organizational learning essay conclusion

Workforce Inter-Personnel Diversity pp 125–130 Cite as

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Improving human productivity and their career development opportunities will continue to be a goal of employees and organizations into perpetuity. There is no end in sight for the number of ideas and variety of ways to explore the implementation of ideas to meet human needs throughout the world. Some organizations are exploring the use of technology to facilitate productivity but technology is still guided by the thoughts of the humans who program it. The knowledge that humans have is bounded only by their ability to turn what they think about into applicable tools to use in their worldly endeavors or to sell to their peers. Organizations focus on practical application of theoretical knowledge. If the knowledge cannot be applied, it is useless to employees as they endeavor to provide maximum quality for their organizations. Having knowledge alone has never been enough for an organization to thrive especially in a capitalist society where time is money. The workforce must be adept at figuring out ways to apply all knowledge and training to organizational processes. I would suggest that an extension of the difference between education and training is that organizational learning requires a combination of education, training, knowledge, and skills to succeed. To continue to leverage workforce inter-personnel diversity into every aspect of the organization, organizations should choose appropriate training and development delivery methods using technology and data analysis to support their efforts.

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• How to conclude an essay | Interactive example

How to Conclude an Essay | Interactive Example

Published on January 24, 2019 by Shona McCombes . Revised on July 23, 2023.

The conclusion is the final paragraph of your essay . A strong conclusion aims to:

• Tie together the essay’s main points
• Show why your argument matters
• Leave the reader with a strong impression

Your conclusion should give a sense of closure and completion to your argument, but also show what new questions or possibilities it has opened up.

This conclusion is taken from our annotated essay example , which discusses the history of the Braille system. Hover over each part to see why it’s effective.

Braille paved the way for dramatic cultural changes in the way blind people were treated and the opportunities available to them. Louis Braille’s innovation was to reimagine existing reading systems from a blind perspective, and the success of this invention required sighted teachers to adapt to their students’ reality instead of the other way around. In this sense, Braille helped drive broader social changes in the status of blindness. New accessibility tools provide practical advantages to those who need them, but they can also change the perspectives and attitudes of those who do not.

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Table of contents

Step 1: return to your thesis, step 2: review your main points, step 3: show why it matters, what shouldn’t go in the conclusion, more examples of essay conclusions, other interesting articles, frequently asked questions about writing an essay conclusion.

To begin your conclusion, signal that the essay is coming to an end by returning to your overall argument.

Don’t just repeat your thesis statement —instead, try to rephrase your argument in a way that shows how it has been developed since the introduction.

Prevent plagiarism. Run a free check.

Next, remind the reader of the main points that you used to support your argument.

Avoid simply summarizing each paragraph or repeating each point in order; try to bring your points together in a way that makes the connections between them clear. The conclusion is your final chance to show how all the paragraphs of your essay add up to a coherent whole.

To wrap up your conclusion, zoom out to a broader view of the topic and consider the implications of your argument. For example:

• Does it contribute a new understanding of your topic?
• Does it raise new questions for future study?
• Does it lead to practical suggestions or predictions?
• Can it be applied to different contexts?
• Can it be connected to a broader debate or theme?

Whatever your essay is about, the conclusion should aim to emphasize the significance of your argument, whether that’s within your academic subject or in the wider world.

Try to end with a strong, decisive sentence, leaving the reader with a lingering sense of interest in your topic.

The easiest way to improve your conclusion is to eliminate these common mistakes.

Don’t include new evidence

Any evidence or analysis that is essential to supporting your thesis statement should appear in the main body of the essay.

The conclusion might include minor pieces of new information—for example, a sentence or two discussing broader implications, or a quotation that nicely summarizes your central point. But it shouldn’t introduce any major new sources or ideas that need further explanation to understand.

Don’t use “concluding phrases”

Avoid using obvious stock phrases to tell the reader what you’re doing:

• “In conclusion…”
• “To sum up…”

These phrases aren’t forbidden, but they can make your writing sound weak. By returning to your main argument, it will quickly become clear that you are concluding the essay—you shouldn’t have to spell it out.

Don’t undermine your argument

Avoid using apologetic phrases that sound uncertain or confused:

• “This is just one approach among many.”
• “There are good arguments on both sides of this issue.”
• “There is no clear answer to this problem.”

Even if your essay has explored different points of view, your own position should be clear. There may be many possible approaches to the topic, but you want to leave the reader convinced that yours is the best one!

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• Literary analysis

This conclusion is taken from an argumentative essay about the internet’s impact on education. It acknowledges the opposing arguments while taking a clear, decisive position.

The internet has had a major positive impact on the world of education; occasional pitfalls aside, its value is evident in numerous applications. The future of teaching lies in the possibilities the internet opens up for communication, research, and interactivity. As the popularity of distance learning shows, students value the flexibility and accessibility offered by digital education, and educators should fully embrace these advantages. The internet’s dangers, real and imaginary, have been documented exhaustively by skeptics, but the internet is here to stay; it is time to focus seriously on its potential for good.

This conclusion is taken from a short expository essay that explains the invention of the printing press and its effects on European society. It focuses on giving a clear, concise overview of what was covered in the essay.

The invention of the printing press was important not only in terms of its immediate cultural and economic effects, but also in terms of its major impact on politics and religion across Europe. In the century following the invention of the printing press, the relatively stationary intellectual atmosphere of the Middle Ages gave way to the social upheavals of the Reformation and the Renaissance. A single technological innovation had contributed to the total reshaping of the continent.

This conclusion is taken from a literary analysis essay about Mary Shelley’s Frankenstein . It summarizes what the essay’s analysis achieved and emphasizes its originality.

By tracing the depiction of Frankenstein through the novel’s three volumes, I have demonstrated how the narrative structure shifts our perception of the character. While the Frankenstein of the first volume is depicted as having innocent intentions, the second and third volumes—first in the creature’s accusatory voice, and then in his own voice—increasingly undermine him, causing him to appear alternately ridiculous and vindictive. Far from the one-dimensional villain he is often taken to be, the character of Frankenstein is compelling because of the dynamic narrative frame in which he is placed. In this frame, Frankenstein’s narrative self-presentation responds to the images of him we see from others’ perspectives. This conclusion sheds new light on the novel, foregrounding Shelley’s unique layering of narrative perspectives and its importance for the depiction of character.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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Your essay’s conclusion should contain:

• A rephrased version of your overall thesis
• A brief review of the key points you made in the main body
• An indication of why your argument matters

The conclusion may also reflect on the broader implications of your argument, showing how your ideas could applied to other contexts or debates.

For a stronger conclusion paragraph, avoid including:

• Important evidence or analysis that wasn’t mentioned in the main body
• Generic concluding phrases (e.g. “In conclusion…”)
• Weak statements that undermine your argument (e.g. “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

The conclusion paragraph of an essay is usually shorter than the introduction . As a rule, it shouldn’t take up more than 10–15% of the text.

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Essays on Organizational Learning Processes and Outcomes in Healthcare

In my dissertation, consisting of three chapters, I investigate how various mechanisms jointly affect organizational learning in the healthcare sector. The first chapter provides a review of the literature on organizational learning, focusing on how different factors impact four distinct organizational learning processes: search, knowledge creation, retention, and transfer. By categorizing past findings, I identify how the same factor may promote or hinder different organizational learning processes and encourage a more detailed examination of how multiple mechanisms interact to affect organizational learning. 

In the second chapter, I examine the relationship between individuals' repeated failures and learning. Through a theoretical framework and empirical analysis of cardiothoracic surgeons in U.S. hospitals, I demonstrate an inverted U-shaped relationship between the number of failures and learning. I find that individuals give up learning after a certain number of failures because their motivation to learn decreases despite increasing learning opportunities. This research aims to reconcile inconsistent findings from the literature on individual failure learning and provides insights into the non-monotonic relationship between failure experiences and individual learning. 

In the third chapter, I explore the impact of contractor employment on organizational learning in terms of adopting an industry's new best practices. Using archival data on heart disease patients in U.S. hospitals, where physicians worked as contractors or full-time employees, I find evidence that organizational learning peaks at a moderate proportion of contractors. I theorize that the integration of diverse knowledge held by contractors and firm-specific knowledge held by full-time employees is most effective at this point. This research contributes to the understanding of how a firm's human capital resource composition affects knowledge transfer and organizational learning—an important topic in light of the rising population of contingent workers. 

Overall, this dissertation contributes to the literature on organizational learning and the microfoundations of organizational capabilities. 

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Module 6: The Writing Process

Essay organization, learning objectives.

• Examine the basic organization of traditional essays

What are some ways that you can organize your essays in college. One standard structure for expository essays is to offer the main idea or assertion early in the essay, and then offer categories of support.

One way to think about this standard structure is to compare it to a courtroom argument in a television drama. The lawyer asserts, “My client is not guilty.” Then the lawyer provides different reasons for lack of guilt: no physical evidence placing the client at the crime scene, client had no motive for the crime, and more.

In writing terms, the assertion is the  thesis sentence , and the different reasons are the  topic sentences . Consider this following example:

• Topic Sentence (reason) #1:  Workers need to learn how to deal with change.
• Topic Sentence (reason) #2:  Because of dealing with such a rapidly changing work environment, 21st-century workers need to learn how to learn.
• Topic Sentence (reason) #3:  Most of all, in order to negotiate rapid change and learning, workers in the 21st century need good communication skills.

As you can see, the supporting ideas in an essay develop out of the main assertion or argument in the thesis sentence.

The structural organization of an essay will vary, depending on the type of writing task you’ve been assigned, but they generally follow this basic structure: The thesis and the topic sentences are all concerned with workers and what they need for the workforce.

Introduction

The introduction provides the reader with context about your topic. You may be familiar with the cliché about how first impressions are important. This is true in writing as well, and you can think of your introduction as that first impression. The goal is to engage the readers, so they want to read on. Sometimes this involves giving an example, telling a story or narrative, asking a question, or building up the situation. The introduction should almost always include the thesis statement.

Body Paragraphs

The body of the essay is separated into paragraphs. Each paragraph usually covers a single claim or argues a single point, expanding on what was introduced in the thesis statement. For example, according to the National Institute of Mental Health, the two main causes of schizophrenia are genetic and environmental. Thus, if you were writing about the causes of schizophrenia, then you would have a body paragraph on genetic causes of schizophrenia and a body paragraph on the environmental causes.

A body paragraph usually includes the following:

• Topic sentence that identifies the topic for the paragraph
• Several sentences that describe and support the topic sentence

Figure 1 . College instructors require more than just “the end” at the close of a paper. Take the time to revisit your thesis statement, bringing all of your claims and evidences together in your conclusion.

• Remember that information from outside sources should be placed in the middle of the paragraph and not at the beginning or the end of the paragraph so that you have time to introduce and explain the outside content
• Quotation marks placed around any information taken verbatim (word for word) from the source
• Summary sentence(s) that draws conclusions from the evidence
• Transitions or bridge sentences between paragraphs.

If you began with a story, draw final conclusions from that story in your conclusion. If you began with a question, refer back to the question and be sure to provide the answer.

A concluding paragraph:

• summarizes final conclusions from the key points
• provides a brief comment on the evidence provided in the paper
• ties in the introduction

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A review of this chapter’s major conclusions, include:

• Classical definitions outline leadership as the social influence of the relationship between two or more persons who depend on each other to attain certain mutual goals. Management is the process of planning, organizing, directing, and controlling the activities of employees.
• A learning organization is any establishment that fosters growth through learning, and continues to expand that growth in the future. A learning organization is either organic or mechanistic.
• Self-awareness is cultivated through Maslow’s understanding of self-actualization. Leaders and managers need to have expectations of themselves and others in order to be self-aware.
• The view of leadership and management is based on experiences throughout life.
• Many metaphors can be used to describe organizational complexity. It depends on how the individual sees the organization in relation to her or his world view.

Moving through this journey of leadership and management in learning organizations, it is important to understand how past knowledge of leadership and management in your lifetime affects perceptions. These examples can act as a reference to your own understanding of leadership and management, and how your previous encounters relate to understandings within a learning organization.

Leadership and Management in Learning Organizations Copyright © by Clayton Smith; Carson Babich; and Mark Lubrick is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Many types of writing follow some version of the basic shape described above. This shape is most obvious in the form of the traditional five-paragraph essay: a model for college writing in which the writer argues his or her viewpoint (thesis) on a topic and uses three reasons or subtopics to support that position. In the five-paragraph model, as illustrated below, the introductory paragraph mentions the three main points or subtopics, and each body paragraph begins with a topic sentence dealing with one of those main points.

SAMPLE ESSAY USING THE FIVE-PARAGRAPH MODEL

Remember, this is a very simplistic model. It presents a basic idea of essay organization and may certainly be helpful in learning to structure an argument, but it should not be followed religiously as an ideal form.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

• “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
• “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
• “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

• data scientist:
• prompt engineering
• in-context learning
• bias detection
• pattern identification
• reinforcement learning from human feedback
• hyperparameter/large language model fine-tuning; transfer learning
• data engineer:
• data wrangling and data warehousing
• data pipeline construction
• multimodal processing
• vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

• Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
• Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
• Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

• Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
• Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
• Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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Learning Organization to Attain Competitive Capacity Essay

Introduction, learning organization, systems thinking, personal mastery, continuous knowledge acquisition, team learning, knowledge sharing, shared vision, mental model, list of references.

In line with survival motive in the era of globalization, various organizations are examining their management systems to attain competitive capacity. This is essential towards remaining relevant in their area of operations.

Since its inception at the beginning of 1990, Learning Organization (LO) has received overwhelming attention as managers realize that they need to build an organization that can remain relevant in the ever changing business environment.

Garvin, Edmondson, and Gino (2008) argue that it is vital that companies should become learning organizations due the tougher competition, and shifting customer preferences. The authors argue that in learning organizations, employees continually acquire, create and transfer knowledge.

Consequently, employees will help their organizations acclimatize to the volatile business environment faster than their competitors. The learning organization is applicable to business organization in general and to service sector such as academic, hospital organization, and hotels (Senge, 1994, p. 48).

Thus, this reflective treatise attempts to explicitly review aspects of learning in Microsoft Company with the view of identifying learning models applied by the company. Besides, the paper relates Garvin’s diagnostic tools to those in use at Microsoft and HP.

Moreover, the treatise identifies the concepts of organization learning, continuous knowledge acquisition, system thinking, knowledge sharing, team learning, personal mystery, and mental models in organizational learning. In conclusion, the paper reflects on success of the applied institution learning module existing in Microsoft Company.

According to Garvin, Edmondson, and Gino, experience is the basis from which learning organizations justify progress in terms of productivity and self motivation. Garvin, in article published in the Harvard Business Review, illustrates a learning association as “an organization skilled at creating, acquiring, and transferring knowledge and modifying its behavior to reflect new knowledge insight and insights” (Garvin, 1993, p.1).

A learning organization do engage in active process of learning through promotion of learning, facilitation, and rewarding collective learning since it does not rely on ad hoc process with the hope that learning will occur through chance.

Garvin, Edmondson, and Gino in an article, “ Is Yours a Learning Organization ?” published in Harvard Business Review (March 2008), replicates on organization edifice wedges as “supportive learning environment, concrete learning processes, and practices leadership that reinforces learning” (Garvin, Edmondson, and Gino, 2008, p.14).

Garvin et al., (2008) argue that by using the diagnostic tools, managers can assess areas of the organization that require urgent improvement. Thus, this help in moving the company closer to an ideal learning organization from the usual. The managers play a significant role in setting up the learning environment for their employees.

As result, creating an effective learning environment will allow employees to draw upon resources, thus making sense out of things and construct consequential solution to business challenges (Fielden 2001).

As mentioned earlier, in the changing business environment of the twenty-first century, only sustainable competitive advantage and will permit businesses to learn faster than its rivals. Firms that implement learning organization training programs have shown improvements in their productivity by an average of about 17 per cent.

Same as in HP, Microsoft Regional Sales Corporation organizational learning is highly valued. The organizational learning here covers individual employees, business manager, business group, and sector-specific needs as they apply to company’s values, mission, and business main concerns.

Garvin (1993) suggests that a learning organization should posses the capacity for problem solving, learning from past experience, experimentation, learning from best practices of other successful organizations, and swift and efficient transfer of knowledge.

On the other hand, Senge (1990) argues that an organization can develop its learning organization attribute through system thinking, shared vision, mental modeling, personal mastery, and team learning. Our organization applies Senge’s five basic disciplines or component technologies of learning, that is, systems thinking, personal mastery, building of shared vision, mental models, and team building.

The learning processes at Microsoft Regional Sales Corporation are basically based on Senge’s principles of organization learning-‘disciplines’ while those at Hewlett Packard are inclined towards Garvin’s model.

These values have been embedded in the company’s tradition and practiced by our employees over the years. For example, at Microsoft Regional Sales Corporation, it is responsible for the developing and driving growth strategies between management and employees.

According to Senge (1990), systems thinking theory is the cornerstone of the learning organization model. Coherently, this system factors in discipline as a concept of theory and body. Senge argues that managers tend to focus on sectors rather than the whole.

The key perception in this line of thought is that recognition of organization systems facilitates informed and appropriate decision science by management unit and other employees. At Microsoft, the organization is viewed in this perspective enabling the training of about 92,000 employees via the four employee education organizations.

The four parts of management that facilitate learning organization at Microsoft are the engineering department responsible for product development, sales and customer care department, leadership and management that is responsible for professional development of employees, and the marketing department.

Senge (1990) argues that individual learning do not warranty organizational learning. However, organization learning cannot function independently without individual learning. He further notes that organization can also learn from people who learn.

Therefore, through personal mastery is the discipline that allows people to acquire knowledge patiently in a continuous manner, while allowing such individuals to see reality objectively. For example, at Microsoft, there is an enterprise of a large governance team called the Learning Council that is responsible for streamlining learning organization within the company’s four learning organizations.

The council’s main responsibility is to make certain that there are links in learning and development initiatives undertaken by the organization in pursuit of its business priorities.

In addition, the other key area that the learning council is involved in is provision of strategic planning methodologies and directions for a range of learning communities at the Corporation. Moreover, the learning council ensures that systems, processes, and learning infrastructure are available to enable organizational learning to take place.

Porth et al. (1999) asserts that continuous knowledge acquisition is crucial for adapting to and surviving in the competitive business environment. Thus, it becomes an important feature of learning organization. Continuous knowledge can be through activities such as research and development, market research, and competitor analysis.

Microsoft Regional Sale Cooperation is involved in team learning through formal and informal activities via its various departments (Senge 1990) just as the HP Company. For example, the engineering excellence is one of the central learning organs tasked with ensuring that learning takes place across all the organizations’ engineering departments.

In addition, it ensures the delivery of guidance, curriculum, and expert advice to all junior engineers. Similarly, ensuring continuity in the acquisition of knowledge is the Sales and Marketing group that are involved in carrying out frequent market research for new products to ensure availability of market.

The group has helped the organization to continue to grow through growth in the market share. Some commentators argue that team learning can also be achieved through direct experience and learning from other individuals’ experience what is referred to as benchmarking.

Fielden (2001) argues that knowledge is only useful when it is made freely available. Consequently, sharing information with employees is significant for organization learning process. Knowledge sharing is the distribution of knowledge or what has been previously leant (Dixon, 1999).

He further notes that knowledge sharing begins with making information available to everybody in the organization. Knowledge sharing can be expanded through shared vision, communication, knowledge, values, and information by developing the cultural norms of sincerity, open-mindedness, trust, and honesty (Porth et al. 1999).

Knowledge sharing is a key success of knowledge management strategy. In addition, to achieve knowledge sharing between employees, leadership commitment is a channel to speed up and reinforce knowledge sharing (Nymark, 1999).

After knowledge acquisition, there is need to store the information in organizational repository system so that other members can easily access and use it in their work as practiced in HP training department. In Microsoft, the corporate learning and development department is tasked with ensuring that knowledge is developed and managed in an open environment where it is accessible to all.

A company that has objectives to attain the status of a learning organization should employ people that are commonly referred to as high caliber workers.

The distinguishing feature of such workers is that they have high levels of education and posses the capacity to acquire new knowledge rapidly and constantly while adjusting to new conditions within a common vision (Senge 1990).

Secondly, they have the capability to work under no supervision and are able to set their own goals and objectives, while observing the attainment and results of these goals.

A learning organization comprises of workers who have excellent interpersonal skills. They have the capacity to solve problems through creative evaluation of different possible outcomes, and by using their own ideas to find solutions to the rising problems (Barrow and Loughlin, 1992, p. 5).

Microsoft knows that strong performance is a prerequisite for future career development and thus employ people with global mindsets, customer focus, result oriented, and deep business understanding (Hewlett-Packard 2010).

Same as in HP, the acquired talents are put into rigorous development program which relies on feedback from development center sessions to evaluate the learning process. However, the evaluation process at Microsoft relies on feedback from managers thus might not be flawless owing to personal attitudes that the managers have towards their employees at the corporation.

The above interactive prospects facilitate elimination of unnecessary bureaucracy and hierarchical levels and go a long way in reducing overhead costs, while efficiently increasing productivity. A study done in the Dutch proved that there is higher productivity in companies that have flat and organizational structure which promotes integration among employees as is exhibited in Microsoft and HP companies (Nymark, 1999).

As a result, acquisition of knowledge is encouraged and proper management encouraged that functions on inclusivity of interactive process.

Nymark (1999) asserts that it is impossible to force workers to be spontaneous, the same way it is impossible to force people to act more autonomous, to be imaginative, or to take more responsibility. Therefore, the organization managers have no direct leeway to force workers to act spontaneously, or take initiatives.

In conclusion, Microsoft is a learning organization just like HP. Though they share more or less the same learning models, there are several quantifiable dissimilarities in balancing one model to another. Microsoft is more aligned to Senge’s organizational learning features which include personal mastery, shared vision, mental model, team learning, and systems thinking.

On the other hand, HP tends to apply more of Garvin’s model. To improve the organization, the management unit should endeavor to institute inclusive organization that motivate employees to internalize essential learning attributes which makes learning in the organization interesting and relevant.

In addition, this model should be inclusive and easy to follow in order to ensure that the system is non discriminative. Relevancy is critical in designing this alternative due to the fact that knowledge and learning environment is not constant.

Factually, relevancy is vital in ensuring that the goal or target of continouse knowledge sharing is achieved within the shortest time possible. It is important to establish a flexible module that can be modified to fit into the need of parties involved.

Finally, paradigms must be constantly evaluated, reevaluated, analyzed, and clarified to ensure that they are as precise as possible. Learning in an organization is vital in goal attainment. Generally, learning process is continuous and relies on structured systems that control information dispensation.

Barrow, M & Loughlin, M 1992, “Towards a learning organization,” Industrial and Commercial Training , vol. 24, no. 1, pp. 3-7.

Dixon, N M 1999, “The Changing Face of Knowledge,” The Learning Organization, vol. 6, no. 5, pp. 212-216.

Fielden, T 2001, “A Knowledge Management State of Mind. Info World , 23: 47.

Garvin, D 1993, “Building a Learning organization,” Harvard Business Review , vol. 2 no 4, pp. 78-91.

Garvin, D A, Edmondson, A C and Gino, F 2008, “Is yours learning organization,” Harvard Business Review , vol. 86 no. 3, pp. 109-116.

Hewlett-Packard 2010, Organization learning: Hewlett Packard (HP), viewed on https://www.slideshare.net/maronoman/hp-challenges-organizational-learning

Nymark, S R 1999, “A study of flexibility and renewal in Danish companies,” Human Resource Development International , vol. 2, no. 1, pp. 59-66.

Porth, S et al. 1999, “Spiritual Themes of the ‘Learning Organization,’” Journal of Organization Change Management , vol. 12 no. 3, pp. 211-220.

Senge, Peter M et al, 1994, The Fifth Discipline Fieldbook: Strategies and Tools for Building a Learning Organization , Currency Doubleday: New York.

Senge, Peter M., 1990, The Fifth Discipline: The Art and Practice of the Learning Organization , Currency Doubleday: New York.

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