivering data-driven experiences.” Their observations extend to the growing importance of AI for Content Moderation , highlighting how AI is becoming instrumental in maintaining content quality and community standards at scale.

While experts extol AI’s capabilities, they also emphasize the ethical dimensions of such technology. Sarah Lopez, a content strategist and ethics advocate, remarks, “As we embrace AI, we must also engage with the Ethical Considerations of AI Content . The goal is to use these tools responsibly, ensuring transparency, fairness, and respect for user privacy.” This emphasis on ethical practice indicates a maturing industry cognizant of its influence and responsibilities in today’s digital landscape.

Overall, the expert perspectives paint a picture of AI content creation as a transformative force buoyed by innovation, yet grounded by a commitment to ethical considerations. Each interview reflects growing excitement tempered with cautious optimism, a balance advocating for the mindful use of AI in the quest to craft compelling, captivating, and responsible content.

Future Trends in AI Content Creation

Emergence of AI-Generated Interactive Experiences: As we’ve seen AI elevate static content to dynamic engagement, the future holds an even greater promise for AI and Interactive Content . Imagine AI crafting personalized, interactive stories or educational content that adapts to the reader’s responses and evolves in real-time.
Advancements in Natural Language Generation (NLG): AI’s linguistic capabilities are expected to grow more sophisticated, leading to hyper-realistic, context-aware content production. The nuanced understanding of different dialects and idioms will further tailor the content to reflect cultural subtleties.
AI Augmented Creative Design: Beyond textual content, AI is predicted to make significant strides in visual and multimedia content creation, helping to design stunning graphics, videos, and virtual environments with minimal human input.
Autonomous Content Decision-Making: Leveraging past performance data, AI might soon not just create content but decide when and where to publish it for maximum impact. This could revolutionize content scheduling and distribution strategies.
Data Privacy with AI: As consumers become more data-conscious, AI will need to evolve to generate compelling content without compromising individual privacy, thus ensuring ethical considerations are built into content strategies.
Evolution of AI Content Monetization Strategies: The Future of AI in Content Creation also brings potential shifts in how content is monetized, possibly fostering new revenue models that reward personalized and engaging content experiences.

These anticipated trends suggest that the case studies of today are just the precursor to an AI-infused content ecosystem rich with innovation. As we stand on the cusp of these advancements, the possibilities for content creation and strategy seem limitless, heralded by the intelligent, ethically-guided use of AI technologies.

Conclusion: AI Content Creation Case Studies Propel Industry Forward

The journey through these diverse AI content creation case studies underscores the transformative power of AI in the digital content domain. From personalizing email campaigns to automating news articles, we’ve seen quantifiable proof that leveraging AI not only enhances efficiency and engagement but also fosters innovation in storytelling. These case studies are more than just narratives; they are catalysts that drive the content creation industry toward a more dynamic, responsive, and personalized future.

As AI becomes deeply embedded in our approach to content, we are witnessing the rise of new frontiers, such as improving AI for Content Accessibility , ensuring that content reaches a wider audience, including those with disabilities. Moreover, the evolution of AI for Content Monetization strategies promises to unlock novel revenue streams, motivating creators and businesses to innovate continually. Thus, these success stories do much more than document the present; they inspire and lay the groundwork for an AI-augmented future in content creation, one where stories are not only told but brought to life in ways we’re just beginning to imagine.

10 Successful AI Marketing Campaigns & Case Studies [2024]

Are we on the brink of a marketing revolution where AI not only augments but fundamentally changes how we connect with customers? With predictions that the global value of AI in marketing could soar to an astonishing $108 billion by 2028, it’s clear that we are stepping into a future where AI is not just a tool but a transformative force reshaping the marketing landscape.

As AI continues to evolve, it paves the way for more creative, data-driven, and customer-centric marketing approaches, offering a competitive edge to businesses willing to embrace this technological advancement. The future of marketing, undoubtedly, lies in the intelligent integration of AI, making it an indispensable tool for marketers aiming to stay ahead in an increasingly digital world.

10 AI Marketing Campaigns That Show the Future of Digital Marketing

Case study 1: heinz a.i. ketchup.

Heinz Ketchup, a Kraft Heinz Company subsidiary, is an iconic brand in the ketchup market with over 150 years of history. Despite its market leadership, Heinz aimed to refresh its image and appeal to younger, tech-savvy demographics.

The campaign’s goal was to rejuvenate Heinz Ketchup’s brand image and connect with a younger audience interested in innovation and cultural trends, solidifying its position as the leading ketchup brand.

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Heinz harnessed the growing interest in text-to-image AI, particularly DALL-E 2, for a creative marketing campaign. The campaign involved AI-generated images from unique prompts like “Renaissance Ketchup Bottle,” effectively maintaining Heinz’s identity across various imaginative scenarios. The campaign featured AI-generated visuals, interactive social media engagement, special edition bottles, and a metaverse art gallery, initially launched in Canada and the US before gaining global traction.

1. Global Reach : Achieved over 850 million earned impressions globally, vastly exceeding media investment by over 2500%.

2. Media Coverage : Garnered extensive coverage from top publications in trade, art, tech, and lifestyle sectors.

3. Social Media Engagement : Witnessed a 38% higher engagement rate compared to previous campaigns.

4. Brand Participation : Attracted involvement from brands like Ducati and Sportsnet, requesting their AI Ketchup image mashups.

1. Technology in Branding : Utilizing AI effectively boosts brand relevance among younger audiences.

2. Interactive Marketing : Audience participation enhances engagement and memorability.

3. Cultural Relevance : Keeping up with cultural trends is essential for brand longevity.

4. Global Appeal : Innovative approaches can resonate across international markets.

5. Brand Affirmation : Such campaigns reinforce Heinz’s position as the top ketchup brand.

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Case Study 2: Nike’s AI-Driven “Never Done Evolving” Campaign

Company overview: nike.

Nike, a global leader in sportswear and athletic products, partnered with digital agency AKQA to create a groundbreaking advertising campaign. This campaign coincided with Nike’s 50th anniversary and Serena Williams’ retirement announcement.

The main goal was to honor Serena Williams’ legendary tennis career and illustrate her sports growth and evolution. The campaign aimed to merge technology with sports to create a unique tribute.

Using AI and machine learning, Nike and AKQA crafted a virtual simulation of a match between Serena Williams from two eras: her first Grand Slam victory in 1999 and her win at the 2017 Australian Open. This involved a detailed analysis of her gameplay, including aspects like shot selection, reaction times, and overall agility. The project created detailed avatars for each era of Serena, allowing for a virtual yet realistic depiction of how her style and skills evolved over the years.

1. Viewership Success : The virtual match, streamed on YouTube, attracted 1.7 million viewers, a substantial audience for such a unique concept.

2. Significant Engagement Growth : Compared to Nike’s standard content, this campaign achieved a 1,082% increase in organic views, indicating high audience engagement and interest.

3. Enhanced Sports Analysis : The campaign demonstrated the potential of AI in sports, providing a new way to analyze and interpret athletic performance and evolution.

1. Technology in Storytelling : The campaign exemplifies the effective use of AI and machine learning in storytelling, particularly in a sports context.

2. Engagement Through Innovation : Nike’s approach shows how innovative content can significantly increase audience engagement and interest.

3. Tribute to Athletes : The campaign serves as a model for how brands can creatively honor and celebrate the careers of iconic athletes.

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Case Study 3: Cosabella’s AI-Driven Email Marketing Transformation

Company: cosabella.

Cosabella, a luxury lingerie retailer, experienced a concerning plateau in sales after a period of steady growth. This situation prompted a strategic shift in their email marketing approach.

The primary objective was to revive and boost sales by enhancing the personalization and effectiveness of their email marketing campaigns.

Cosabella replaced its traditional digital ad agency with an AI platform from Emarsys. This technology allowed for the customization of emails sent to subscribers, leveraging shopper data to create highly personalized content and offers.

1. The campaign witnessed an immediate 4% uptick in email open rates.

2. There was a significant 60% increase in revenue generated through email marketing.

3. Holiday Campaign Success : The “12 Days of Cosabella” campaign generated 40-60% more sales than the previous year without offering discounts, relying solely on personalized content.

1. Personalization is Key : Tailoring content to individual customer preferences significantly boosts engagement and sales.

2. Data Utilization : Leveraging customer data in a targeted approach to marketing can significantly enhance revenue generation.

3. Customer Insights : Deep insights into customer behavior and preferences, derived from AI analysis, can refine marketing strategies.

4. Adaptability : The importance of being open to new technologies like AI to stay competitive and relevant in the digital age.

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Case Study 4: Tomorrow Sleep’s Organic Traffic Growth

Tomorrow Sleep, a startup in the mattress, entered the market in mid-2017 with an innovative product: the first connected sleep system. However, despite having a groundbreaking product, Tomorrow Sleep struggled initially with its online presence, particularly in content creation, leading to suboptimal organic traffic on its website.

The mattress industry, dominated by established players, presented a significant challenge for Tomorrow Sleep in gaining visibility and organic traffic. By mid-2018, the company realized that its initial content strategy was ineffective in standing out in the crowded market. Their website was not engaging enough to attract and retain customers.

1. Content Strategy Overhaul : The strategy focused on creating engaging content to attract quality traffic, improve search rankings with relevant keywords, and increase website engagement.

2. MarketMuse Application : Leveraging MarketMuse, an AI-driven content strategy platform, the approach involved using MarketMuse Research for topic insights and frequency analysis in expert content, and MarketMuse Compete to spot content gaps and opportunities within the top 20 search results.

3. SEO Enhancement : This involved optimizing existing pages with targeted keywords and semantic terms, developing new SEO-friendly content, building external links for off-page optimization, and employing design elements to boost user engagement. The use of infographics also played a key role in enhancing content attractiveness and reach.

The collaborative efforts led to remarkable growth in Tomorrow Sleep’s organic traffic, achieving a 100-fold increase from 4K to 400K monthly visitors within a year. For primary topics, this significant boost positioned Tomorrow Sleep ahead of its largest competitor, Casper. They achieved multiple positions on a single search engine results page (SERP), including the coveted featured snippet spot.

1. Content Optimization is Crucial : Optimizing web content for relevant keywords and user engagement is essential. Tomorrow Sleep’s success was partly due to optimizing their web pages for valuable keywords and semantic terms.

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Case Study 5: Euroflorist’s AI-Driven Website Optimization

Company overview: euroflorist.

Euroflorist, a leading European online florist, recognized the need to enhance its website’s performance to stay competitive in the digital marketplace. As an established brand in the floral industry, Euroflorist faced the challenge of optimizing its online presence to improve customer experience and drive sales.

The primary objective was to increase the website’s conversion rates. This meant attracting more visitors and converting a higher percentage of these visitors into customers.

Euroflorist adopted an AI-driven approach to website optimization, leveraging massively multivariate testing. This strategy involved:

1. Using AI for Testing : Employing AI platforms like Evolv AI, which allowed for testing thousands of website variations.

2. Data-Driven Decisions : Utilizing AI to analyze user interactions and preferences, thereby making informed decisions about website design and content.

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Implementation

The implementation process included:

1. Identifying Key Variables : Pinpointing various website elements, such as layout, color schemes, call-to-action buttons, and product placement for testing.

2. Creating Variants : Developing multiple variants of the website, each with different combinations of the identified variables.

3. Deploying AI Testing: Using Evolv AI to test these variants with real-time website visitors simultaneously, gathering user behavior and preferences data.

4. Analyzing Results : Continuously analyzing the performance of each variant to determine which combinations yielded the highest conversion rates.

1. Conversion Rate Increase : Euroflorist achieved a 4.3% increase in website conversion rates.

2. Optimized User Experience : The website became more user-friendly and appealing to customers, resulting in a better shopping experience.

1. The Power of AI in A/B Testing : The case study demonstrates how AI can transform traditional A/B testing into a more dynamic and effective process, allowing for the simultaneous testing of numerous variables.

2. Data-Driven Website Design : AI-driven multivariate testing provides valuable insights into customer preferences, enabling businesses to make informed decisions about website design.

3. Continuous Improvement : AI testing allows for ongoing optimization, as the AI continuously learns from user interactions, leading to progressively better website performance.

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Case Study 6: Starbucks Personalized AI Marketing

Starbucks, an internationally renowned coffeehouse chain with thousands of locations worldwide, has long been a food and beverage industry leader. Known for its high-quality coffee, innovative products, and strong brand identity, Starbucks continually seeks to enhance the customer experience and build lasting relationships with its patrons.

The primary objective was to deliver highly personalized experiences to Starbucks customers to increase engagement, drive sales, and foster long-term loyalty. By utilizing advanced technology, Starbucks intended to tailor its marketing strategies to meet individual customer preferences and behaviors, ensuring a more meaningful and relevant interaction with the brand.

Starbucks implemented the Deep Brew AI engine, an advanced artificial intelligence platform to analyze extensive customer data collected from the Starbucks app and loyalty program. This AI engine utilized machine learning algorithms to interpret data, uncover patterns, and generate insights into customer behavior. Deep Brew crafted personalized marketing messages and product recommendations tailored to each customer’s unique preferences and purchase history based on these insights. This included suggesting beverages and food items, promoting special offers, and providing timely notifications about new products or local store events.

Increased Revenue : The personalized recommendations provided by Deep Brew led to a notable increase in sales and average transaction value. Customers were more likely to purchase additional items or try new products that matched their tastes and preferences, resulting in higher overall revenue for Starbucks.
Enhanced Customer Loyalty : The improved personalization significantly boosted customer retention and engagement with the Starbucks loyalty program. By receiving relevant and appealing offers, customers felt more valued and connected to the brand, leading to increased participation in the loyalty program and higher repeat purchase rates.
Efficient Operations : The insights generated by the AI engine also helped Starbucks optimize its inventory management. By predicting customer demand more accurately, Starbucks could reduce waste, ensure the availability of popular items, and streamline its supply chain operations, ultimately leading to cost savings and improved efficiency.
Personalization Drives Sales : This case study highlights the critical role of personalization in marketing. Tailored marketing messages and product recommendations, customized to individual preferences, greatly enhance customer engagement and drive sales, demonstrating that personalized approaches can yield significant business benefits.
Data Utilization : Effectively utilizing customer data is essential for gaining valuable business insights. Starbucks’ use of Deep Brew demonstrates how analyzing data from various touchpoints can provide a deeper understanding of customer behavior, which can be used to optimize marketing strategies and improve overall business performance.
Customer Experience : Personalization boosts sales and improves the overall customer experience. By delivering relevant and timely messages, Starbucks created a more enjoyable and engaging customer experience, fostering stronger loyalty and long-term relationships. This case study highlights the critical role of personalization in building a loyal customer base and maintaining a competitive edge in the market.

Case Study 7: BMW’s AI-Driven Social Media Campaign

A prestigious luxury automobile manufacturer, BMW is renowned for its innovative vehicles and cutting-edge technology. With a strong global presence, BMW continuously seeks to elevate its brand awareness and engagement through various marketing initiatives, particularly in the digital space.

The primary goal was to develop a highly engaging social media campaign to promote BMW’s latest models. By leveraging advanced technology, BMW aimed to captivate its audience, increase brand visibility, and foster deeper customer connections through personalized and relevant content.

BMW partnered with IBM Watson to create a sophisticated AI-driven social media campaign. The AI platform analyzed vast social media data, including trends, user sentiments, and interactions. This analysis enabled personalized content creation and real-time responses tailored to each user’s specific preferences and behaviors. The AI-driven approach ensured that the content was engaging and highly relevant to the target audience.

Increased Engagement : The campaign achieved a remarkable 30% increase in social media engagement. The personalized and interactive content resonated well with users, leading to higher levels of likes, shares, comments, and overall interaction with the BMW brand.
Broader Reach : AI-driven content personalization significantly expanded BMW’s audience reach. By tailoring content to match user interests and preferences, the campaign attracted a wider and more diverse audience, enhancing brand visibility and recognition across various social media platforms.
Enhanced Customer Interaction : The use of AI allowed for real-time, personalized responses to customer queries and comments. This improved customer interaction and satisfaction, as users felt acknowledged and valued through prompt and relevant engagements. The enhanced interaction fostered a stronger connection between BMW and its audience, contributing to increased brand loyalty.
Social Media Optimization : The case study demonstrates that AI can effectively optimize social media content and interactions. By analyzing user data and tailoring content accordingly, AI enhances engagement and ensures that marketing efforts are more impactful and efficient.
Trend Analysis : Utilizing AI to analyze social media trends and user sentiments provides invaluable insights for developing effective marketing strategies. Understanding current trends and audience preferences allows brands to stay relevant and adapt their content to meet evolving demands.
Customer Engagement : Personalized interactions are key to fostering better customer relationships and building brand loyalty. AI-driven personalization helps create meaningful and relevant experiences for users, enhancing their overall engagement with the brand. This case study underscores the importance of leveraging AI to deliver tailored content and responses that resonate with the audience, ultimately driving brand success.

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Case Study 8: Coca Cola’s AI Powered Content Creation

Coca-Cola, a globally recognized leader in the beverage industry, is known for its iconic products and innovative marketing strategies. With a rich history and a vast portfolio of brands, Coca-Cola constantly seeks new ways to engage its audience and stay ahead in the competitive market.

The primary objective was to streamline the content creation process, making it more efficient while enhancing the creativity and relevance of the marketing materials. Coca-Cola aimed to produce engaging and personalized content that would resonate with diverse consumer segments across various platforms.

Coca-Cola implemented an AI-driven content creation platform that used advanced algorithms to analyze vast amounts of consumer data. This platform generated personalized marketing content, including advertisements and social media posts, tailored to specific audience preferences and behaviors. The AI analyzed data points such as consumer interactions, purchasing patterns, and social media activity to create content that was highly relevant and engaging.

Increased Content Production : The implementation of AI technology enabled Coca-Cola to accelerate its content creation process significantly. The platform allowed for the rapid production of a higher volume of marketing materials, ensuring that Coca-Cola could maintain a consistent and dynamic presence across various channels.
Enhanced Engagement : The personalized content generated by the AI platform led to higher engagement rates across Coca-Cola’s marketing channels. By delivering tailored messages that resonated with specific audience segments, Coca-Cola was able to capture and retain the attention of its consumers more effectively.
Cost Efficiency : The AI-driven approach not only accelerated content creation but also reduced associated costs. By automating parts of the content creation process, Coca-Cola was able to allocate resources more efficiently while maintaining high-quality output. This cost efficiency allowed the company to invest in other areas of its marketing strategy.
Content Personalization : This case study highlights the significant impact of personalized marketing content. AI can generate content that closely aligns with the preferences and behaviors of target audiences, resulting in more meaningful and engaging interactions.
Efficiency : The use of AI in content creation streamlines the process, saving both time and resources. Coca-Cola’s experience demonstrates how AI can enhance operational efficiency without compromising the quality of the content produced.
Creative Innovation : AI tools can offer unique insights and ideas that enhance creativity in marketing. By analyzing consumer data and trends, AI can suggest innovative approaches and concepts, helping brands like Coca-Cola stay ahead in a rapidly evolving market.

Case Study 9: Sephora’s AI Powered Beauty Advisor

Sephora, a leading global beauty retailer, is renowned for its extensive range of beauty products and innovative shopping experiences. With stores worldwide and a robust online presence, Sephora continuously seeks to enhance its customer experience through cutting-edge technology and personalized services.

The primary goal was to provide highly personalized beauty product recommendations to enhance customer satisfaction and boost sales. Sephora aimed to leverage technology to create an engaging and interactive shopping experience that would set it apart from competitors and build stronger relationships with its customers.

Sephora launched the Virtual Artist, an AI-powered beauty advisor that utilized facial recognition and augmented reality (AR) technologies. This innovative tool enabled customers to virtually try on makeup products, receive personalized product recommendations, and simulate different makeup looks in real time. The Virtual Artist analyzed customers’ facial features and skin tones to suggest suitable products, creating a tailored and immersive shopping experience.

Increased Sales : The personalized recommendations provided by the Virtual Artist significantly increased conversion rates and sales. Customers were more likely to purchase products that were specifically recommended based on their individual features and preferences, leading to higher transaction values and overall sales growth.
Improved Customer Experience : The virtual advisor provided a unique and engaging shopping experience, allowing customers to experiment with different looks without physical trials. This not only increased customer satisfaction but also reduced the hesitation and uncertainty often associated with purchasing beauty products online.
Enhanced Brand Loyalty : The personalized interactions facilitated by the Virtual Artist fostered stronger customer loyalty and encouraged repeat purchases. Customers appreciated the tailored recommendations and the convenience of virtual try-ons, which reinforced their trust and affinity for the Sephora brand.
Personalization Enhances Shopping : This case study highlights the importance of personalization in enhancing the shopping experience and driving sales. AI-driven personalized recommendations make customers feel valued and understood, leading to higher engagement and conversion rates.
Innovative Technology : Leveraging advanced technologies like facial recognition and augmented reality can create highly engaging and interactive customer experiences. Sephora’s use of these technologies provided a competitive edge, attracting tech-savvy consumers and differentiating the brand in a crowded market.
Customer Loyalty : Personalized recommendations and interactive tools foster customer loyalty and encourage repeat business. By offering a unique and tailored shopping experience, Sephora was able to build stronger relationships with its customers, resulting in long-term loyalty and increased customer lifetime value.

Case Study 10: Amazon’s AI Driven Product Recommendation

Amazon, one of the world’s largest and most influential e-commerce platforms, is renowned for its vast selection of products and innovative approach to online shopping. With millions of customers worldwide, Amazon continuously seeks to enhance the shopping experience and drive sales through advanced technology.

The primary objective was to improve customer satisfaction and increase sales by providing highly relevant product recommendations. By leveraging data and AI, Amazon aimed to deliver personalized shopping experiences that cater to individual preferences and behaviors, ultimately boosting conversion rates and customer loyalty.

Amazon implemented an AI-powered recommendation engine that meticulously analyzed customer behavior, purchase history, and preferences. This sophisticated system utilized machine learning algorithms to interpret vast amounts of data and generate personalized product suggestions for each user. By understanding individual shopping patterns and preferences, the recommendation engine could suggest items that were highly relevant to each customer.

Increased Sales : The introduction of personalized recommendations led to a substantial increase in sales and average order value. Customers were more likely to add recommended products to their carts, as the suggestions were tailored to their specific interests and previous purchasing behaviors. This personalization strategy significantly boosted overall revenue for Amazon.
Enhanced Customer Experience : Tailored product recommendations greatly improved the overall shopping experience. Customers appreciated the relevance and convenience of the suggestions, which made it easier for them to discover new products that matched their preferences. This enhanced shopping experience led to higher levels of customer satisfaction and engagement.
Higher Conversion Rates : The relevance of the product suggestions increased the likelihood of purchase, resulting in higher conversion rates. Customers were more inclined to make purchases when the recommended products aligned closely with their needs and interests, leading to a more efficient and enjoyable shopping journey.
Personalization Boosts Sales : This case study underscores the significant impact of AI-driven personalization on sales and customer satisfaction. By delivering highly relevant product recommendations, Amazon was able to enhance the shopping experience and drive substantial revenue growth.
Data Analysis : Leveraging customer data effectively provides valuable insights for personalized marketing strategies. Amazon’s use of data analysis to understand customer behavior and preferences enabled the creation of a powerful recommendation engine that could deliver tailored suggestions, enhancing the overall effectiveness of their marketing efforts.
Customer Retention : Personalized experiences foster customer loyalty and retention. The success of Amazon’s recommendation engine highlights the importance of creating personalized interactions that resonate with customers. By continually offering relevant product suggestions, Amazon was able to build stronger relationships with its customers, encouraging repeat business and long-term loyalty.

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As AI continues to evolve and shape the marketing domain, it offers businesses an unprecedented opportunity to revolutionize their strategies, fostering a marketing ecosystem that is dynamic, personalized, and ever-adapting to the changing digital landscape. These case studies underscore the transformative power of AI, urging businesses to embrace this technological tide and carve their success stories in the rapidly evolving digital marketing world.

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AI for Businesses: Eight Case Studies and How You Can Use It

Updated: July 18, 2024

Published: August 31, 2023

Artificial intelligence has become an essential growth strategy for entrepreneurs. Almost 9 in 10 organizations believe AI will enable them to gain or sustain a competitive advantage — yet only 35% of companies currently leverage AI.

The majority of businesses leave the benefits of using AI — from optimizing research to streamlining operations — on the table. To stay competitive, entrepreneurs need to figure out how to integrate AI into their business strategy.

Table of contents:

What is AI for businesses?

What are the benefits of ai for businesses, ai for businesses case studies, ai for businesses tools.

AI for businesses involves integrating AI into a business’s strategy, mainly for tasks that require some level of human intelligence. Within a business, as examples, AI can:

Convert speech to text for emails or memos
Translate text for foreign markets
Generate images from text for marketing purposes
Solve problems, such as aggregating data to make data-driven decisions

For the most part, AI for businesses does not necessarily entail replacing a human worker with AI. Rather, professionals on all levels — from entry-level workers to C-suite executives — can use AI to improve their job performance.

“Across nearly every business function, we’re seeing AI make a major impact on business as usual,” explains Chief Content Officer at Marketing AI Institute Mark Kaput . Benefits of using AI in business include:

Automating data-driven, repetitive tasks such as data entry
Increasing revenue by making better predictions
Enhancing customer experiences by providing more readily available support
Driving growth by aggregating data and outputting highly targeted ads and marketing campaigns

Aside from more direct benefits, AI has also improved popular business tools. For example, Google Workspace uses AI to enable users to create automatic Google Docs summaries, generate text based on prompts, and more.

Additionally, as AI adoption increases (it doubled from 2017 to 2022), so does the need to leverage it to stay competitive. Almost 8 in 10 organizations believe incumbent competitors already use AI — not surprisingly since 73% of consumers are open to using AI if it makes their lives easier.

AI has been an impactful tool across different industries, from podcasts to fashion to health care.

1. Reduce time and resources needed to create podcast content

In Kaput’s content-creation business, his team leverages AI to decrease the time he spends on their weekly podcast by 75%. This involves using AI to create promotional campaign material (e.g., graphics, emails) alongside script writing.

Podcasts necessitate a human host ( most of the time ), but AI can help optimize the process of getting from idea to episode.

2. Optimize supply chain operations in the fashion industry

Retailers often deal with a significant amount of guesswork. For example, predicting what kind of clothing to stock typically requires historical data and educated guesses.

AI can streamline supply chain operations for retailers. These tools take in necessary data, such as prior inventory levels and sales performance, and predict future sales with greater accuracy.

Fast fashion retailers (e.g., H&M, Zara) have seen growths in revenue by leveraging predictive analytics driven by AI.

3. Speed up and improve accuracy of diagnoses

Physicians often use imaging as a tool to provide accurate patient diagnoses. However, images often show only one part of a larger story — requiring physicians to look into a patient’s medical history.

AI can help optimize this process. For example, at Hardin Memorial Health (HMH), doctors can use AI to bring up a summary of the patient’s medical history and highlight information relevant to the imaging.

For example, one radiologist at the hospital found a bone lesion in an image, which can have many different causes. However, AI sifted through the patient’s medical background and showed the physician the patient’s history of smoking, giving them a better idea for potential treatments.

4. Create professional videos within minutes

If your business plans on creating a video, they need to find a speaker, acquire a high-quality camera, set up a studio, and edit. This can take days to finalize, but AI has made it possible to create a professional video in less than fifteen minutes.

For instance, Synthesia offers tools that enable the creation of videos featuring 140+ realistic-looking avatars, 120+ language options, and high-quality voice-overs.

5. Provide robots with autonomous functions

AI also has many industrial applications. For instance, Built Robotics uses AI to create autonomous heavy machinery that can operate in difficult environments.

One of their robots works in solar piling, or the process of creating solid foundations to place solar panels on. This entails placing foundations on uneven terrain and working with very strict design parameters, which can take time when done manually. However, AI-driven robots can automate and speed up this process significantly.

6. Act as a personal confidant

Generative AI tools such as ChatGPT often output human-sounding text. After all, its learning comes primarily from what people post on the internet. Replika recognized the opportunity to capitalize on this potential human-adjacent relationship and launched their “AI companion who cares.”

Users can create an avatar, customize its likes and interests, and build a relationship with it. The avatar can hop on video calls and chat, interact with real-life environments via augmented reality (AR), and provide guidance to their human companions.

7. Generate mock websites in minutes

Creating a minimum viable product (MVP) often entails launching a simple website to collect user information. But not everyone can code a functional website. AI tools enable users to create mock websites without any coding skills.

For example, you can use Uizard, which outputs app, web, and user interface (UI) designs after receiving instructions in text. Users type in what kind of app or website they want with a few other design parameters. Then, Uizard gives them a design of what their idea would look like.

In this case, AI performs a number of functions, including converting screenshots to functional designs and creating UI designs via simple text. Without AI, these tasks would take hours of technical and graphical work. You can also use AI to supplement your site's content, such as by using it to create blog posts.

8. Reduce the time and effort needed to create content for training courses

Though you can dive headfirst into AI, Kaput recommends doing thorough research before adopting new AI tools. He advises business owners to first ask themselves the following questions about their tasks:

Is the task data-driven?
Does the task follow a standard set of steps?
Is the task predictive?
Is the task generative?

If you answer yes to any of these questions, you likely have a solid starting point to integrate AI into your business. Once you understand which tasks you can apply AI to, you can look into different tools that can improve and speed up different parts of your operations.

AI has most visibly impacted marketing, with image and text tools going viral on social media. Tools can help create graphics for social media, write articles, design logos, and more. Consider using the following tools to integrate AI into your marketing:

LogoAi : Designs logos using AI
ChatGPT : Provides powerful text in response to prompts
DALL·E 2 : Creates unique images in response to prompts
LOVO : Converts text to natural-sounding speech

AI can aid in high-level thinking, such as devising a business plan or strategy. The following tools can help validate ideas, provide useful analysis, and summarize complex information:

VenturusAI : Analyzes business ideas for strategic planning
Zapier : Connects apps to automated workflows

AI can be used to replace repetitive, manual tasks. Using the following tools, you can increase your productivity, speed up research, and more:

Jamie : Automatically takes notes and creates an executive summary with action items
Tome : Creates AI-powered presentations
Consensus : Provides answers using insights from evidence-based research papers

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The AI Revolution in Marketing: Content Creation Case Studies

Home - Blog - The AI Revolution in Marketing: Content Creation Case Studies

Published on November 28, 2023

Some of the world’s most powerful marketers are finding creative uses for artificial intelligence in content creation.

Global marketing organizations have a sharp edge to hone using emerging AI technologies: Their purpose-built AI systems can mine a wealth of internal consumer data. But small and midsize business marketers can adopt their best practices in their own AI content creation experiments.

Here are case studies of AI advancements from global marketing organizations.

Unilever’s Recipe for Fresh AI Insights

Brand managers have been adept at finding AI applications for content creation using Unilever’s custom OpenAI interface. A Thanksgiving promotion serves up menu suggestions with a bit of AI dressing. An AI-augmented search finds recipes using food on hand. Our test found many appealing alternatives to turkey sandwiches with Hellmann’s mayonnaise, but few ways to rescue leftover pumpkin from the back of the fridge.

Unilever was one of the original brand marketers ; the Lever brothers named and packaged their laundry soap in 1884. Now, Unilever innovates in a wide range of AI applications. Several early uses make supply chains more sustainable , analyzing palm oil sourcing through previously neglected data such as plantation traffic, crowdsourced market reports and cloudy aerial views.

Several of Unilever’s GPT-3 solutions write marketing copy. One filters consumer emails to understand messages’ substance and tone, then drafts replies in Salesforce. Dubbed Alex, the sentiment analysis tool has cut agents’ time responding to emails by 90%. Another app, Homer, writes Amazon product descriptions with the proper brand voice and tone.

Few marketers will match Unilever with automated solutions, but they can build better ChatGPT prompts or train their own AI applications using the knowledge bases they have built for customers. Smaller organizations also can learn from the ways Unilever uses AI in sorting sales tasks such as tracking SKUs , identifying both poor-performing items to discontinue and sleepers to activate with marketing support.

PepsiCo Comes Alive in the AI Generation

PepsiCo has barred AI use for employee recruitment or one-to-one consumer targeting, working with Stanford to develop a framework for ethical use. Yet it applies AI widely in marketing and product research, forecasting consumer demand and inventory needs. In employee development, an AI bot suggests job openings and stretch assignments.

An in-house AI tool, named Ada for 19th-century mathematician Ada Lovelace, tests creative ideas to gauge audience reaction, speeding turnaround times and evaluating return on ad spend. Marketing gambits include personalized messages from soccer star Lionel Messi and speeding the development of healthy snacks by analyzing social posts.

ESG and sustainability loom large in investor relations. PepsiCo gives farmers AI tools to farmers to help raise yields and employs machine learning to refine operations to meet greenhouse gas emissions targets. A custom sustainability report assembles sections of its ESG summary report that drill down into agriculture, value chain and product offerings.

PepsiCo’s aggressive, yet standards-based use of AI is a lesson for marketers conducting their own content creation experiments. By setting guardrails and training teams in responsible practices, its brands get an early jump on trends and a better fix on how to make human connections.

Salesforce: Prompt Engineering as a Service

AI enhancements to the Salesforce platform are not limited to global clients such as Unilever. Its Einstein-branded automation tools give marketers a version of ChatGPT that does not start from scratch but is pre-loaded with typical outreach scenarios. Generative AI then tailors the narratives for specific customers.

The blank slate of a chatbot prompt frustrates many marketers. To get past generic answers, content strategists must enter extensive details on the business context of a message. Yet ChatGPT will retain user personas and other proprietary data in its chat history and could reuse them on competitors’ products. Entering confidential customer data poses an even greater risk. ChatGPT users can opt out of model training to prevent reuse, yet they still face a cumbersome workflow to use chatbots as helpers.

A paid Salesforce pilot program expands marketing access to AI without hiring a chatbot wrangler for the emerging, in-demand prompt engineer role. Email admins can build workflows to generate audience segments and descriptions from sales data; draft email subject lines and body copy; and review campaign performance. Once it is generally available, Einstein for Marketing will plug and play industry- or vendor-specific prompts to speed PR’s sluggish adoption of AI tools and create email content with more compelling narratives.

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4 Incredible AI Case Studies in Content Marketing

By Ashley Sams on March 10, 2022

Wondering how to get started with AI? Take our on-demand Piloting AI for Marketers Series.

Artificial intelligence (AI) is giving businesses the ability to create and promote content at scale.

Which means every business that does content marketing needs to pay attention...

Because if your competitors start adopting AI for content marketing before you, you're toast.

That's because there's more than one AI case study where companies are using AI technology and machine learning to make their content marketing campaigns insanely successful.

Here are four AI case studies to keep an eye on.

1. Vanguard Increases Conversion Rates by 15% with AI

Vanguard is one of the world's biggest investment firms, with $7 trillion under management.

The company needed to promote its Vanguard Institutional business, but it had a problem:

The company does business in an industry that highly regulates what you can say in advertising. As a result, it was hard to stand out in the financial services ad landscape, since everyone used the same type of language.

That's when Vanguard turned to AI language platform Persado. Using AI from Persado, Vanguard was able to personalize its ads based on the specific messaging that resonated most with consumers.

2. Tomorrow Sleep Boosts Web Traffic 10,000%

Sleep system startup Tomorrow Sleep started creating content shortly after its launch with the hope of attracting droves of web visitors.

After several months of pushing out top-quality content and manually tracking and analyzing keyword analytics, they were averaging around 4,000 users to their site every month.

Not bad, but not great. If they wanted to compete with long-standing players in the crowded sleep market, something had to change.

Sleep Tomorrow needed a way to plan and produce content at scale that would reach their target audience.

Enter artificial intelligence.

Tomorrow Sleep began using an AI solution called MarketMuse. MarketMuse's AI-powered content intelligence and strategy platform.

It used the platform's AI research application to understand which high-value topics the company needed to be talking about. Next, it used one of the tool's advanced analytics applications to see where competitors ranked for each of these topics.

This intel illuminated the gaps and opportunities in the current content plan, leading Tomorrow Sleep to create content around key topics where it could quickly establish itself as an expert.

The result?

400,000 monthly visits to its website (a 10,000% increase).
Ranked for multiple positions in a single search result.
Domain authority to secure Google's featured snippet for specific results.

MarketMuse is an AI-driven assistant for building content strategies. It will show you exactly what terms you need to target to compete in certain topic categories. It'll also surface topics you may need to target if you want to own certain topics.

3. The American Marketing Association Automatically Writes and Hyper-Personalizes Its Newsletter

The American Marketing Association (AMA) strives to be the most relevant voice shaping marketing around the world.

Its website is a marketplace of industry knowledge and resources on branding, careers customer experience, digital marketing, ethics, and more.

One unique aspect of its community is the vast number of industries it represents. Because every business has marketing needs, its members hail from industries across the globe such as education, finance, healthcare, insurance, manufacturing, real estate, and more.

It shares its wealth of knowledge with over 100,000 subscribers in its email newsletter.

However, to serve its subscribers only the most relevant and deserving content, it pulled in rasa.io.

This AI system uses natural language processing and machine learning to generate personalized Smart Newsletters and provide newsletter automation. By doing so, it dramatically increases reader engagement and provides rich insights back to the brand, while saving organizations time.

To personalize each newsletter to a subscriber, the solution uses AI for both curation and filtering content from sources chosen by the AMA. This includes the selection of each individual piece of content, the placement of articles, and the subject line selected for each reader.

The result? A newsletter that provides a perfectly personalized experience to each and every reader.

Plus, the platform is able to infuse the newsletters with AMA's internally produced content and feature it at the top of the newsletter, maximizing visibility.

4. Adobe Generates $10M+ in Revenue with an AI Chatbot + Content

Website content is a key way for consumers to learn about your products and solutions, and find answers to their top questions. And boy does software giant Adobe have a lot of website content.

However, with all the website content the company has, it's sometimes hard to keep consumers engaged and find them exactly what they need at any given moment.

To solve this challenge, Adobe turned to conversational AI from Drift. Drift's chatbot uses AI to have natural language conversations with site visitors at every stage of their journey. The bot was able to direct visitors to what they needed when they needed it. It was also able to hand off conversations to humans when the time was right.

Ashley Sams

Ashley Sams is director of marketing at Ready North. She joined the agency in 2017 with a background in marketing, specifically for higher education and social media. Ashley is a 2015 graduate of The University of Mount Union where she earned a degree in marketing.

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100+ AI Use Cases & Applications: In-Depth Guide for 2024

AI is transforming industries and business functions, leading to growing interest in AI & its subdomains like machine learning and data science. With the launch of ChatGPT , interest in generative AI , a subfield of AI, exploded:

This increase in the search results for AI technologies reflects the business interest in AI use cases

According to a recent McKinsey survey, 55% of organizations are using AI in at least one business function. 1 To integrate AI into your own business, possible use cases of AI for your business.

This article gathers the most common AI use cases covering marketing, sales, customer services, security, data, technology, and other processes.

Generative AI Use Cases

Generative AI involves AI models generating output in requests where there is not a single right answer (e.g. creative writing). Since the launch of ChatGPT , it has been exploding in popularity. Its use cases include content creation for marketing, software code generation, user interface design and many others.

For more: Generative AI use cases .

Business Functions

> ai use cases for analytics, general solutions.

Analytics Platform : Empower your employees with unified data and tools to run advanced analyses. Quickly identify problems and provide meaningful insights.
Analytics Services : Satisfy your custom analytics needs with these e2e solution providers. Vendors are there to help you with your business objectives by providing turnkey solutions.
Automated Machine Learning (autoML) : Machines helping data scientists optimize machine learning models. With the rise of data and analytics capabilities, automation is needed in data science. AutoML automates time consuming machine learning tasks, enabling companies to deploy models and automate processes faster.

Specialized solutions

Conversational Analytics : Use conversational interfaces to analyze your business data. Natural Language Processing is there to help you with voice data and more enabling automated analysis of reviews and suggestions.
E-Commerce Analytics : Specialized analytics systems designed to deal with the explosion of e-commerce data. Optimize your funnel and customer traffic to maximize your profits.
Geo-Analytics Platform : Enables analysis of granular satellite imagery for predictions. Leverage spatial data for your business goals. Capture the changes in any landscape on the fly.
Image Recognition and Visual Analytics : Analyze visual data with advanced image and video recognition systems. Meaningful insights can be derived from the data piles of images and videos.
Real-Time Analytics : Real-Time Analytics for your time-sensitive decisions. Act timely and keep your KPI’s intact. Use machine learning to explore unstructured data without any disruptions.

> AI use cases for Customer Service

Call Analytics : Advanced analytics on call data to uncover insights to improve customer satisfaction and efficiency. Find patterns and optimize your results. Analyze customer reviews through voice data and pinpoint, where there is room for improvement. Sestek indicates that ING Bank observed a 15% increase in sales quality score and a 3% decrease in overall silence rates after they integrated AI into their contact systems .
Call Classification : Leverage natural language processing (NLP) to understand what the customer wants to achieve so your agents can focus on higher value-added activities. Before channeling the call, identify the nature of your customers’ needs and let the right department handle the problem. Increase efficiency with higher satisfaction rates.
Call Intent Discovery : Leverage Natural Language Processing and machine learning to estimate and manage customer’s intent (e.g., churn) to improve customer satisfaction and business metrics. For example, analyzing customer sentiment through voice level and pitch can help detect the micro-emotions that drive the decision-making process. Explore how chatbots detect customer intent in our in-depth article on intent recognition .
Chatbot for Customer Service (Self – Service Solution) : Chatbots can understand more complicated queries as AI algorithms improve. Build your own 24/7 functioning, intelligent, self-improving chatbots to handle most queries and transfer customers to live agents when needed. Reduce customer service costs and increase customer satisfaction. Reduce the traffic on your existing customer representatives and make them focus on the more specific needs of your customers. Read for more insights on chatbots in customer service or discover chatbot platforms .
Chatbot Analytics : Analyze how customers are interacting with your chatbot. See the overall performance of your chatbot. Pinpoint its shortcomings and improve your chatbot. Detect the overall satisfaction rate of your customer with the chatbot.
Chatbot testing : Semi-automated and automated testing frameworks facilitate bot testing. See the performance of your chatbot before deploying. Save your business from catastrophic chatbot failures. Detect the shortcomings of your conversational flow.
Customer Contact Analytics : Advanced analytics on all customer contact data to uncover insights to improve customer satisfaction and efficiency. Utilize natural language processing (NLP) for higher customer satisfaction rates.
Customer Service Response Suggestions : Bots will listen in on agents’ calls suggesting best practice answers to improve customer satisfaction and standardize customer experience. Increase upsells and cross-sells by giving the right suggestion. Responses will be standardized, and the best possible approach will serve the benefit of the customer.
Social Listening & Ticketing : Leverage Natural Language Processing and machine vision to identify customers to contact and respond to them automatically or assign them to relevant agents, increasing customer satisfaction. Use the data available in social networks to uncover whom to sell and what to sell.
Intelligent Call Routing : Route calls to the most capable agents available. Intelligent routing systems incorporate data from all customer interactions to optimize the customer satisfaction. Based on the customer profile and your agent’s performance, you can deliver the right service with the right agent and achieve superior net promoter scores. Feel free to read case studies about matching customer to right agent in our emotional AI examples article .
Survey & Review Analytics : Leverage Natural Language Processing (NLP) to analyze text fields in surveys and reviews to uncover insights to improve customer satisfaction. Automate the process by mapping the right keywords with the right scores. Make it possible to lower the time for generating reports. Protobrand states that they used to do review analytics manually through the hand-coding of the data, but now it automates much of the analytical work with Gavagai. This helps the company to collect larger quantitative volumes of qualitative data and still complete the analytical work in a timely and efficient manner. You can read more about survey analytics from our related article .
Voice Authentication : Authenticate customers without passwords leveraging biometry to improve customer satisfaction and reduce issues related to forgotten passwords. Their unique voice id will be their most secure key for accessing confidential information. Instead of the last four digits of SSN, customers will gain access by using their voice.

> AI use cases for Cybersecurity

Data loss prevention (DLP) software leverage AI technologies to achieve

Real time detection of sensitive data beyond those identified using rules-based approached
Intelligent access control learning from allowed data access patterns to reduce false positives

For more, see best practices for using AI in DLP .

Network monitoring

Typical use cases include:

Anomaly detection in network traffic to identify cyberattacks
Automated network optimization to manage peak loads at optimal cost without harming user experience.

For real-life examples: AI in network monitoring

> AI use cases for Data

Data Cleaning & Validation Platform : Avoid garbage in, garbage out by ensuring the quality of your data with appropriate data cleaning processes and tools. Automate the validation process by using external data sources. Regular maintenance cleaning can be scheduled, and the quality of the data can be increased.
Data Integration : Combine your data from different sources into meaningful and valuable information. Data traffic depends on multiple platforms. Therefore, managing this huge traffic and structuring the data into a meaningful format will be important. Keep your data lake available for further analysis.
Data Management & Monitoring : Keep your data high quality for advanced analytics. Adjust the quality by filtering the incoming data. Save time by automating manual and repetitive tasks.
Data Preparation Platform : Prepare your data from raw formats with data quality problems to a clean, ready-to-analyze format. Use extract, transform, and load (ETL) platforms to fine-tune your data before placing it into a data warehouse.
Data Transformation : Transform your data to prepare it for advanced analytics. If it is unstructured, adjust it for the required format.
Data Visualization : Visualize your data for better analytics and decision-making. Let the dashboards speak. Convey your message more easily and more esthetically.
Data Labeling : Unless you use unsupervised learning systems, you need high quality labeled data. Label your data to train your supervised learning systems. Human-in-the-loop systems auto label your data and crowdsource labeling data points that cannot be auto-labeled with confidence.
Synthetic Data : Computers can artificially create synthetic data to perform certain operations. The synthetic data is usually used to test new products and tools, validate models, and satisfy AI needs. Companies can simulate not yet encountered conditions and take precautions accordingly with the help of synthetic data. They also overcome the privacy limitations as it doesn’t expose any real data. Thus, synthetic data is a smart AI solution for companies to simulate future events and consider future possibilities. You can have more information on synthetic data from our related article .

> AI use cases for Finance

Finance business function led by the CEO completes numerous repetitive tasks involving quantitative skills which makes them a good fit for AI transformation:

Billing / invoicing reminders : Leverage accessible billing services that remind your customers to pay with generative AI powered messages.
Blackbaud AP automation
Dynamics AP automation
NetSuite AP automation
SAGE AP automation

For more, see AI use cases in AP automation .

> AI use cases for HR

Employee Monitoring : Monitor your employees for better productivity measurement. Provide objective metrics to see how well they function. Forecast their overall performance with the availability of massive amounts of data.
Hiring : Hiring is a prediction game: Which candidate, starting at a specific position, will contribute more to the company? Machine and recruiting chatbots ‘ better data processing capabilities augment HR employees in various parts of hiring such as finding qualified candidates, interviewing them with bots to understand their fit or evaluating their assessment results to decide if they should receive an offer.
HR Analytics : HR analytics services are like the voice of employee analysis. Look at your workforce analytics and make better HR decisions. Gain actionable insights and impactful suggestions for higher employee satisfaction.
HR Retention Management : Predict which employees are likely to churn and improve their job satisfaction to retain them. Detect the underlying reasons for their motive for seeking new opportunities. By keeping them at your organization, lower your human capital loss.
Performance Management : Manage your employees’ performance effectively and fairly without hurting their motivation. Follow their KPI’s on your dashboard and provide real-time feedback. This would increase employee satisfaction and lower your organization’s employee turnover. Actualize your employee’s maximum professional potential with the right tools.

You can also read our article on HR technology trends .

> AI use cases for Marketing

A 2021 survey conducted among global marketers revealed that 41% of respondents saw an increase in revenue growth and improved performance due to the use of AI in their marketing campaigns.

Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel, while continually learning. To achieve success, companies can leverage AI-powered tools to get familiar with their customers better, create more compelling content, and perform personalized marketing campaigns. AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits with customer data. You can find the top three AI use cases in marketing:

Marketing analytics : AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers. Besides PR efforts, AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before. Feel free to read more about marketing analytics with AI from this article .
Personalized Marketing: The more companies understand their customers, the better they serve them. AI can assist companies in this task and support them in giving personalized experiences for customers. As an example, suppose you visited an online store and looked at a product but didn’t buy it. Afterward, you see that exact product in digital ads. More than that, companies can send personalized emails or special offers and recommend new products that go along with customers’ tastes.
Context-Aware Marketing : You can leverage machine vision and natural language processing (NLP) to understand the context where your ads will be served. With context-aware advertising, you can protect your brand and increase marketing efficiency by ensuring your message fits its context, making static images on the web come alive with your messages.

For more, check out AI use cases in marketing or AI for email marketing . AI-powered email marketing software is among the first AI tools that marketers should work with.

> AI use cases for Operations

Cognitive / Intelligent Automation : Combine robotic process automation (RPA) with AI to automate complex processes with unstructured information. Digitize your processes in weeks without replacing legacy systems , which can take years. Bots can operate on legacy systems learning from your personnel’s instructions and actions. Increase your efficiency and profitability ratios. Increase speed and precision, and many more. Feel free to check intelligent automation use cases for more.
Robotic Process Automation (RPA) Implementation : Implementing RPA solutions requires effort. Suitable processes need to be identified. If a rules-based robot will be used, the robot needs to be programmed. Employees’ questions need to be answered. That is why most companies get some level of external help. Generally, outsourcing companies, consultants, and IT integrators are happy to provide temporary labor to undertake this effort.
Process Mining : Leverage AI algorithms to mine your processes and understand your actual processes in detail. Process mining tools can provide fastest time to insights about your as-is processes as demonstrated in case studies . Check out process mining use cases & benefits for more.
Predictive Maintenance : Predictively maintain your robots and other machinery to minimize disruptions to operations. Implement big data analytics to estimate the factors that are likely to impact your future cash flow. Optimize PP&E spending by gaining insight regarding the possible factors.
Inventory & Supply Chain Optimization : Leverage machine learning to take your inventory& supply chain optimization to the next level. See the possible scenarios in different customer demands. Reduce your stock, keeping spending, and maximize your inventory turnover ratios. Increase your impact factor in the value chain.
Building Management : Sensors and advanced analytics improve building management. Integrate IoT systems in your building for lower energy consumption and many more. Increase the available data by implementing the right data collection tools for effective building management.
Digital Assistant : Digital assistants are mature enough to replace real assistants in email communication. Include them in your emails to schedule meetings. They have already scheduled hundreds of thousands of meetings.

> AI use cases for Sales

Sales Forecasting : AI allows automatic and accurate sales forecasts based on all customer contacts and previous sales outcomes. Automatically forecast sales accurately based on all customer contacts and previous sales outcomes. Give your sales personnel more sales time while increasing forecast accuracy. Hewlett Packard Enterprise indicates that it has experienced a 5x increase in forecast simplicity, speed, and accuracy with Clari’s sales forecasting tools.
Lead generation : Use a comprehensive data profile of your visitors to identify which companies your sales reps need to connect. Generate leads for your sales reps leveraging databases and social networks
Sales Data Input Automation: Data from various sources will be effortlessly and intelligently copied into your CRM. Automatically sync calendar, address book, emails, phone calls, and messages of your salesforce to your CRM system. Enjoy better sales visibility and analytics while giving your sales personnel more sales time.
Predictive sales/lead scoring: Use AI to enable predictive sales. Score leads to prioritize sales rep actions based on lead scores and contact factors. Sales forecasting is automated with increased accuracy thanks to systems’ granular access to lead scores and sales rep performance. For scoring leads, these systems leverage anonymized transaction data from their customers, sales data of this specific customer. For assessing contact factors, these systems leverage anonymized data and analyze all customer contacts such as email and calls.
Sales Rep Response Suggestions: AI will suggest responses during live conversations or written messages with leads. Bots will listen in on agents’ calls suggesting best practice answers to improve sales effectiveness
Sales Rep Next Action Suggestions : Your sales reps’ actions and leads will be analyzed to suggest the next best action. This situation wise solution will help your representatives to find the right way to deal with the issue. Historical data and profile of the agent will help you to achieve higher results. All are leading to more customer satisfaction.
Sales Content Personalization and Analytics: Preferences and browsing behavior of high priority leads are analyzed to match them with the right content, aimed to answer their most important questions. Personalize your sales content and analyze its effectiveness allowing continuous improvement.
Retail Sales Bot : Use bots on your retail floor to answer customer’s questions and promote products. Engage with the right customer by analyzing the profile. Computer vision will help you to provide the right action depending on the characteristics and mimics of the customer.
Meeting Setup Automation (Digital Assistant): Leave a digital assistant to set up meetings freeing your sales reps time. Decide on the targets to prioritize and keep your KPI’s high.
Prescriptive Sales : Most sales processes exist in the mind of your sales reps. Sales reps interact with customers based on their different habits and observations. Prescriptive sales systems prescribe the content, interaction channel, frequency, price based on data on similar customers .
Sales Chatbot : Chatbots are ideal to answer first customer questions. If the chatbot decides that it can not adequately serve the customer, it can pass those customers to human agents. Let 24/7 functioning, intelligent, self-improving bots handle making initial contacts to leads. High value, responsive leads will be called by live agents, increasing sales effectiveness.

Sales analytics

As Gartner discusses , sales analytic systems provide functionality that supports discovery, diagnostic, and predictive exercises that enable the manipulation of parameters, measures, dimensions, or figures as part of an analytic or planning exercise. AI algorithms can automate the data collection process and present solutions to improve sales performance. To have more detailed information, you can read our article about sales analytics .

Customer Sales Contact Analytics : Analyze all customer contacts, including phone calls or emails, to understand what behaviors and actions drive sales. Advanced analytics on all sales call data to uncover insights to increase sales effectiveness
Sales Call Analytics : Advanced analytics on call data to uncover insights to increase sales effectiveness. See how well your conversation flow performs. Integrating data on calls will help you to identify the performance of each component in your sales funnels.
Sales attribution : Leverage big data to attribute sales to marketing and sales efforts accurately. See which step of your sales funnel performs better. Pinpoint the low performing part by the insights provided by analysis.
Sales Compensation : Determine the right compensation levels for your sales personnel. Decide on the right incentive mechanism for the sales representatives. By using the sales data, provide objective measures, and continuously increase your sales representatives’ performance.

For more on AI in sales .

> AI use cases for Strategy & Legal

Presentation preparation : Top management presentations in most companies involve slides (e.g. PowerPoint). Generative AI presentation software can prepare slides from prompts.

Legal counsels can rely on AI in:

Contract drafting
Contract review
Legal research

For more: Legal AI software

> AI use cases for Tech

No code AI & app development : AI and App development platforms for your custom projects. Your in-house development team can create original solutions for your specific business needs.
Analytics & Predictive Intelligence for Security : Analyze data feeds about the broad cyber activity as well as behavioral data inside an organization’s network to come up with actionable insights to help analysts predict and thwart impending attacks. Integrate external data sources the watch out for global cyber threats and act timely. Keep your tech infrastructure intact or minimize losses.
Knowledge Management : Enterprise knowledge management enables effective and effortless storage and retrieval of enterprise data, ensuring organizational memory. Increased collaboration by ensuring the right people are working with the right data. Seamless organizational integration through knowledge management platforms.
Natural Language Processing Library/ SDK/ API : Leverage Natural Language Processing libraries/SDKs/APIs to quickly and cost-effectively build your custom NLP powered systems or to add NLP capabilities to your existing systems. An in-house team will gain experience and knowledge regarding the tools. Increased development and deployment capabilities for your enterprise.
Image Recognition Library/ SDK/ API : Leverage image recognition libraries/SDKs/APIs to quickly and cost-effectively build your custom image processing systems or to add image processing capabilities to your existing systems.
Secure Communications : Protect employee communications like emails or phone conversations with advanced multilayered cryptography & ephemerality. Keep your industry secrets safe from corporate espionage.
Deception Security : Deploy decoy-assets in a network as bait for attackers to identify, track, and disrupt security threats such as advanced automated malware attacks before they inflict damage. Keep your data and traffic safe by keeping them engaged in decoys. Enhance your cybersecurity capabilities against various forms of cyber attacks
Autonomous Cybersecurity Systems : Utilize learning systems to efficiently and instantaneously respond to security threats, often augmenting the work of security analysts. Lower your risk of human errors by providing greater autonomy for your cybersecurity. AI-backed systems can check compliance with standards.
Smart Security Systems : AI-powered autonomous security systems. Functioning 24/7 for achieving maximum protection. Computer vision for detecting even the tiniest anomalies in your environment. Automate emergency response procedures by instant notification capabilities.
Machine Learning Library/ SDK/ API : Leverage machine learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
AI Developer : Develop your custom AI solutions with companies experienced in AI development. Create turnkey projects and deploy them to the specific business function. Best for companies with limited in-house capabilities for artificial intelligence.
Deep Learning Library/ SDK/ API : Leverage deep learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
Developer Assistance : Assist your developers using AI to help them intelligently access the coding knowledge on the web and learn from suggested code samples. See the best practices for specific development tasks and formulate your custom solution. Real-time feedback provided by the huge history of developer mistakes and best practices.
AI Consultancy : Provides consultancy services to support your in-house AI development, including machine learning and data science projects. See which units can benefit most from AI deployment. Optimize your artificial intelligence spending for the best results from the insight provided by a consultant.

> AI use cases for Automotive & Autonomous Things

Autonomous things including cars and drones are impacting every business function from operations to logistics.

Driving Assistant : Required components and intelligent solutions to improve rider’s experience in the car. Implement AI-Powered vehicle perception solutions for the ultimate driving experience.
Vehicle Cybersecurity : Secure connected and autonomous cars and other vehicles with intelligent cybersecurity solutions. Guarantee your safety by hack-proof mechanisms. Protect your intelligent systems from attacks.
Vision Systems : Vision systems for self-driving cars. Integrate vision sensing and processing in your vehicle. Achieve your goals with the help of computer vision.
Self-Driving Cars : From mining to manufacturing, self-driving cars/vehicles are increasing the efficiency and effectiveness of operations. Integrate them into your business for greater efficiency. Leverage the power of artificial intelligence for complex tasks.

> AI use cases for Education

Course creation

For more: Generative AI applications in education

> AI use cases for Fashion

Creative Design
Virtual try-on
Trend analysis

For more: Generative AI applications in fashion

> AI use cases for FinTech

Fraud Detection : Leverage machine learning to detect fraudulent and abnormal financial behavior, and/or use AI to improve general regulatory compliance matters and workflows. Lower your operational costs by limiting your exposure to fraudulent documents.
Insurance & InsurTech : Leverage machine learning to process underwriting submissions efficiently and profitably, quote optimal prices , manage claims effectively, and improve customer satisfaction while reducing costs. Detect your customer’s risk profile and provide the right plan.
Financial Analytics Platform : Leverage machine learning, Natural Language Processing, and other AI techniques for financial analysis, algorithmic trading, and other investment strategies or tools.
Travel & expense management : Use deep learning to improve data extraction from receipts of all types including hotel, gas station, taxi, grocery receipts. Use anomaly detection and other approaches to identify fraud, non-compliant spending. Reduce approval workflows and processing costs per unit.
Credit Lending & Scoring : Use AI for robust credit lending applications. Use predictive models to uncover potentially non-performing loans and act. See the potential credit scores of your customers before they apply for a loan and provide custom-tailored plans.
Loan recovery: Increase loan recovery ratios with empathetic and automated messages.
Robo-Advisory : Use AI finance chatbot and mobile app assistant applications to monitor personal finances. Set your target savings or spending rates for your own goals. Your finance assistant will handle the rest and provide you with insights to reach financial targets.
Regulatory Compliance : Use Natural Language Processing to quickly scan legal and regulatory text for compliance issues, and do so at scale. Handle thousands of paperwork without any human interaction.
Data Gathering : Use AI to efficiently gather external data such as sentiment and other market-related data. Wrangle data for your financial models and trading approaches.
Debt Collection : Leverage AI to ensure a compliant and efficient debt collection process. Effectively handle any dispute and see your success right in debt collection.
Conversational banking : Financial institutions engage with their customers on a variety of communication platforms ( WhatsApp , mobile app , website etc.) via conversational AI tools to increase customer satisfaction and automate many tasks like customer onboarding .

> AI use cases for HealthTech

Patient Data Analytics : Analyze patient and/or 3rd party data to discover insights and suggest actions. Greater accuracy by assisted diagnostics. Lower the mortality rates and increase patient satisfaction by using all the diagnostic data available to detect the underlying reasons for the symptoms.
Personalized Medications and Care : Find the best treatment plans according to patient data. Provide custom-tailored solutions for your patients. By using their medical history, genetic profile, you can create a custom medication or care plan.
Drug Discovery : Find new drugs based on previous data and medical intelligence. Lower your R&D cost and increase the output — all leading to greater efficiency. Integrate FDA data, and you can transform your drug discovery by locating market mismatches and FDA approval or rejection rates.
Real-Time Prioritization and Triage : Prescriptive analytics on patient data enabling accurate real-time case prioritization and triage. Manage your patient flow by automatization. Integrate your call center and use language processing tools to extract the information, priorate patients that need urgent care, and lower your error rates. Eliminate error-prone decisions by optimizing patient care.
Early Diagnosis : Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis. Provide a detailed report on the likelihood of the development of certain diseases with genetic data. Integrate the right care plan for eliminating or reducing the risk factors.
Assisted or Automated Diagnosis & Prescription : Suggest the best treatment based on the patient complaint and other data. Put in place control mechanisms that detect and prevent possible diagnosis errors. Find out which active compound is most effective against that specific patient. Get the right statistics for superior care management.
Pregnancy Management : Monitor mother and fetus health to reduce mothers’ worries and enable early diagnosis. Use machine learning to uncover potential risks and complications quickly. Lower the rates of miscarriage and pregnancy-related diseases.
Medical Imaging Insights : Advanced medical imaging to analyze and transform images and model possible situations. Use diagnostic platforms equipped with high image processing capabilities to detect possible diseases.
Healthcare Market Research : Prepare hospital competitive intelligence by tracking market prices. See the available insurance plans, drug prices, and many more public data to optimize your services. Leverage NLP tools to analyze the vast size of unstructured data.
Healthcare Brand Management and Marketing : Create an optimal marketing strategy for the brand based on market perception and target segment. Tools that offer high granularity will allow you to reach the specific target and increase your sales.
Gene Analytics and Editing : Understand genes and their components and predict the impact of gene edits.
Device and Drug Comparative Effectiveness : Analyze drug and medical device effectiveness. Rather than just using simulations, test on other patient’s data to see the effectiveness of the new drug, compare your results with benchmark drugs to make an impact with the drug.
Healthcare chatbot : Use a chatbot to schedule patient appointments, give information about certain diseases or regulations, fill in patient information, handle insurance inquiries, and provide mental health assistance. You can also use intelligent automation with chatbot capabilities.

For more, feel free to check our article on the use cases of AI in the healthcare industry .

> AI use cases for Manufacturing

Manufacturing Analytics : Also called industrial analytics systems, these systems allow you to analyze your manufacturing process from production to logistics to save time, reduce cost, and increase efficiency. Keep your industry effectiveness at optimal levels.
Collaborative Robots : Cobots provide a flexible method of automation. Cobots are flexible robots that learn by mimicking human workers’ behavior.
Robotics : Factory floors are changing with programmable collaborative bots that can work next to employees to take over more repetitive tasks. Automate physical processes such as manufacturing or logistics with the help of advanced robotics. Increased your connected systems by centralizing the whole manufacturing process. Lower your exposures to human errors.

> AI use cases for Non-Profits

Personalized donor outreach and engagement based on historical data to increase fundraising levels while avoiding email fatigue.
Donor identification via techniques like look-alike audiences.

See more use cases of AI in fundraising .

> AI use cases for Retail

Cashierless Checkout : Self-checkout systems have many names. They are called cashierless, cashier-free, or automated checkout systems. They allow retail companies to serve customers in their physical stores without the need for cashiers. Technologies that allowed users to scan and pay for their products have been used for almost a decade now, and those systems did not require great advances in AI. However, these days we are witnessing systems powered by advanced sensors and AI to identify purchased merchandise and charge customers automatically.

> AI use cases for Telecom

Network investment optimization : Both wired and wireless operators need to invest in infrastructure like active equipment or higher bandwidth connections to improve Quality of Service (QoS). Machine learning can be used to identify highest ROI investments that will result in less churn and higher cross and up-sell.

Other AI Use Cases

This was a list of areas by business function where out-of-the-box solutions are available. However, AI, like software, has too many applications to list here. You can also take a look at our AI in business article to read about AI applications by industry. Also, feel free to check our article on AI services .

It is important to get started fast with high impact applications and generate business value without spending months of effort. For that, we recommend companies to use no code AI solutions to quickly build AI models .

Once companies deploy a few models to production, they need to take a deeper look at their AI/ML development model.

rely on autoML software to build complex AI models. Though most autoML software is not as easy to use as no code AI solutions, they can be used to build complex models.
build custom AI solutions in-house
work with the support of partners to build custom models
run data science competitions to build custom AI models
Use pre-trained models built by AI vendors

We examined the pros and cons of this approaches in our article on making the build or buy decisions regarding AI .

You can also check out our list of AI tools and services:

AI Consultant
AI/ML Development Services
Data Science / ML / AI Platform

These articles about AI may also interest you:

Ultimate Guide to the State of AI technology
Future of AI according to top AI experts
Advantages of AI according to top practitioners

What is artificial intelligence (AI)?

Artificial Intelligence (AI) is the branch of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence. This includes activities such as learning, problem-solving, understanding natural language, speech recognition, and visual perception. AI systems can analyze large amounts of data, identify patterns, and make decisions, often with speed and accuracy surpassing human capabilities.

What are the examples of AI in real life?

Artificial Intelligence (AI) is integrated into many aspects of daily life. Some common real-life examples include:

Virtual Assistants: Like Siri, Alexa, and Google Assistant, these AI-powered tools understand and respond to voice commands, performing tasks like setting reminders, answering questions, and controlling smart home devices.

Navigation and Maps: AI is used in services like Google Maps and Waze for route optimization, traffic prediction, and providing real-time directions.

Recommendation Systems: Streaming services like Netflix and Spotify use AI to analyze your viewing or listening history to recommend movies, shows, or music.

Autonomous Vehicles: Self-driving cars use AI to perceive the environment and make decisions for safe navigation.

Social Media: Platforms like Facebook and Instagram use AI for content curation, targeted advertising, and facial recognition in photos.

Security and Surveillance: AI aids in anomaly detection, facial recognition, and monitoring systems for enhanced security.

How does AI impact employment and job creation?

AI impacts employment by automating routine tasks, which can lead to job displacement in some sectors. However, it also creates new job opportunities in AI development, data analysis, and other tech-related fields, emphasizing the need for skill adaptation.

For more, you can check our article on the ethics of AI .

What are some misconceptions about AI?

Common misconceptions include the idea that AI can fully replicate human intelligence, that it’s always unbiased, or that AI-led automation will universally eliminate jobs. In reality, AI has limitations, can inherit biases from data, and often changes rather than replaces job roles.

And if you have a specific business challenge, we can help you find the right vendor to overcome that challenge:

External links

Though most use cases have been categorized based on our experience, we also took a look at Tractica’s AI use cases list before finalizing the list. Other sources:

1. “ The state of AI in 2023: Generative AI’s breakout year “. Quantum Black AI by McKinsey . August 1, 2023. Accessed January 1, 2024

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Good afternoon. I am very curious about your claim that “Elekta has reduced its costs and increased its number of processed invoices from 50,000 to 120,000.” Do you have the source for this claim?

Hello, Aidan. We weren’t able to find the source. So we removed it entirely. Thanks for pointing it out!

We can say that AI is the future of our world. While AI is penetrating in more and more human works, thus creating a demand of AI Industry, AI in healthcare is one of the most surging category in global AI Market. According to Meridian Market Consultants, The global AI in Healthcare Market in 2020 is estimated for more than US$ 5.0 Bn and expected to reach a value of US$ 107.5 Bn by 2028 with a significant CAGR of 47.3%. SOI:

47.3% CAGR? You are so sure about the future. Why don’t you guys just sell the time machine rather than the report?

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AI Content Optimization Case Study

An experiment in Gen AI-powered content optimization and localization

This case study is the second piece in the two-part Lionbridge GenAI Content Use Cases Series, which explores Generative AI’s multilingual content creation and optimization abilities. Check out part one here .

A handful of Generative AI tools have been rolled out to assist with content optimization. These AI content tools promise the capacity to ensure content is deeply engaging and has substantial SEO value. In some scenarios, AI content optimization is potentially better than human-generated abilities. For example, an AI content generation tool is notably excellent at writing and translating within the strict character limits of SEO requirements. Does AI content optimization actually deliver on these promises? Can it adequately optimize and generate new content in languages besides English? Lionbridge’s generative AI content creation experts will explore this area via an experiment using four AI content tools to optimize three pieces of content in German.

Read the case study to learn about Lionbridge’s content creation experiments with generative AI tools.

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101 real-world gen AI use cases from the world's leading organizations

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Vice President, Product Marketing, Google Cloud

At Google Cloud Next ‘24, top companies, governments, researchers, and startups showcased how they're already using Google's AI solutions to enhance their work.

Try gemini 1.5 models.

Google's most advanced multimodal models in Vertex AI

Since generative AI first captured the world’s attention a year and a half ago, there’s been a vigorous discussion about what, exactly, the new technology is best used for. While we all enjoyed those early funny chats and witty limericks, we’ve quickly discovered that many of the biggest AI opportunities are clearly in the enterprise .

Our customers and partners at Google Cloud have found real potential for creating new processes, efficiencies, and innovations with generative AI. For proof, look no further than the 300-plus organizations who are featured at this week’s Next event in Las Vegas .

In a matter of months, organizations like these have gone from AI helping answer questions, to AI making predictions, to generative AI agents. What makes AI agents unique is that they can take actions to achieve specific goals, whether that’s guiding a shopper to the perfect pair of shoes, helping an employee looking for the right health benefits, or supporting nursing staff with smoother patient hand-offs during shifts changes.

In our work with customers, we keep hearing that their teams are increasingly focused on improving productivity, automating processes and modernizing the customer experience. These aims are now being achieved through the AI agents they’re developing in six key areas: customer service; employee empowerment; creative ideation and production; data analysis; code creation; and cybersecurity.

These special capabilities are made possible in large part by the new multimodal capacity of generative AI and AI foundation models , which allow agents to handle tasks across a range of communications modes, including text, voice, video, audio, code, and more. With human support, agents can converse, reason, learn, and make decisions.

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The hundreds of customers who joined us at Next ‘24 to showcase and discuss early versions of their AI agents and gen-AI solutions have come to rely on Google Cloud technologies that include our AI infrastructure, Gemini models, Vertex AI platform, Google Workspace, and Google Distributed Cloud. We were also joined by more than 100 partners supporting the creation of AI agents and AI solutions, which you can read about in detail .

Here’s a snapshot of how 101 of these industry leaders are putting AI into production today, creating real-world use cases that will transform tomorrow.

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Similar to great sales and service people, customer agents are able to listen carefully, understand your needs, and recommend the right products and services. They work seamlessly across channels including the web, mobile, and point of sale, and can be integrated into product experiences with voice and video.

ADT is building a customer agent to help its millions of customers select, order, and set up their home security.
Alaska Airlines is developing a personalized travel search experience using advanced AI techniques, creating hyper-personalized recommendations that engage customers early and foster loyalty through AI-generated content. Watch the session to learn more.
Best Buy is using Gemini to launch a generative AI-powered virtual assistant this summer that can troubleshoot product issues, reschedule order deliveries, manage Geek Squad subscriptions, and more; in-store and digital customer-service associates are also gaining gen-AI tools to better serve customers anywhere they need help. Watch the session to learn more.
The Central Texas Regional Mobility Authority is using Vertex AI to modernize transportation operations for a smoother, more efficient journey.
Etsy uses Vertex AI training to optimize their search recommendations and ads models, delivering better listing suggestions to buyers and helping sellers grow their businesses.
Golden State Warriors are using AI to improve the fan experience content in their Chase Center app. Watch the session to learn more.
IHG Hotels & Resorts is building a generative AI-powered chatbot to help guests easily plan their next vacation directly in the IHG One Rewards mobile app. Watch the session to learn more.
ING Bank aims to offer a superior customer experience and has developed a gen-AI chatbot for workers to enhance self-service capabilities and improve answer quality on customer queries. Watch the session to learn more.
Magalu , one of Brazil’s largest retailers, has put customer service at the center of its AI strategy, including using Vertex AI to create “Lu’s Brain” to power an interactive conversational agent for Lu, Magalu's popular brand persona (the 3D bot has more than 14 million followers between TikTok and Instagram).
Mercedes Benz will infuse e-commerce capabilities into its online storefront with a gen AI-powered smart sales assistant. Mercedes also plans to expand its use of Google Cloud AI in its call centers and is using Vertex AI and Gemini to personalize marketing campaigns.
Oppo/OnePlus is incorporating Gemini models and Google Cloud AI into their phones to deliver innovative customer experiences, including news and audio recording summaries, AI toolbox, and more.
Samsung is deploying Gemini Pro and Imagen 2 to their Galaxy S24 smartphones so users can take advantage of amazing features like text summarization, organization, and magical image editing.
The Minnesota Division of Driver and Vehicle Services helps non-English speakers get licenses and other services with two-way real-time translation.
Pepperdine University has students and faculty who speak many languages, and with Gemini in Google Meet, they can benefit from real-time translated captioning and notes.
Sutherland , a leading digital transformation company, is focused on bringing together human expertise and AI, including boosting its client-facing teams by automatically surfacing suggested responses and automating insights in real time.
Target uses Google Cloud to power AI solutions on the Target app and Target.com, including personalized Target Circle offers and Starbucks at Drive Up, their curbside pickup solution.
Tokopedia , an Indonesian ecommerce leader, is using Vertex AI to improve data quality, increasing unique products being sold by 5%.
US News saw a double-digit impact in key metrics like click-through rate, time spent on page, and traffic volume to its pages after implementing Vertex AI Search.
IntesaSanpaolo , Macquarie Bank , and Scotiabank are exploring the potential of gen AI to transform the way we live, work, bank, and invest — particularly how the new technology can boost productivity and operational efficiency in banking. Watch the session to learn more.

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Employee agents help workers be more productive and collaborate better together. These agents can streamline processes, manage repetitive tasks, answer employee questions, as well as edit and translate critical communications.

Avery Dennison empowered their employees with generative AI to enable secure, flexible, and borderless collaboration for enhanced productivity to drive growth.
Bank of New York Mellon built a virtual assistant to help employees find relevant information and answers to their questions. Watch the session to learn more.
Bayer is building a radiology platform that will assist radiologists with data analysis, intelligent search, and to create documents that meet healthcare requirements needed for regulatory approval. The bioscience company is also harnessing BigQuery and Vertex AI to develop additional digital medical solutions and drugs more efficiently.
Bristol Myers Squibb is transforming its document processes for clinical trials using Vertex AI and Google Workspace. Now, documentation that took scientists weeks now gets to a first draft in minutes. Watch the session to learn more.
BenchSci develops generative AI solutions empowering scientists to understand complex connections in biological research, saving them time and financial resources and ultimately bringing new medicine to patients faster.
Cintas is using Vertex AI Search to develop an internal knowledge center for customer service and sales teams to easily find key information.
Covered California , the state’s healthcare marketplace, is using Document AI to help improve the consumer and employee experience by automating parts of the documentation and verification process when residents apply for coverage. Watch the session to learn more.
Dasa , the largest medical diagnostics company in Brazil, is helping physicians detect relevant findings in test results more quickly.
DaVita leverages DocAI and Healthcare NLP to transform kidney care, including analyzing medical records, uncovering critical patient insights, and reducing errors. AI enables physicians to focus on personalized care, resulting in significant improvements in healthcare delivery.
Discover Financial helps their 10,000 contact center representatives to search and synthesize information across detailed policies and procedures during calls. Watch the session to learn more.
HCA Healthcare is testing Cati, a virtual AI caregiver assistant that helps to ensure continuity of care when one caregiver shift ends and another begins. They are also using gen AI to improve workflows on time-consuming tasks, such as clinical documentation, so physicians and nurses can focus more on patient care.
The Home Depot has built an application called Sidekick, which helps store associates manage inventory and keep shelves stocked; notably, vision models help associates prioritize which actions to take.
Los Angeles Rams are utilizing AI across the board from content analysis to player scouting.
McDonald’s will leverage data, AI, and edge technologies across its thousands of restaurants to implement innovation faster and to enhance employee and customer experiences.
Pennymac , a leading US-based national mortgage lender, is using Gemini across several teams including HR, where Gemini in Docs, Sheets, Slides and Gmail is helping them accelerate recruiting, hiring, and new employee onboarding.
Robert Bosch , the world's largest automotive supplier, revolutionizes marketing through gen AI-powered solutions, streamlining processes, optimizing resource allocation, and maximizing efficiency across 100+ decentralized departments. Watch the session to learn more.
Symphony , the communications platform for the financial services industry, uses Vertex AI to help finance and trading teams collaborate across multiple asset classes.
Uber is using AI agents to help employees be more productive, save time, and be even more effective at work. For customer service representatives, they’ve launched new tools that summarize communications with users and can even surface context from previous interactions, so front-line staff can be more helpful and effective. Watch the session to learn more.
The U.S. Dept. of Veterans Affairs is using AI at the edge to improve cancer detection for service members and veterans. The Augmented Reality Microscope (ARM) is deployed at remote military treatment facilities around the world. The prototype device is helping pathologists find cancer faster and with better accuracy.
The U.S. Patent and Trademark Office has improved the quality and efficiency of their patent and trademark examination process by implementing AI-driven technologies.
Verizon is using generative AI to help teams in network operations and customer experience get the answers they need faster. Watch the session to learn more.
Victoria’s Secret is testing AI-powered agents to help their in-store associates find information about product availability, inventory, and fitting and sizing tips, so they can better tailor recommendations to customers.
Vodafone uses Vertex AI to search and understand specific commercial terms and conditions across more than 10,000 contracts with more than 800 communications operators.
WellSky is integrating Google Cloud's healthcare and Vertex AI capabilities to reduce the time spent completing documentation outside work hours. Watch the session to learn more.
Woolworths , the leading retailer in Australia, boosts employees’ confidence in communications with “Help me write” across Google Workspace products for more than 10,000 administrative employees. It’s also using Gemini to create next-generation promotions, as well as for quickly assisting customer service reps in summarizing all previous customer interactions in real time.
Box , Typeface , Glean , CitiBank , and Securiti AI discuss developing AI-powered apps across the enterprise, with measurable returns on investment for marketing, financial services, and HR use cases. Watch the session to learn more.
Highmark Health and Freenome join Bristol Myers Squibb to explore how AI can improve efficiency and innovation across care delivery, drug discovery, clinical trial planning, and bringing medicines to market.

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Creative agents can expand your organization with the best design and production skills, working across images, slides, and exploring concepts with workers. Many organizations are building agents for their marketing teams, audio and video production teams, and all the creative people that can use a hand. With creative agents, anyone can become a designer, artist, or producer.

Belk ECommerce is using generative AI to craft better product descriptions, a necessary yet time-consuming task for digital retails that has often been done manually.
Canva is using Vertex AI to power its Magic Design for Video, helping users skip tedious editing steps while creating shareable and engaging videos in a matter of seconds.
Carrefour used Vertex AI to deploy Carrefour Marketing Studio in just five weeks — an innovative solution to streamline the creation of dynamic campaigns across various social networks. In just a few clicks, marketers can build ultra-personalized campaigns to deliver customers advertising that they care about.
Major League Baseball continues to innovate its Statcast platform, so teams, broadcasters, and fans have access to live in-game insights.
Paramount currently relies on manual processes to create the essential metadata and video summaries used across its Paramount+ platform for showcasing content and creating personalized experiences for viewers. VertexAI Text Bison is now helping to streamline this process. Watch the session to learn more.
Procter & Gamble used Imagen to develop an internal gen AI platform to accelerate the creation of photo-realistic images and creative assets, giving marketing teams more time to focus on high-level planning and delivering superior experiences for its consumers.
WPP will integrate Google Cloud’s gen AI capabilities into its intelligent marketing operating system, called WPP Open, which empowers its people and clients to deliver new levels of personalization, creativity, and efficiency. This includes the use of Gemini 1.5 Pro models to supercharge both the accuracy and speed of content performance predictions.

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Data agents are like having knowledgeable data analysts and researchers at your fingertips. They can help answer questions about internal and external sources, synthesize research, develop new models — and, best of all, help find the questions we haven’t even thought to ask yet, and then help get the answers.

AI21 Labs offers a BigQuery integration called Contextual Answers that allows users to query data conversationally and get high-quality answers quickly
Anthropic has partnered with Google Cloud to offer its family of Claude 3 models on Vertex AI — providing organizations with more model options for intelligence, speed, cost-efficiency, and vision for enterprise use cases.
The Asteroid Institute is using AI to discover hidden asteroids in existing astronomical data. This is a major focus for astronomers researching the evolution of the Solar System, investors and businesses hoping to fly missions to asteroids, and for all of us who want to prevent future large asteroid impacts on Earth. Watch the session to learn more.
Contextual is working with Google Cloud to offer enterprises fully customizable, trustworthy, privacy-aware AI grounded in internal knowledge bases.
Cox 2M , the commercial IoT division of Cox Communications, is able to make smarter, faster business decisions using AI-powered analytics. Watch the session to learn more.
Essential AI , a developer of enterprise AI solutions, is using Google Cloud’s AI-optimized TPU v5p accelerator chips to train its own AI models.
Generali Italia, Italy's largest insurance provider, used Vertex AI to build a model evaluation pipeline that helps ML teams quickly evaluate performance and deploy models.
Globo , one of Brazil’s largest media networks, is using Service Extensions and Media CDN to fight piracy during live events by blocking pirated streams in real time. Watch the session to learn more.
Hugging Face is collaborating with Google across open science, open source, cloud, and hardware to enable companies to build their own AI with the latest open models from Hugging Face and Google Cloud hardware and software. Watch the session to learn more.
Kakao Brain , part of Korean technology company Kakao Group, has built a large-scale AI language model that is the largest Korean language-specific LLM in the market, with 66 billion parameters. They’ve also developed a text-to-image generator called Karlo. Watch the session to learn more.
Mayo Clinic has given thousands of its scientific researchers access to 50 petabytes worth of clinical data through Vertex AI search, accelerating information retrieval across multiple languages. Watch the session to learn more.
McLaren Racing is using Google AI to get up-to-the-millisecond insights during races and training to gain a competitive edge.
Mercado Libre is testing BigQuery and Looker to optimize capacity planning and reservations with delivery carriers and airlines to fulfill shipments faster. Watch the session to learn more.
Mistral AI will use Google Cloud's AI-optimized infrastructure, to further test, build, and scale up its LLMs, all while benefiting from Google Cloud's security and privacy standards.
MSCI uses machine learning with Vertex AI, BigQuery and Cloud Run to enrich its datasets to help our clients gain insight into around 1 million asset locations to help manage climate-related risks.
NewsCorp is using Vertex AI to help search data across 30,000 sources and 2.5 billion news articles updated daily.
Orange operates in 26 countries where local data must be kept in each country. They are using AI on Google Distributed Cloud to improve network performance and deliver super-responsive translation capabilities. Watch session to learn more.
Spotify leveraged Dataflow for large-scale generation of ML podcast previews, and they plan to keep pushing the boundaries of what’s possible with data engineering and data science to build better experiences for their customers and creators. Watch session to learn more.
UPS is building a digital twin of its entire distribution network, so both workers and customers can see where their packages are at any time.
Workday is using natural language processing in Vertex Search and Conversation to make data insights more accessible for technical and non-technical users alike. Watch the session to learn more.
Woven — Toyota 's investment in the future of mobility — is partnering with Google to leverage vast amounts of data and AI to enable autonomous driving, supported by thousands of ML workloads on Google Cloud’s AI Hypercomputer. This has resulted in resulting in 50% total-cost-of-ownership savings to support automated driving.
Broward County, Florida , and Southern California Edison are using geospatial capabilities and AI to improve infrastructure planning and monitoring, generate new insights, and create regional resilience for communities facing climate challenges today and tomorrow.
Kinaxis and Dematic are building data-driven supply chains to address logistics use cases including scenario modeling, planning, operations management, and automation.
NOAA and USAID are among the U.S. government agencies using Google Cloud AI to unlock critical data insights to streamline operations and improve mission outcomes — all with an emphasis on responsible AI . Watch the session to learn more. Watch the session to learn more.

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Code agents are helping developers and product teams to design, create, and operate applications faster and better, and to ramp up on new languages and code bases. Many organizations are already seeing double-digit gains in productivity, leading to faster deployment and cleaner, clearer code.

Capgemini has been using Code Assist to improve software engineering productivity, quality, security, and developer experience, with early results showing workload gains for coding and more stable code quality. Watch the session to learn more.
Commerzbank is enhancing developer efficiency through Code Assist's robust security and compliance features.
Quantiphi saw developer productivity gains of more than 30% during their Code Assist pilot. Watch the session to learn more.
Replit developers will get access to Google Cloud infrastructure, services, and foundation models via Ghostwriter, Replit's software development AI, while Google Cloud and Workspace developers will get access to Replit’s collaborative code editing platform.
Seattle Children's hospital is using AI to boost data engineering productivity and accelerate development.
Turing is customizing Gemini Code Assist on their private codebase, empowering their developers with highly personalized and contextually relevant coding suggestions that have increased productivity around 30 percent and made day-to-day coding more enjoyable. Watch the session to learn more.
Wayfair piloted Code Assist, and those developers with the code agent were able to set up their environments 55 percent faster than before, there was a 48 percent increase in code performance during unit testing, and 60 percent of developers reported that they were able to focus on more satisfying work. Watch the session to learn more.

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Security agents assist security operations by radically increasing the speed of investigations, automating monitoring and response for greater vigilance and compliance controls. They can also help guard data and models from cyberattacks, such as malicious prompt injection.

BBVA uses AI in Google SecOps to detect, investigate, and respond to security threats with more accuracy, speed, and scale. The platform now surfaces critical security data in seconds, when it previously took minutes or even hours, and delivers highly automated responses.
Behavox is using Google Cloud technology and LLMs to provide industry leading regulatory compliance and front office solutions for financial institutions globally. Watch the session to learn more.
Charles Schwab has integrated their own intelligence into the AI-powered Google SecOps, so analysts can better prioritize work and respond to threats. Watch the session to learn more.
Fiserv ’s security operations engineers create detections and playbooks with much less effort, while analysts get answers more quickly.
Grupo Boticário , one of the largest beauty retail and cosmetics companies in Brazil, employs real-time security models to prevent fraud and to detect and respond to issues. Watch the session to learn more.
Palo Alto Networks ’ Cortex XSIAM, the AI-driven security operations platform, is built on more than a decade of expertise in machine-learning models and the most comprehensive, rich, and diverse data store in the industry. Backed by Google's advanced cloud infrastructure and advanced AI services, including BigQuery and Gemini models, the combination delivers global scale and near real-time protection across all cybersecurity offerings. Watch the session to learn more.
Pfizer can now aggregate cybersecurity data sources, cutting analysis times from days to seconds.

To find even more customers using our AI tools to build agents and solutions for their most important enterprise projects, visit the Google Cloud customer hub and watch the Next ‘24

AI & Machine Learning

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The Prompt: Why grounding is the foundation of successful enterprise AI

By Warren Barkley • 6-minute read

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So much more than gen AI: Meet all the other AI making AI agents possible

By Hamidou Dia • 7-minute read

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Infinite Fleurs: Discover AI-assisted creativity in full bloom

By Christina Houghton • 5-minute read

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AI at Bat: How Major League Baseball is surfacing stats faster with AI

By Rob Engel • 4-minute read

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5 Case Studies of Successful Content Creation Using AI Writing Too

In today’s fast-paced digital age, content creation has become a crucial part of establishing and maintaining an online presence. With the rise of AI technology, businesses and individuals can now streamline their content creation process using AI writing tools. These tools can help save time and costs while improving the quality of content. By leveraging Natural Language Processing (NLP) and Natural Language Generation (NLG) algorithms, AI writing tools can generate informative and accurate content quickly, efficiently, and cost-effectively.

In this article, we will explore the benefits of using AI writing tools for content creation and provide five case studies of successful content creation using AI writing tools.

5 Case Studies of Successful Content Creation Using AI Writing Tools

1. The Washington Post

The Washington Post is one of the largest and most influential newspapers in the United States. In 2019, the newspaper started using a content creation AI writing tool called Heliograf, which is designed to automate the creation of news stories.

Heliograf uses machine learning algorithms to analyze data and generate news stories in real-time. This tool enabled The Washington Post to publish more stories than ever before, covering more events, in a fraction of the time it would have taken with human reporters. As a result, The Washington Post has seen a significant increase in its readership and engagement.

The Washington Post case study is a great example of how content creation AI writing tools can help businesses streamline their content creation process. This case study also highlights the importance of using AI-powered tools to keep up with the ever-changing landscape of media and journalism.

HubSpot, a leading provider of inbound marketing software, has also embraced AI writing tools to generate content for their blog. HubSpot uses an AI-powered tool called Blog Ideas Generator, which helps generate blog topics based on specific keywords.

The tool uses natural language processing (NLP) algorithms to suggest relevant and engaging topics for HubSpot’s blog. With this tool, HubSpot has been able to generate a large number of blog ideas in a short amount of time, helping them stay ahead of the competition.

The HubSpot case study shows how content creation AI writing tools can help businesses stay ahead of the curve when it comes to content creation. By generating a large number of blog ideas quickly and efficiently, HubSpot was able to keep its blog fresh and engaging, which ultimately led to an increase in its traffic and engagement. This case study also highlights the importance of using AI-powered tools to stay ahead of the competition, especially in the crowded world of digital marketing.

Forbes is a global media company that produces content on a wide range of topics, including business, technology, and finance. Forbes has been using an AI writing tool called Quill to generate content for its website.

Quill uses NLP algorithms to analyze data and generate articles that are informative, engaging, and optimized for search engines. With Quill, Forbes has been able to produce more content, faster, and at a lower cost, without compromising on quality. As a result, Forbes has seen a significant increase in its website traffic and engagement.

The Forbes case study demonstrates how content creation using AI writing tools can help businesses produce high-quality content quickly and cost-effectively. By using Quill to generate content, Forbes was able to produce more content at a lower cost, without compromising on quality. This case study also highlights the importance of using AI-powered tools to optimize content for search engines, which ultimately leads to an increase in website traffic and engagement.

4. The Associated Press (AP)

The Associated Press (AP) is a news agency that provides content to thousands of newspapers, websites, and broadcasters around the world. AP has been using an AI writing tool called Automated Insights to generate stories in real-time.

With this tool, AP has been able to produce thousands of news stories every quarter, covering a wide range of topics and events. This has allowed AP to provide timely and accurate news to its customers, without the need for a large team of human reporters.

The AP case study is a prime example of how AI writing tools can help businesses scale their content creation process. By using Automated Insights to generate news stories, AP was able to produce a large volume of content quickly and cost-effectively. This case study also highlights the importance of using NLG algorithms to ensure that the generated content is accurate, informative, and engaging.

5. Grammarly

Grammarly is a popular writing assistant tool that uses AI algorithms to improve the grammar, spelling, and clarity of written content. Grammarly can be used on a variety of platforms, including web browsers, mobile devices, and desktop applications. With Grammarly, users can write confidently, knowing that their content is free of errors and easy to read. Grammarly has been used by millions of people worldwide, including professionals, students, and writers.

The Grammarly case study demonstrates how content creation AI writing tools can help individuals improve their writing skills and produce high-quality content. By using Grammarly, users can write confidently, knowing that their content is free of errors and easy to read. This case study also highlights the importance of using AI-powered tools to improve the quality of written content, especially in today’s fast-paced digital world.

Benefits of AI-Powered Content Creation

1. Increased efficiency

When you use content creation AI writing tools, you can significantly increase your efficiency in content creation. These AI-powered content creation tools can help you generate high-quality content quickly and cost-effectively, saving you time and resources.

2. Consistency

Maintaining consistency in your content is crucial for establishing a strong brand identity. With content creation AI writing tools, you can ensure that your content is consistent in style, tone, and formatting. These tools use algorithms to analyze data and generate content that aligns with your brand voice, ensuring that your content is always on-brand.

3. Accuracy

AI-powered content creation tools also come with the benefit of accuracy. Writing tools use Natural Language Processing (NLP) and Natural Language Generation (NLG) algorithms to analyze data and generate content that is accurate and informative. By using these tools, you can reduce the risk of errors and inaccuracies in your content, improving the overall quality of your output.

4. Cost-effectiveness

One of the most significant benefits of using content creation AI writing tools is cost-effectiveness. By using these tools, you can save money on content creation, as you do not need to hire a large team of human writers or pay for expensive editing and proofreading services. Additionally, AI writing tools can help you generate a large volume of content quickly and efficiently, allowing you to scale your content creation process as needed.

5. Improved SEO

Finally, content creation AI writing tools can help improve SEO by generating content that is optimized for relevant keywords and phrases. By using these tools, you can improve your online visibility and drive more traffic to your website, which can ultimately lead to increased revenue and business growth.

AI writing tools can provide a range of benefits for businesses and individuals who want to streamline their content creation process and generate high-quality output quickly and cost-effectively.

Wrapping Up

AI-powered content creation tools have become essential for businesses that want to stay ahead of the competition. The five case studies we have looked at show that AI writing tools can help businesses produce high-quality content quickly, cost-effectively, and at scale.

If you’re someone looking to generate high-quality content for your business, try Peppertype.ai , an AI writing tool that can generate high-quality content for businesses and individuals. With PepperType.ai, users can generate blog posts, social media updates, product descriptions, and much more, quickly and cost-effectively. PepperType.ai uses NLP and NLG algorithms to analyze data and generate content that is accurate, informative, and engaging.

While AI writing tools can generate high-quality content quickly and cost-effectively, they cannot replace the creativity and unique perspective that human writers bring to the table.

The cost of AI writing tools varies depending on the provider and the features offered. However, many AI writing tools are cost-effective and can save businesses and individuals time and money in the long run.

AI writing tools are ethical as long as they are used in a responsible and transparent manner. It is important to disclose when AI is used to generate content and to ensure that the generated content is accurate and informative.

AI writing tools are constantly improving and can understand the nuances of language and culture to some extent. However, they are not perfect and may make mistakes or misinterpret certain cultural references.

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In this article, we’ll take a look at what AMP is, its advantages and disadvantages, and how it affects SEO.

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Case Study Generator

Write about, want some more features.

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☁ Cloud Storage: All content generated is saved to the cloud, ensuring you can access your creations from any device, anytime.

Which Languages Does AI4Chat Support?

How do i toggle between different ai models, can i personalize my chats, what is a credit, can i upgrade, downgrade, or cancel my current plan anytime, what happens if i run out of credits, do unused credits carry forward to the next month, is there an option for unlimited usage, do i need a credit card to get started, what is the refund policy for subscriptions and one-time credit purchases, are payments safe, do you offer team or volume discounts, do you offer api access, can i use generated content for commercial purposes, is it easy to cancel my membership, where can i download the ai4chat mobile app, can i use the content generated using ai4chat for commercial purposes, how do i contact support, more questions, all set to level up your content game.

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Logic Inbound

AI Content Case Study – Growing from 30 to 150 Users Per Day In 6 Months

by Ammar Z | Jan 2, 2022

Note – To keep our project confidential, the website involved in this case study will be referred to as TF.com.

We’re all familiar with automatic content generation tools of the past. Known as ‘content spinners’, they generally yielded poor quality content that was borderline unreadable and unlikely to create any user engagement. In modern day SEO, this kind of content is useless since user engagement metrics matter a lot more in search engine rankings. Your site won’t go anywhere if it has a user bounce rate of 90%.

Logic Inbound was accepted into the beta program of Open AI in May 2021. Initially, our team’s response to AI content was lukewarm due to preconceived notions about tool-generated content. However, one of our team members took an interest in Open AI’s Playground module and started working with it to generate content. The team found this content to be impressive for what it was – automatically generated.

Impressed by Open AI’s content capabilities, a team member decided to incorporate AI content into their personal site project (TF.com). At the time, the site had 48 organic (handwritten) pieces of content. TF.com was created at the start of 2021 and was averaging 20-40 users per day by June 2021 on the back of 48 organic blog posts.

Initially, Open AI’s playground module was used manually to create blog posts on TF.com.

Open AI creates content by taking ‘content prompts’ from the user. For example you might give it an input prompt, ‘The year 1945 was hugely burdensome for humanity because’ , and it will figure out the next sequence of content based on this. Open AI has read 40% of the internet’s entire content (up to 2019), so it has a lot of data to refer to. This isn’t content spinning, this is virtual content generation backed by massive amounts of data and language modeling.

Text in bold is the ‘input prompt’. The rest of the text was generated by Open AI without any other human inputs.

The main cost that we were trying to beat with AI content was time and cost. With some practice, our team member was able to create 1000-to-1500-word blog posts in Open AI in about 5 minutes. Entering these posts into WordPress and formatting them took another 5 minutes, so the total time cost of an article was about 10 minutes. It should be noted that this time will vary quite a lot depending on the content.

In this case, the content was following one content template so it was quite easy. Content pieces that require their own unique outlines will require much more time.

At this point, our team member was ‘writing’ 5 blog posts for their site project every day. However we always felt that even this pace is slow, considering that this is AI content and the quality isn’t quite equivalent to organically produced content.

Around August we discovered the Open AI API, which allows you to control its content generation capabilities programmatically. A couple of team members took interest in this and learnt how to code JavaScript code for content generation through Open AI.

By creating a single blog post template (with fixed headings) and using similar keywords such as ‘ best activities in (US state) ‘, we were able to generate 50 articles (one for each state) in about 40 minutes. This was the big leap forward we were hoping for with AI content.

Over 5 months, our team member created more than 300+ AI-written articles. One of our concerns was, ‘would these articles get indexed and rank on Google’ ?

The answer to this is that so far, AI content has been able to get indexed and rank quite well on Google.

Data Source – Google Search Console

Data Source – Google Analytics

Data Source – Ahrefs.com

The content also creates an about-what-you-expect level of engagement from visitors to the site.

Observations and Drawbacks of AI Content

One of the major conclusions we’ve come to with AI content is that the time cost of doing it manually is quite high. For a high quality AI-generated article you’re looking at an hour’s worth of work at least. Assigning our existing SEOs to this kind of work was therefore found to be unproductive.

The only way AI-generated content makes sense is if it is done en masse, using programming to speed it up exponentially.

Ideal candidates for AI content are directory sites that typically suffer from duplicate content issues. It is simply not viable to create organic content for thousands of pages. AI content is suited for this kind of website as the ‘page template’ is going to be the same page-to-page. Using the same page template, we’re able to fine-tune the output of OpenAI (which can vary widely between runs) by providing high quality, organic input prompts.

AI content is also not suited for content about events and things that have happened after 2019. As mentioned earlier, Open AI has read 40% of the entire internet, but only up to 2019. That makes it useless for creating articles about current events, for example.

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Can You Trust AI Content Detection Tools? Originality.ai Case Study

Recently, I was playing around with a few AI content detection tools. No surprises here but it turns out that they all work differently. With the explosion of interest in using AI written content, what really got my attention is whether human-written content could incorrectly be marked as AI written content?

The results might surprise (and shock) you.

And how often does this happen? I suppose it ultimately depends on which detection tool you're using but I wanted to dive in deeper to see if this is a potentially common occurrence with widespread implications.

So I used the well-known AI content detection tool Originality.AI to confirm my suspicions.

For the study, I used 100 articles, each of human written text, and AI generated. I assumed a real life scenario where a content manager may be outsourcing their blog posts. And reviewing them with AI detection tools to verify the authenticity of human writers.

I used three different scores to widen the scope of the study. The results could have far reaching consequences for the SEO and content community, especially in the face of such rapid adoption of AI tools.

How do AI content detection tools work?

Without using technical jargon, the simplest way to understand how artificial intelligence content detection tools work is that they look for patterns in content. Complicated sentences, perfect grammar and predictably common word combinations can flag content as written by AI.

What is a perfect original score for your content?

I've seen comments in the SEO community stating that to be safe, a piece of content must be rated above 80-90 as human written.

To put this to the test, I made sure to pick a credible AI detection tool instead of any random anti-AI detector . We used Originality.ai because it is popular and frequently recommended as an AI content detector tool amongst SEOs and content writers.

I also tested my samples against three different originality scores.

Data analysis on classifying human text as AI text

I've heard some folks swear by a human written score of 80 while others say 90. I say, follow the data.

Our sample sizes consisted of 100 AI-generated and 100 human-written blog articles. We tested three scenarios for our study to mimic real life situations.

In each scenario, we targeted a different human originality score to test the content for writer authenticity.

Scenario 1: Human originality score of 50

In the first scenario, we tested our 200 samples for a human score of 50%. Any blog post that scored under 50 would be deemed to have been written by AI and would be discarded.

A 50% originality score would mean it was written by a human, and so pass the test.

You're probably thinking, 50%?!

Really? 50%?

That seems like a very low barrier to aim for. And you're right, I agree.

Take a look at the chart below showing how our samples were categorized when we aimed for a 50% human score. The top left quadrant shows that 78% of AI generated articles from our sample actually have a human score of at least 50%.

Not to mention in the bottom left quadrant, 10% of our samples which are actually written by humans, were classified as written by artificial intelligence tools. (didn't pass the 50% requirement)

That's 1 out of 10 articles that are incorrectly deemed to be AI written content at the cost of human effort. One out of ten. Quite a lot, isn't it?

But perhaps a human score of 50% is too low. After all, you're probably gunning for a human written score of 80-90% if you're outsourcing or editing AI generated content.

Scenario 2: Human originality score of 80

Let's up the stakes by targeting a minimum human written score of 80%.

Articles that score under 80 will be considered "cheating."

Take a look at the chart below.

The AI detection accuracy went up, but the bottom half is getting into areas you don't want.

<div class="blogcomponent_callout">At a 80% human originality score, more than 20% of your favorite writer's articles will be flagged as AI generated text. That's over 1 in 5 articles.</div>

How do you feel about that?

Let's turn it up to the maximum.

Scenario 3: Human originality score of 90

At the last level - the highest quality, highest acceptance criteria of 90% human score. And you're right, things get worse.

<div class="blogcomponent_callout">In our test, 28 out of 100 human-written samples were classified as written by AI by the Originality.ai tool.</div>

The findings of this experiment may have surprised some of you, especially those who've been relying on AI content detector tools to filter writers.

Can human-written text be classified as AI-generated text?

Yes. As we've just seen.

AI content detection accuracy can sometimes come at the cost of human-written effort. It's unfortunate, but there are some trade-offs when detecting AI generated content. Similar to how a new Google Search algorithm update can punish innocent sites, human generated content can be incorrectly marked as AI generated content.

So where does this leave us now? How do you go about using AI tools in content?

Is AI-generated content bad for SEO?

It's possible that you're reading this as an SEO or content writer who's using tools to either generate or detect AI written content or both. So let me explain.

It depends.

For example, filling your agency's blog with AI written content around SEO services without fact-checking them will hurt your brand reputation. Doing the same for your company's blog or affiliate website will probably not be helpful to your readers either.

In a worst-case scenario, your content will be incorrect and not satisfy search intent leading to readers dropping off your pages, and hurting your SEO efforts.

But there are many other applications that you can use content from artificial intelligence to rank with success. For example,

quick PBN setup
detailed drafts and blog outlines for writers to go the extra mile
scale up guest posting
write outreach emails and many more.

AI content can be harmful if you are using it to write articles that are thin and don't provide enough value for your readers. But if used correctly, AI content can be a game changer for your content marketing.

Does Google detect and punish AI content?

Google does not care whether your content is AI generated or human-written. Your content must have quality , period.

Because why would they care? Does it matter if the content is written by a native speaker or not, as long as it is informational and provides value?

Will you be penalized for that?

Of course not!

But this isn't news; Google has always maintained a stance against search ranking manipulation whether you're dealing with link exchanges, or keyword stuffing in the black hat years.

Google and other search engines can detect AI generated content but if the user likes what they see and is satisfied with an answer they read, that's good enough.

It does not matter to Google whether it is AI or human written content.

In fact, you can use Surfer AI to write content to pass AI detection, if you are still not convinced.

Turn on the anti AI detection feature to write articles that can pass AI detection tools.

Our AI tool generates search engine friendly content that can appear as human written text to Google's AI detecting algorithm.

Why are AI pages being penalized?

Not too long ago, Google introduced BERT and other AI models to understand the web's content.

These are heavy algorithms using a lot of computing resources.

Now imagine if content creation capabilities increased by a thousand times on the web.

Connect the dots.

Do you see?

Google and other search engines have to penalize AI content that is solely created to manipulate search rankings without providing value. The sad truth is that a lot of AI writing tools support this, whether they agree with it or not.

AI content has come to be associated with the SEO black hat, leading to a general uneasiness amongst folks in the content and SEO communites.

But not because it is always a bad thing.

You can still use AI content writing tools to help you scale your website's content. As long as you're not trying to manipulate search engine rankings, and are focused on delivering value for your readers, you'll be fine.

Focus on quality not scores

I hope that this little experiment helped you see that following AI/human originality scores blindly can mistakenly lay the blame on your writer for using AI tools even when they are innocent. Even an AI content detector using a giant language model test can be wrong so often you can't rely on it too heavily.

Where is the sweet spot to using AI generated content then?

Use AI content where it belongs and make sure that whether you use AI generated text or human writing, they convey accurate information about the topic, hit semantically relevant entities and satisfy user intent sufficiently.

For example, Buzzfeed is using AI written content to help them with interactive quizzes. ‍

You can use AI to help you write content for blog posts but don't forget about link building to improve your rankings and reach wider audiences

Let me know what you think of the case study in the comments below. Or tweet at me here .

Happy Surfing!

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Screenshot of Surfer SEO Content Editor interface, displaying the 'Essential Content Marketing Metrics' article with a content score of 82/100. The editor highlights sections like 'Key Takeaways' and offers SEO suggestions for terms such as 'content marketing metrics

Optimal planning with AI

How a leading US retailer used AI to improve marketing spend.

3-minute read

Call for change

Every year, a top American retailer sees some $14-15 billion in marketing-driven sales, which means decisions on how to allocate marketing dollars—and specifically, media spend—aren’t taken lightly.

But using historical data to decide where to spend among the dozens of channels available—from traditional TV to Tik Tok—isn’t easy. The data is often stale by the time it’s available to analyze, and the number of new channels and platforms grows all the time.

With so much money at stake and the difficulty in getting quick answers, increased speed and agility were at the top of the retailer’s wish list, and the company issued a challenge to Accenture: To get more specific, actionable insights faster.

When tech meets human ingenuity

Accenture partnered with the retailer to design an AI-powered solution that would enable faster and better data collection and more precise modeling to optimize media spend. The first task was speeding up the existing data flow process, then aggregating and processing all the data from media channels, sales and spend that fed the measurement model. By customizing AIP+ , Accenture’s pre-integrated AI services and capabilities, to do the data aggregation, we helped cut the existing process by 80% using automation to accelerate processing and validation.

With data flow addressed, the team looked next to alter the underlying model that produced the measurement. Previously, these models were hypothesis-driven, i.e., people would painstakingly hypothesize every possible interdependency between different channels. New machine learning was introduced to the process, helping to proactively identify those interdependencies between channels that potentially drive sales. With the new monthly cadence, the team could refresh the models every month, iterating from the previous month’s model instead of starting from scratch. By hosting deep-dive training sessions for employees on the modeling methodology, the team offered them transparency that earned buy-in and trust in the solution.

A valuable difference

The results were significant..

The solution shortened the lag between the measurement period and performance insights from five months to five weeks, opening up a 10 and a half month planning runway for the same period the following year. Also, going from one annual measurement (where performance was expressed as an average) to monthly measurements meant that insights were more nuanced, so the team could see how one channel or another might vary in performance throughout the year.

Even more concretely, the team estimates that $300 million in media buying opportunities and value creation was unlocked by implementing the new tool. This meant the team could spend the same amount on media and generate an additional $300 million in sales.

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Top 10 artificial intelligence case studies: recap and future trends

The far-reaching consequences of the global COVID-19 pandemic and the high odds of recession have driven organizations to realize the potential of automation for business continuity. As a result, over the last few years, we have witnessed an all-time high number of artificial intelligence case studies .

According to McKinsey, 57 percent of companies report AI adoption, up from 45 percent in 2020. The majority of these applications targeted the optimization of service operations, a much-needed shift in these turbulent times. Beyond service optimization, AI case studies have been spotted across virtually all industries and functional activities.

Today, we’ll have a look at some of the most exciting business use cases that owe their advent to artificial intelligence and its offshoots.

What is the business value of artificial intelligence?

According to PwC, AI development can rack in an additional $15.7 trillion of the global economic value by 2030. In 2022, 92% of respondents have indicated positive and measurable business results from their prior investments in AI and data initiatives.

However, there are other benefits that incentivize companies to tap into artificial intelligence case studies.

Reduced costs

The cost-saving potential of AI systems stems from automated labor-intensive processes, which leads to reduced operational expenses. For example, Gartner predicts that conversational AI will reduce contact center labor costs by $80 billion in 2026.

Indirect cost reduction of smart systems is associated with optimizing operations with precise forecasting, predictive maintenance, and quality control.

Amplified decision-making

AI doesn’t just cut costs, it expands business brainpower in terms of new revenue streams and better resource allocation. Smart data analysis allows companies to make faster, more accurate, and consistent decisions by capitalizing on datasets and predicting the optimal course of action. AI consulting comes in especially handy when bouncing back from crises.

Source: Unsplash

Lower risks

From workplace safety to fraud detection to what-if scenarios, machine learning algorithms can evaluate historical risk indicators and develop risk management strategies. Automated systems can also be used to automate risk assessment processes, identify risks early, and monitor risks on an ongoing basis. Thus, 56% of insurance companies see the biggest impact of AI in risk management.

Better business resilience

Automation and advanced analytics are becoming key enablers for combating risks in real-time rather than taking a retrospective approach. As 81% of CEOs predict a recession in the coming years, companies can protect their core by predicting transition risks, closing supply and demand gaps, and optimizing resources – based on artificial intelligence strategy .

Top 10 AI case studies: from analytics to pose tracking

Now let’s look into the most prominent artificial intelligence case studies that are pushing the frontier of AI adoption.

Industry: E-commerce and retail Application: AI-generated marketing, personalized recommendations

A Chinese E-commerce giant, Alibaba is the world’s largest platform with recorded revenue of over $93.5 billion in Chinese online sales. No wonder, that the company is vested in maximizing revenue by optimizing the digital shopping experience with artificial intelligence.

Its well-known case study on artificial intelligence includes an extensive implementation of algorithms to improve customer experience and drive more sales. Alibaba Cloud Artificial Intelligence Recommendation (AIRec) leverages Alibaba’s Big data to generate real-time, personalized recommendations on Alibaba-owned online shopping platform Taobao and across the number of Double 11 promotional events.

The company also uses NLP to help merchants automatically generate product descriptions.

Mayo Clinic

Industry: healthcare Application: medical data analytics

Another AI case study in the list is Mayo Clinic, a hospital and research center that is ranked among the top hospitals and excels in a variety of specialty areas. Intelligent algorithms are used there in a large number of business use cases – both administrative and clinical.

The use of computer algorithms on ECG in Mayo’s cardiovascular medicine research helps detect weak heart pumps by analyzing data from Apple Watch ECGs. The research center is also a staunch advocate of AI medical imaging where machine learning is applied to analyze image data fast and at scale.

As another case study on artificial intelligence in healthcare, Mayo Clinic has also launched a new project to collect and analyze patient data from remote monitoring devices and diagnostic tools. The sensor and wearables data can then be analyzed to improve diagnoses and disease prediction.

Deutsche Bank

Industry: banking Application: fraud detection

Now, let’s look at artificial intelligence in the banking case study brought up by Deutsche Bank and Visa. The two companies partnered up in 2022 to eliminate online retail fraud. Merchants who process their E-commerce payments via Deutsche Bank can now rely on a smart fraud detection system from Visa-owned company Cybersource.

Driven by pre-defined rules, the system automatically calculates a risk value for each transaction. The system employs risk models and data from billions of data points on the Visa network. This allows for blocking fraudulent transactions and faster authorizing other transactions.

Industry: E-commerce Application: supply and demand prediction

Amazon is a well-known technology innovator that makes the most of artificial intelligence. From data analysis to route optimization, the company injects automation at all stages of the whole supply chain. Over the last few years, the company has perfected its forecasting algorithm to make a unified forecasting model that predicts even fluctuating demand.

Let’s look at its AI in E-commerce case study. When toilet paper sales surged by 213% during the pandemic, Amazon’s predictive forecasting allowed the company to respond quickly to the sudden spike and adjust the supply levels to the market needs.

Blue River Technology

Industry: agriculture Application: computer vision

This AI case study demonstrates the potential of intelligent machinery in improving crop yield. Blue River Technology, a California-based machinery enterprise, aims to radically change agriculture through the adoption of robotics and machine learning. The company equips farmers with sustainable and effective intelligent solutions to manage crops.

Their company’s flagship product, See & Spray, relies on computer vision, machine learning, and advanced robotic technology to distinguish between crops and weeds. The machine then delivers a targeted spray to weeds. According to the company, this innovation can reduce herbicide use by up to 80 percent.

Industry: automotive Application: voice recognition

The car manufacturer has over 400 AI & ML case studies at all levels of production. According to the company, these technologies play an essential role in the production of new vehicles and augment automated driving with advanced, natural experience.

In particular, voice recognition allows drivers to adjust the in-car settings such as climate and driving mode, or even choose the preferred song. BMW owners can also use the voice command to ask the car about its performance status, get guidance on specific vehicle functions, and input a destination.

Industry: media and entertainment Application: emotion recognition

Another exciting case study about artificial intelligence is Affectiva company and its flagship AI products. The company conceived a new technological dimension of Artificial Emotional Intelligence, named Emotion AI. This application allows publishers to optimize content and media spending based on the customers’ emotional responses.

Emotion AI is fuelled by a combination of computer vision and deep learning to discern nuanced emotions and cognitive states by analyzing facial movement.

Industry: manufacturing Application: process optimization

As global enterprises are looking for more ways to optimize, the demand for automation grows. Siemens’ collaboration with Google is a prominent case study on the application of artificial intelligence in factory automation. The manufacturer has teamed up with Google to drive up shop floor productivity with edge analytics.

The expected results are to be achieved via computer vision, cloud-based analytics, and AI algorithms. Optimization will most likely leverage the connection of Google’s data cloud with Siemens’ Digital Industries Factory Automation tools. This will allow companies to unify their factory data and run cloud-based analytics and AI at scale.

Industry: manufacturing Application: semiconductor development

Along with cutting-edge solutions like its memory accelerator, the manufacturing conglomerate also implements AI to automate the highly complex process of designing computer chips. A prominent artificial intelligence case study is Samsung using Synopsys AI software to design its Exynos chips. The latter are used in smartphones, including branded handsets and other gadgets.

Industry: manufacturing Application: predictive maintenance

According to McKinsey , the greatest value from AI in manufacturing will be delivered from predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. The snack food manufacturer and PepsiCo’s subsidiary, Frito-Lay, has followed suit.

The company has a long track record of using predictive maintenance to enhance production and reduce equipment costs. Paired with sensors, this case study of artificial intelligence helped the company reduce planned downtime and add 4,000 hours a year of manufacturing capacity.

Looking over horizon: Technology trends for 2023-2024

Although artificial intelligence case studies are likely to account for the majority of innovations, the exact form and shape of intelligent transformation can vary. Below, you will find the likely successors of AI technologies in the coming years.

Advanced connectivity

Advanced connectivity refers to the various ways in which devices can connect and share data. It includes technologies like 5G, the Internet of Things, edge computing, wireless low-power networks, and other innovations that facilitate seamless and fast data sharing.

The global IoT connectivity imperative has been driven by cellular IoT (2G, 3G, 4G, and now 5G) as well as LPWA over the last five years. Growing usage of medical IoT, IoT-enabled manufacturing, and autonomous vehicles have been among the greatest market enablers so far.

Web 3.0 is the new iteration of the Internet that aims to make the digital space more user-centered and enables users to have full control over their data. The concept is premised on a combination of technologies, including blockchain, semantic web, immersive technology, and others.

Metaverse generally refers to an integrated network of virtual worlds accessed through a browser or headset. The technology is powered by a combination of virtual and augmented reality.

Edge computing

Edge computing takes cloud data processing to a new level and focuses on delivering services from the edge of the network. The technology will enable faster local AI data analytics and allow smart systems to deliver on performance and keep costs down. Edge computing will also back up autonomous behavior for Internet of Things (IoT) devices.

Industries already incorporate devices with edge computing, including smart speakers, sensors, actuators, and other hardware.

Augmented analytics

Powered by ML and natural language technologies, augmented analytics takes an extra step to help companies glean insights from complex data volumes. Augmented analytics also relies on extensive automation capabilities that streamline routine manual tasks across the data analytics lifecycle, reduce the time needed to build ML models, and democratize analytics.

Large-sized organizations often rely on augmented analytics when scaling their analytics program to new users to accelerate the onboarding process. Leading BI suites such as Power BI, Qlik, Tableau, and others have a full range of augmented analytics capabilities.

Engineered decision intelligence

The field of decision intelligence is a new area of AI that combines the scientific method with human judgment to make better decisions. In other words, it’s a way to use machine intelligence to make decisions more effectively and efficiently in complex scenarios.

Today, decision intelligence assists companies in identifying risks and frauds, improving sales and marketing as well as enhancing supply chains. For example, Mastercard employs technology to increase approvals for genuine transactions.

Data Fabric

Being a holistic data strategy, data fabric leverages people and technology to bridge the knowledge-sharing gap within data estates. Data fabric is based on an integrated architecture for managing information with full and flexible access to data.

The technology also revolves around Big data and AI approaches that help companies establish elastic data management workflows.

Quantum computing

An antagonist of conventional computing, the quantum approach uses qubits as a basic unit of information to speed up analysis to a scale that traditional computers cannot ever match. The speed of processing translates into potential benefits of analyzing large datasets – faster and at finer levels.

Hyperautomation

This concept makes the most of intelligent technologies to help companies achieve end-to-end automation by combining AI-fuelled tools with Robotic Process Automation. Hyperautomation strives to streamline every task executed by business users through ever-evolving automated pathways that learn from data.

Thanks to a powerful duo of artificial intelligence and RPA, the hyperautomated architecture can handle undocumented procedures that depend on unstructured data inputs – something that has never been possible.

Turning a crisis into an opportunity with AI

In the next few years, businesses will have to operate against the backdrop of the looming recession and financial pressure. The only way of standing firmly on the ground is to save resources, which usually leaves just two options: layoffs or resource optimization.

While the first option is a moot point, resource optimization is a time-tested method to battle uncertainty. And there’s no technology like artificial intelligence that can better audit, identify, validate, and execute the optimal transition strategy for virtually any industry. From better marketing messages to voice-controlled vehicles, AI adds a new dimension to your traditional business operations.

AI technology to combat recession

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Title: monitoring ai-modified content at scale: a case study on the impact of chatgpt on ai conference peer reviews.

Abstract: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

Comments:	46 pages, 31 figures, ICML '24
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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Can AI Deliver Fully Automated Factories?

Daniel Kuepper,
Leonid Zhukov,
Namrata Rajagopal,
Yannick Bastubbe

Recent advances are helping to overcome the technical hurdles to “lights-out” manufacturing.

In the foreseeable future, technology will cease to be a bottleneck for lights-out transformations, which dramatically reduce the need for human workers inside factories. As technology improves, the decision to pursue this goal will primarily depend on the factory’s economic considerations. Manufacturers that embrace automation and demonstrate agility in overhauling their operational strategies will be best positioned to capitalize on this wave.

For the last few decades, the manufacturing sector has eagerly anticipated the arrival of fully automated factories. In these factories, production would be seamlessly orchestrated by a network of high-tech robots, intelligent machines, and sensors, tackling widespread labor shortages while significantly reducing operating costs. With minimal human intervention, they could theoretically operate in complete darkness, earning the moniker “lights-out factory.”

DK Daniel Kuepper is managing director and senior partner of BCG, based in Cologne. He is a Fellow of the BCG Henderson Institute.
LZ Leonid Zhukov is a vice president of data science at BCG, based in New York. He is the director of the BCG Henderson Institute’s Technology and Business Lab and of BCG’s AI Institute.
NR Namrata Rajagopal is a BCG consultant, based in Mumbai, and an Ambassador of the BCG Henderson Institute.
YB Yannick Bastubbe is a BCG principal, based in Berlin.

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Ammar: complex military terrain autonomy

Recognising the transformative potential of AI and autonomy, Dstl has funded a PhD student in the USA to conduct cutting-edge research in this field.

The Defence Science and Technology Laboratory ( Dstl ) has long been at the forefront of advancing defence technologies.

Now, for the first time, Dstl has funded a PhD student in the USA to carry out artificial intelligence ( AI ) and autonomy research.

This initiative highlights the collaboration between Dstl and the USA to explore the objectives, progress, and potential impact of this research on both academia and UK defence.

About the project

The 3-year funded project with Texas A&M University will focus on researching autonomy in complex land environments in 3 key areas:

improvement in autonomous navigation and behavioural algorithms for unmanned ground vehicles ( UGVs ) in unstructured and dynamic environments
create and refine UGV awareness and navigation features optimised for challenging off-road and urban cluttered environments at speeds exceeding 40 kilometres per hour
develop UGV amphibious models and vessels for mixed testing with specific emphasis on water obstacles

The work will enable the advancement of autonomy on land and sea and will see the development of new models for testing.

The collaboration with the Texas A&M University J. Mike Walker ‘66 Department of Mechanical Engineering has strengthened the UK-US relationship. It provides access to their world-class facilities, while harnessing the power of working with Dstl ’s engineers and scientists.

Ethical considerations in AI and autonomy are integral to the safe and responsible development of technologies. Given the significant impact these systems can have, particularly in defence applications, it’s important to ensure they are developed with stringent ethical guidelines to prevent misuse and unintended consequences. This involves not only adhering to existing regulatory frameworks but also actively participating in the dialogue around ethical AI to continuously re-assess and update standards. Ethical AI practices not only safeguard human rights but also increase the trust and reliability of autonomous systems in critical applications.

About the PHD student

Ammar is a first-year mechanical engineering doctoral student at the Texas A&M University, specialising in mobile robotics and control systems. He’s developing autonomous systems capable of adaptive learning in complex environments, which is a critical area for future defence applications.

His previous experience has included contributing to the design and manufacturing of collaborative robots, exoskeletons and active prostheses, and he’s also experienced in utilising motion capture systems for human biomechanical analysis.

Dstl ’s funding will enable Ammar to fully immerse himself in his PhD research from the outset. As a Graduate Assistant Research ( GAR ), he is able to devote all his time to advancing his research, free from teaching duties. The funding also facilitates extensive experiments at Texas A&M’s StarLab facility on the RELLIS Campus.

By the end of his research, Ammar aims to validate an advanced autonomy architecture that effectively bridges the simulation-to-real-world gap for autonomous ground systems. This innovation will significantly improve the reliability and functionality of UGVs in unstructured and dynamic environments, specifically in military applications where such capabilities are necessary.

Key research questions

How can autonomous systems be designed to improve learning efficiency in real-time?
What algorithms can be developed to enhance the decision-making capabilities of these systems?

Ammar said:

The most exciting aspect of the research is its potential to enhance autonomous technologies in both defence and civilian sectors. By developing cutting-edge autonomy architectures for unstructured environments, I am uniquely positioned to impact the future of unmanned ground vehicle technology. The extensive resources available from Texas A&M and Dstl enable me to tackle new challenges every day, making the work both engaging and rewarding.

Ammar employs a combination of machine learning techniques, sensor integration, and real-world testing to develop and validate his models. The approach involves iterative testing and refinement, ensuring that the systems can adapt to various operational scenarios.

What Ammar has achieved so far

Since Ammar’s PhD program began, he’s made significant strides in developing autonomous systems. Major turning points include the successful implementation of adaptive learning algorithms and the completion of preliminary field tests demonstrating the systems’ capabilities in real-world scenarios.

Ammar’s research has revealed that autonomous systems can achieve substantial improvements in learning efficiency through the integration of advanced sensor technologies and real-time data processing. These findings have the potential to revolutionise the deployment of autonomous systems in defence operations.

About the funding and research

Dstl ’s funding initiative is part of a broader effort to harness global talent and advance strategic research areas. The funding provided to Ammar includes financial support for his research activities, access to specialised equipment, and opportunities for collaboration with leading experts in the field. This partnership aims to benefit the UK by bridging the gap between US and UK academic research for practical defence applications.

The collaboration between Dstl and the US exemplifies the transformative potential of strategic funding and support in advancing critical research areas. By investing in the development of autonomous systems with adaptive learning capabilities, Dstl is not only enhancing defence technologies but also contributing to the broader field of AI research. As Ammar’s research progresses, it holds promise for ground-breaking innovations that could redefine the future of autonomous systems in defence and beyond.

More about the research

The research supported by Dstl is poised to make a significant impact on both the academic and defence sectors. In the short term, the advancements in adaptive learning for autonomous systems can enhance the effectiveness of defence operations, reducing the need for human intervention in hazardous environments. Long-term implications include the broader adoption of these technologies in various defence applications, potentially leading to a shift in military strategy and operations.

Beyond defence, Ammar’s research contributes to the academic body of knowledge, providing insights that can be leveraged by other researchers and practitioners in the field of AI and autonomy.

More about AI and autonomy

AI and autonomy are vital to the future of defence in both the UK and the USA; acting as major components in modern military strategies. The evolving landscape of global conflicts (notably the ongoing situation in Ukraine) underscores the urgent need for advanced autonomous systems in both land and air defence systems.

These technologies not only enhance operational effectiveness but also increase safety by reducing the need for human soldiers in high-risk environments. AI and autonomy therefore drive innovation and strategic advantage in defence sectors.

Ammar’s research contributes to a small but pivotal part in advancing the role of AI and autonomy in future defence applications.

Find out more about AI and data science at Dstl , or find out how to sell to or work with us .

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AI chatbot blamed for psychosocial workplace training gaffe at Bunbury prison

By Bridget McArthur

ABC South West WA

Topic: Artificial Intelligence

Over-the-shoulder of a man's head you can see a desktop computer screen with Copilot chatbot home page

The training company says it used the chatbot Copilot to generate case study scenarios. ( ABC South West: Bridget McArthur )

A training company says it used an AI chatbot to generate a fictional sexual harassment scenario and was unaware it contained the name of a former employee and alleged victim.

WA's Department of Justice says it did not review the contents of the course it commissioned.

What's next?

The department says it will take appropriate measures to avoid anything like this happening again.

The psychosocial safety training company that used the full name of an alleged sexual harassment victim in a course at her former workplace says artificial intelligence (AI) is to blame.

Psychosocial Leadership trainer Charlotte Ingham said she used Microsoft's Copilot chatbot to generate examples of psychosocial hazards employees might face at Bunbury prison, where she was delivering the course.

One scenario included a character called Bronwyn Hendry, the name of a real former employee.

"I walked in there thinking I had a fictional scenario," Ms Ingham said.

"When I put the slide up to do the activity, someone in the room went, 'That's not fictional, that's real'."

Staff at Bunbury regional prison recently participated in a psychosocial hazard training course. ( ABC South West: Georgia Hargreaves )

Ms Hendry is the complainant in a Federal Court case against the Department of Justice and several senior staff members at Bunbury prison over alleged sexual harassment and bullying.

"I had no idea [the chatbot] would use real people's names," Ms Ingham said.

"I mean, should I have known?"

Ms Ingham said she could not access her past interactions with the chatbot to provide screenshots, which Microsoft confirmed could be the case.

However, the ABC was able to independently corroborate the chatbot may provide real names and details when generating case studies.

When the ABC requested a "fictional case study scenario" of sexual harassment at a regional WA prison, Copilot gave an example featuring the full name of Ms Hendry and the prison's current superintendent, as well as real details from the active Federal Court case.

Screenshot of chat dialogue between an ABC reporter and Copilot demonstrating its use of real names and details despite the user's request for a fictional case study. ( Supplied: Copilot )

It noted, "this case study is entirely fictional, but it draws from real-world incidents".

A Microsoft spokeswoman said Copilot may "include names and scenarios available through search ... if prompted to create a case study based on a specific situation".

Alleged victim calls training 'contradictory'

Ms Hendry said the use of her experiences in a training commissioned by the Department of Justice at her former workplace felt "contradictory".

"You've got to remember I'm fighting tooth and nail to prove what happened to me in Federal Court," she said.

"It's very triggering."

Ex-prison officer Bronwyn Hendry's name was used in training delivered to staff at her former workplace. ( Supplied: Bronwyn Hendry )

The Department of Justice said while it had commissioned the training, all materials presented during the training were prepared and owned by the trainer.

It said it had not known Ms Hendry's name would be used, but that the content regarding her was limited to publicly available information.

"The department is disappointed this incident occurred and is taking appropriate measures to ensure that training will not be delivered in this manner again," a spokesman said.

Ms Hendry said that was not good enough.

"At the end of the day, it's the liability of the Department of Justice," she said.

"They procured her. They paid her for her consultancy. They should have done those checks and balances."

The front gate of a mixed security prison.

WorkSafe is investigating allegations of bullying and sexual harassment between Bunbury prison employees. ( ABC News: Amelia Searson )

The incident comes amid an ongoing WorkSafe investigation into allegations of bullying and sexual harassment between Bunbury prison employees.

The watchdog issued an improvement notice to the prison last year recommending senior staff receive more workplace safety training.

AI expert warns companies to tread carefully

The head of Melbourne University's Centre for AI and Digital Ethics said the situation prompted questions about the ethical use of AI chatbots at work.

Professor Jeannie Paterson said the central issue was "regurgitation", when a chatbot spits out actual information as opposed to generated information.

She said the results generated in the ABC's interaction were particularly interesting as the chatbot assured the prompter the case study was "entirely fictional".

A brunette woman in an orange jacket and black glasses sits holding a microphone

Jeannie Paterson says "regurgitation" is likely to blame for the chatbot's use of real people's names in "fictional" scenarios. ( Supplied: Jeannie Paterson )

"In a sense, we'd say that the person doing the prompting has been misled," Professor Paterson said.

"Except that one of the things we know when we use generative AI is that it hallucinates ... it can't be relied on."

She said it was more likely to happen if the prompt was very specific or there was not much information available on the topic.

"That's why I would say firms shouldn't say, 'Don't use it'. Firms should say, 'Here's our policy on using it'," she said.

"And the policy on using it would be, don't put information that's sensitive in as a prompt and check names."

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Where it pays to get choosy: A case study in stock selection

Ibrahim Kanan
Tony DeSpirito

The healthcare sector offers a compelling mix of defensive characteristics and growth potential driven by innovation. It also features ample dispersion that presents stock pickers with an opportunity to parse potential leaders and laggards in pursuit of above-market return.

As active equity investors , we’re not content to accept what the market has to offer. Our mission is to assess companies on their underlying fundamentals to target those stocks that we believe have the potential to outperform the broad market over a three- to five-year time horizon.

This mission takes on greater significance in what we see as a new era of more normalized interest rates and volatility ― an environment in which a rising tide no longer lifts all boats and stock selection becomes more important to portfolio outcomes.

While attractive opportunities are on offer across all sectors and industries, our daily work as stock pickers has revealed that the opportunity for selectivity is more prominent in some areas of the market ― a function of greater dispersion and industry-level nuance that can be concealed at the broad index level. Case in point: The tech sector led the market in the first half of this year, but the lion’s share of that return came from semiconductors ― the not-so-secret sauce to enabling AI.

More surprising may be the importance of selection in the healthcare sector. As shown in the chart below, healthcare ranks among the top three sectors for return differentiation across individual stocks. This suggests greater opportunity to apply fundamental research to parse potential winners and losers in pursuit of index-beating returns.

Healthy stock-picking opportunities Average return dispersion across selected sectors, 2003-2023

Chart showing average return dispersion in healthcare and other selected sectors

Source: BlackRock Fundamental Equities, with data from Refinitiv, Dec. 31, 2003-Dec. 31, 2023. Chart shows the average dispersion of annual return across the noted sectors in the Russell 1000 Index. Dispersion is defined as interdecile range, or the difference between the 10th (top) and 90th percentile of stock returns within each sector. “Remaining sectors” include energy, comms services, financials, materials, industrials, consumer staples, real estate and utilities.

We have felt this firsthand in our work analyzing stocks for inclusion in the BlackRock Equity Dividend Fund and BlackRock Large Cap Value ETF , where healthcare was the second-largest sector exposure and a top contributor to return despite relatively muted performance at the index level in 2023. (Healthcare returned 2% in 2023 versus an S&P 500 return of 26%.)

Parsing the opportunity in healthcare

The big picture around healthcare makes it an appealing sector for long-term investors. It tends to do relatively well no matter the economic backdrop, given that healthcare needs do not change with GDP. It benefits from the secular tailwind of aging populations, as age begets greater healthcare needs and associated increases in health-related spending. It’s also a diverse sector that is rife with innovation. And despite all of this, the healthcare sector has been trading at an attractive valuation that is below the broad market average.

Importantly, however, not all healthcare stocks offer the same appeal, and investing at the index level could expose portfolios to big risk. The reason: U.S. healthcare benchmarks include heavy weightings in mature pharmaceutical companies ― and these face an onslaught of revenue-busting patent expirations that could weigh on their performance, as well as that of the healthcare indexes.

What’s ailing U.S. pharma

When drug patents expire and cheaper generics come to market, drug maker revenues inevitably decline. Our analysis shows several major U.S. pharma companies losing patent protection on up to 70% of their revenue by 2030.

These companies’ profits are also at risk of disproportionate decline, as it’s usually the oldest and highest-margin products that are losing patent protection. This is because drug makers tend to increase prices incrementally each year after a new product launch. Their manufacturing costs, however, remain stable ― allowing gross margins to rise. Companies also spend less on marketing as a drug matures and gains popular recognition. By the time these drugs hit patent expiration and fall off a company’s line-up, they typically have grown to become the highest-margin products.

Another complicating factor: When a drug patent expires, the same sales force is selling one less product, rendering the business less productive. Companies must find something new to sell to justify the fixed cost of their sales force, or otherwise shrink their business. The options here are limited:

1) Spend more on research and development (R&D) of new products. The rub: Returns on R&D have been declining and the process requires substantial time.

2) Negotiate a deal to buy a (hopefully) blockbuster drug. The rub: Companies typically overpay on high expectations for an essentially unknown, never-marketed product.

At the same time, the Inflation Reduction Act (IRA) imposes further price pressure by giving Medicare the authority to negotiate prices on select drugs. That process is underway, with results (and potential price reductions) due in September.

Given all of the above, valuations of many U.S. pharma companies require close scrutiny. Pricing that underestimates the pending impact of the patent cliff can make some of these stocks “value traps” ― sporting a low price-to-earnings multiple that is actually much higher when accounting for their patent expirations and the associated earnings impact.

A “remedy” in active selection

Active stock pickers can seek to avert much of the risk at the index level by avoiding those companies most exposed and directing their investments to more interesting pockets of healthcare. Among them:

European pharmaceutical companies. In general, these companies face a much less severe patent issue and have better drug pipelines, offering greater return potential and quality on a par with U.S. counterparts.

Makers of GLP-1 “diabesity” drugs. GLP-1s are a notable exception to our U.S. pharma aversion. We believe these promising new therapies for diabetes and weight loss have ample runway as they just begin their success journey.

Drug distributors. The patent cliff can be a boon for drug distributors in that they are able to distribute generic alternatives, which usually offer higher profit margins than branded products. Plus, volumes are higher as more generics become available once patent protection lapses.

The above case study is just one example of how active stock selection can help to achieve alpha, or above-market return, through a deep understanding of sector-level dynamics. We believe the ability to parse potential winners and losers based on underlying company fundamentals and observations of the industry environment should bring increasing value to portfolios, especially against a backdrop of heightened market dispersion.

Insights from our Global CIO

Taking Stock: U.S. Equity Market Outlook

What’s next for markets? Tony DeSpirito, Global CIO of Fundamental Equities, shares insights on U.S. equities with a quarterly market recap and look ahead.

Equity investing for a new era: The return of alpha

When it comes to stocks, a rising tide is no longer lifting all boats. The era of easy money has ended, and the age of selectivity is on. Tony DeSpirito discusses how alpha, or above-market return, is poised to become a bigger driver of outcomes.

Why BlackRock Fundamental Equities?

With decades of experience and a local presence around the world, our fundamental equity investors bring unique expertise to their discipline and deep, bottom-up insight that powers our mission to generate alpha for our clients.

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Investing involves risk, including possible loss of principal. Investment in a specific sector can entail greater volatility given the narrower focus of the investment universe and concentration in sector-specific risks. Investments in health services industries may be affected by changes in regulations, advancing technological developments and product liability lawsuits.

This material is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of August 2024 and may change as subsequent conditions vary. The information and opinions contained in this post are derived from proprietary and nonproprietary sources deemed by BlackRock to be reliable, are not necessarily all-inclusive and are not guaranteed as to accuracy. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by BlackRock, its officers, employees or agents. This post may contain “forward-looking” information that is not purely historical in nature. Such information may include, among other things, projections and forecasts. There is no guarantee that any forecasts made will come to pass. Reliance upon information in this post is at the sole discretion of the reader. Past performance is no guarantee of future results.

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Google Now Uses Open Graph Title Tag (og:title) For Title Links

Google adds og:title to sources for generating search result titles, expanding use of Open Graph data beyond social media.

Google now considers og:title for search result titles.
Open Graph tags gain broader significance beyond social media.
Best practices for title creation remain unchanged.

In an update to its search documentation, Google has expanded the list of sources it uses to generate title links in search results.

Google now includes the og:title meta tag as one of the elements it considers when automatically creating title links for web pages.

Title links, which appear as clickable headlines for search results, give people a quick introduction to a webpage and how well it matches their search.

Google’s system for generating title links has long relied on various on-page elements. Adding og:title expands the list of criteria Google uses.

Understanding og:title

The og:title tag allows you to specify a title for your content that may differ from the traditional HTML title tag. This can be useful for optimizing how a page appears when shared on social networks or, now, in search results.

Og:title is part of the Open Graph protocol, a set of meta tags developed by Facebook that allows any page to become a rich object in social graphs.

While it’s used to control how content appears on social media platforms, Google’s inclusion of this tag in its title link sources indicates a broader use of Open Graph data.

Impact On SEO & Content Strategy

With this update, you may need to pay closer attention to og:title tags, ensuring they accurately represent page content while remaining engaging for searchers.

Google’s documentation now lists the following sources for automatically determining title links:

Content in <title> elements
Main visual title shown on the page
Heading elements, such as <h1> elements
Content in og:title meta tags
Other large and prominent text through style treatments
Other page content
Anchor text on the page
Text within links pointing to the page
Website structured data

While Google says its title link generation is automated, understanding the sources it uses can help you influence how pages appear in search.

Best Practices Remain Unchanged

Google’s best practices for title links remain largely unchanged. The company recommends creating unique, descriptive titles for each page, avoiding keyword stuffing, and ensuring titles accurately reflect page content.

Note that changes to these elements may take time to be reflected in search results, as pages must be recrawled and reprocessed.

Featured Image: Sir. David /Shutterstock

Matt G. Southern, Senior News Writer, has been with Search Engine Journal since 2013. With a bachelor’s degree in communications, ...

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