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Thesis Services Limited

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Thesis Services Limited Exchange building St. johns street, Chichester, PO19 1UP

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THESIS SERVICES LIMITED is an active Private Limited Company, registered at Companies House under the number 11541569. THESIS SERVICES LIMITED was incorporated on 29 August 2018 with a registered office address based in Chichester. THESIS SERVICES LIMITED has been operating for 5 year(s) and 10 month(s). According to the latest confirmation statement submitted on 29 November 2023, there is currently 3 active director(s) and activities related to the SIC Code 82990 - Other business support service activities n.e.c..

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Last submitted on 29 November 2023 (7 months ago)

Next confirmation dated 29 November 2024

Due by 13 December 2024 (6 months remaining)

No change of name occurred since incorporation

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Last accounts submitted for period 30 April 2023 (14 months ago)

Accounts type was Full

Next accounts dated 30 April 2024

Due by 31 January 2025

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Private Limited Company with Share Capital

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Irwin mitchell secretaries limited appointed on: 29 /08 /2018 -, david william tyerman appointed on: 29 /08 /2018 - investment manager british, mr stephen ronald mugford appointed on: 29 /08 /2018 - chartered accountant british, total liabilities, cash in bank, debt ratio (%), total assets, update shareholders & psc.

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THESIS SERVICES LIMITED

Company number 11541569

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• What Is a Thesis? | Ultimate Guide & Examples

What Is a Thesis? | Ultimate Guide & Examples

Published on September 14, 2022 by Tegan George . Revised on April 16, 2024.

A thesis is a type of research paper based on your original research. It is usually submitted as the final step of a master’s program or a capstone to a bachelor’s degree.

Writing a thesis can be a daunting experience. Other than a dissertation , it is one of the longest pieces of writing students typically complete. It relies on your ability to conduct research from start to finish: choosing a relevant topic , crafting a proposal , designing your research , collecting data , developing a robust analysis, drawing strong conclusions , and writing concisely .

Thesis template

You can also download our full thesis template in the format of your choice below. Our template includes a ready-made table of contents , as well as guidance for what each chapter should include. It’s easy to make it your own, and can help you get started.

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Table of contents

Thesis vs. thesis statement, how to structure a thesis, acknowledgements or preface, list of figures and tables, list of abbreviations, introduction, literature review, methodology, reference list, proofreading and editing, defending your thesis, other interesting articles, frequently asked questions about theses.

You may have heard the word thesis as a standalone term or as a component of academic writing called a thesis statement . Keep in mind that these are two very different things.

• A thesis statement is a very common component of an essay, particularly in the humanities. It usually comprises 1 or 2 sentences in the introduction of your essay , and should clearly and concisely summarize the central points of your academic essay .
• A thesis is a long-form piece of academic writing, often taking more than a full semester to complete. It is generally a degree requirement for Master’s programs, and is also sometimes required to complete a bachelor’s degree in liberal arts colleges.
• In the US, a dissertation is generally written as a final step toward obtaining a PhD.
• In other countries (particularly the UK), a dissertation is generally written at the bachelor’s or master’s level.

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The final structure of your thesis depends on a variety of components, such as:

• Your discipline
• Your theoretical approach

Humanities theses are often structured more like a longer-form essay . Just like in an essay, you build an argument to support a central thesis.

In both hard and social sciences, theses typically include an introduction , literature review , methodology section ,  results section , discussion section , and conclusion section . These are each presented in their own dedicated section or chapter. In some cases, you might want to add an appendix .

Thesis examples

We’ve compiled a short list of thesis examples to help you get started.

• Example thesis #1:   “Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807” by Suchait Kahlon.
• Example thesis #2: “’A Starving Man Helping Another Starving Man’: UNRRA, India, and the Genesis of Global Relief, 1943-1947″ by Julian Saint Reiman.

The very first page of your thesis contains all necessary identifying information, including:

• Your full title
• Your full name
• Your department
• Your institution and degree program
• Your submission date.

Sometimes the title page also includes your student ID, the name of your supervisor, or the university’s logo. Check out your university’s guidelines if you’re not sure.

Read more about title pages

The acknowledgements section is usually optional. Its main point is to allow you to thank everyone who helped you in your thesis journey, such as supervisors, friends, or family. You can also choose to write a preface , but it’s typically one or the other, not both.

Read more about acknowledgements Read more about prefaces

An abstract is a short summary of your thesis. Usually a maximum of 300 words long, it’s should include brief descriptions of your research objectives , methods, results, and conclusions. Though it may seem short, it introduces your work to your audience, serving as a first impression of your thesis.

Read more about abstracts

A table of contents lists all of your sections, plus their corresponding page numbers and subheadings if you have them. This helps your reader seamlessly navigate your document.

Your table of contents should include all the major parts of your thesis. In particular, don’t forget the the appendices. If you used heading styles, it’s easy to generate an automatic table Microsoft Word.

Read more about tables of contents

While not mandatory, if you used a lot of tables and/or figures, it’s nice to include a list of them to help guide your reader. It’s also easy to generate one of these in Word: just use the “Insert Caption” feature.

Read more about lists of figures and tables

If you have used a lot of industry- or field-specific abbreviations in your thesis, you should include them in an alphabetized list of abbreviations . This way, your readers can easily look up any meanings they aren’t familiar with.

Read more about lists of abbreviations

Relatedly, if you find yourself using a lot of very specialized or field-specific terms that may not be familiar to your reader, consider including a glossary . Alphabetize the terms you want to include with a brief definition.

Read more about glossaries

An introduction sets up the topic, purpose, and relevance of your thesis, as well as expectations for your reader. This should:

• Ground your research topic , sharing any background information your reader may need
• Define the scope of your work
• Introduce any existing research on your topic, situating your work within a broader problem or debate
• State your research question(s)
• Outline (briefly) how the remainder of your work will proceed

In other words, your introduction should clearly and concisely show your reader the “what, why, and how” of your research.

Read more about introductions

A literature review helps you gain a robust understanding of any extant academic work on your topic, encompassing:

• Selecting relevant sources
• Determining the credibility of your sources
• Critically evaluating each of your sources
• Drawing connections between sources, including any themes, patterns, conflicts, or gaps

A literature review is not merely a summary of existing work. Rather, your literature review should ultimately lead to a clear justification for your own research, perhaps via:

• Addressing a gap in the literature
• Building on existing knowledge to draw new conclusions
• Exploring a new theoretical or methodological approach
• Introducing a new solution to an unresolved problem
• Definitively advocating for one side of a theoretical debate

Read more about literature reviews

Theoretical framework

Your literature review can often form the basis for your theoretical framework, but these are not the same thing. A theoretical framework defines and analyzes the concepts and theories that your research hinges on.

Read more about theoretical frameworks

Your methodology chapter shows your reader how you conducted your research. It should be written clearly and methodically, easily allowing your reader to critically assess the credibility of your argument. Furthermore, your methods section should convince your reader that your method was the best way to answer your research question.

A methodology section should generally include:

• Your overall approach ( quantitative vs. qualitative )
• Your research methods (e.g., a longitudinal study )
• Your data collection methods (e.g., interviews or a controlled experiment
• Any tools or materials you used (e.g., computer software)
• The data analysis methods you chose (e.g., statistical analysis , discourse analysis )
• A strong, but not defensive justification of your methods

Read more about methodology sections

Your results section should highlight what your methodology discovered. These two sections work in tandem, but shouldn’t repeat each other. While your results section can include hypotheses or themes, don’t include any speculation or new arguments here.

Your results section should:

• State each (relevant) result with any (relevant) descriptive statistics (e.g., mean , standard deviation ) and inferential statistics (e.g., test statistics , p values )
• Explain how each result relates to the research question
• Determine whether the hypothesis was supported

Additional data (like raw numbers or interview transcripts ) can be included as an appendix . You can include tables and figures, but only if they help the reader better understand your results.

Read more about results sections

Your discussion section is where you can interpret your results in detail. Did they meet your expectations? How well do they fit within the framework that you built? You can refer back to any relevant source material to situate your results within your field, but leave most of that analysis in your literature review.

For any unexpected results, offer explanations or alternative interpretations of your data.

Read more about discussion sections

Your thesis conclusion should concisely answer your main research question. It should leave your reader with an ultra-clear understanding of your central argument, and emphasize what your research specifically has contributed to your field.

Why does your research matter? What recommendations for future research do you have? Lastly, wrap up your work with any concluding remarks.

Read more about conclusions

In order to avoid plagiarism , don’t forget to include a full reference list at the end of your thesis, citing the sources that you used. Choose one citation style and follow it consistently throughout your thesis, taking note of the formatting requirements of each style.

Which style you choose is often set by your department or your field, but common styles include MLA , Chicago , and APA.

Create APA citations Create MLA citations

In order to stay clear and concise, your thesis should include the most essential information needed to answer your research question. However, chances are you have many contributing documents, like interview transcripts or survey questions . These can be added as appendices , to save space in the main body.

Read more about appendices

Once you’re done writing, the next part of your editing process begins. Leave plenty of time for proofreading and editing prior to submission. Nothing looks worse than grammar mistakes or sloppy spelling errors!

Consider using a professional thesis editing service or grammar checker to make sure your final project is perfect.

Once you’ve submitted your final product, it’s common practice to have a thesis defense, an oral component of your finished work. This is scheduled by your advisor or committee, and usually entails a presentation and Q&A session.

After your defense , your committee will meet to determine if you deserve any departmental honors or accolades. However, keep in mind that defenses are usually just a formality. If there are any serious issues with your work, these should be resolved with your advisor way before a defense.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Survivorship bias
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• Availability heuristic
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• Hindsight bias
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• Supervised vs. unsupervised learning

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The conclusion of your thesis or dissertation shouldn’t take up more than 5–7% of your overall word count.

If you only used a few abbreviations in your thesis or dissertation , you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimizing confusion about abbreviations unfamiliar to your reader.

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

A thesis is typically written by students finishing up a bachelor’s or Master’s degree. Some educational institutions, particularly in the liberal arts, have mandatory theses, but they are often not mandatory to graduate from bachelor’s degrees. It is more common for a thesis to be a graduation requirement from a Master’s degree.

Even if not mandatory, you may want to consider writing a thesis if you:

• Plan to attend graduate school soon
• Have a particular topic you’d like to study more in-depth
• Are considering a career in research
• Would like a capstone experience to tie up your academic experience

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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.

McKinsey Live Event: Unlocking the full value of gen AI

Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.

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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