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• How to Write a Thesis Statement | 4 Steps & Examples

How to Write a Thesis Statement | 4 Steps & Examples

Published on January 11, 2019 by Shona McCombes . Revised on August 15, 2023 by Eoghan Ryan.

A thesis statement is a sentence that sums up the central point of your paper or essay . It usually comes near the end of your introduction .

Your thesis will look a bit different depending on the type of essay you’re writing. But the thesis statement should always clearly state the main idea you want to get across. Everything else in your essay should relate back to this idea.

You can write your thesis statement by following four simple steps:

• Start with a question
• Write your initial answer
• Develop your answer
• Refine your thesis statement

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Table of contents

What is a thesis statement, placement of the thesis statement, step 1: start with a question, step 2: write your initial answer, step 3: develop your answer, step 4: refine your thesis statement, types of thesis statements, other interesting articles, frequently asked questions about thesis statements.

A thesis statement summarizes the central points of your essay. It is a signpost telling the reader what the essay will argue and why.

The best thesis statements are:

• Concise: A good thesis statement is short and sweet—don’t use more words than necessary. State your point clearly and directly in one or two sentences.
• Contentious: Your thesis shouldn’t be a simple statement of fact that everyone already knows. A good thesis statement is a claim that requires further evidence or analysis to back it up.
• Coherent: Everything mentioned in your thesis statement must be supported and explained in the rest of your paper.

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The thesis statement generally appears at the end of your essay introduction or research paper introduction .

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts and among young people more generally is hotly debated. For many who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its many benefits for education: the internet facilitates easier access to information, exposure to different perspectives, and a flexible learning environment for both students and teachers.

You should come up with an initial thesis, sometimes called a working thesis , early in the writing process . As soon as you’ve decided on your essay topic , you need to work out what you want to say about it—a clear thesis will give your essay direction and structure.

You might already have a question in your assignment, but if not, try to come up with your own. What would you like to find out or decide about your topic?

For example, you might ask:

After some initial research, you can formulate a tentative answer to this question. At this stage it can be simple, and it should guide the research process and writing process .

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Now you need to consider why this is your answer and how you will convince your reader to agree with you. As you read more about your topic and begin writing, your answer should get more detailed.

In your essay about the internet and education, the thesis states your position and sketches out the key arguments you’ll use to support it.

The negatives of internet use are outweighed by its many benefits for education because it facilitates easier access to information.

In your essay about braille, the thesis statement summarizes the key historical development that you’ll explain.

The invention of braille in the 19th century transformed the lives of blind people, allowing them to participate more actively in public life.

A strong thesis statement should tell the reader:

• Why you hold this position
• What they’ll learn from your essay
• The key points of your argument or narrative

The final thesis statement doesn’t just state your position, but summarizes your overall argument or the entire topic you’re going to explain. To strengthen a weak thesis statement, it can help to consider the broader context of your topic.

These examples are more specific and show that you’ll explore your topic in depth.

Your thesis statement should match the goals of your essay, which vary depending on the type of essay you’re writing:

• In an argumentative essay , your thesis statement should take a strong position. Your aim in the essay is to convince your reader of this thesis based on evidence and logical reasoning.
• In an expository essay , you’ll aim to explain the facts of a topic or process. Your thesis statement doesn’t have to include a strong opinion in this case, but it should clearly state the central point you want to make, and mention the key elements you’ll explain.

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!

• Ad hominem fallacy
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• Sunk cost fallacy

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A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

Follow these four steps to come up with a thesis statement :

• Ask a question about your topic .
• Write your initial answer.
• Develop your answer by including reasons.
• Refine your answer, adding more detail and nuance.

The thesis statement should be placed at the end of your essay introduction .

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McCombes, S. (2023, August 15). How to Write a Thesis Statement | 4 Steps & Examples. Scribbr. Retrieved June 11, 2024, from https://www.scribbr.com/academic-essay/thesis-statement/

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Ano Ang Synthesis? – Kahulugan At Mga Halimbawa

Sagot sa tanong na “ano ang synthesis”.

ANO ANG SYNTHESIS – Ang isang syntehis o sintesis ay ang buod o pinakamaikli pero pinaka importanteng impormasyon galing sa isang kwento o pangyayari.

Hindi ito isang panibagong kwento ngunit ang pinakaikling pagsasadula ng mga importanteng ganap sa isang kwento. Taglay nito ang sagot sa mga importanteng tanong katulog ng “Sino, ano, paano, saan, at kailan” na ganap ang mga pangyayari.

Bukod rito, dapat nagkaugnay-ugnay na rin ang mga kaganapan o pangyayari sa isang kwento sa iyon sintesis. Hindi rin ito dapat lumihis sa impormasyon na nakasaad na sa kwento.

Dapat ring panatilihing matibay ang mga katotohanang tagkay nito sa malinaw na paraan ng paglalahad. Isa pa, ang mga punto ng may-akda ay dapat ring bigyan ng halaga.

Ang sintesis rin ay ginagamit upang matulungan ang nagbabasa o nakikinig na lalong maunawaan ang mga bagay na tinalakay ng may-akda.

May tatlong klase rin ito:

• Background Synthesis
• Thesis-Driven Synthesis
• Synthesis for the Literature

Para makagawa ng magandang sintesis, kailangan mong intindihin ang layunin ng may-akda. Pagkatapos, pumili ka ng naayon na sanggunian batay sa layunin.

Sunod, gumawa ka ng plano sa organisasyon ng iyong sulatin. Pagkatapos niyan, pwedeng ka nang sumulat ng mga laman ng sintesis.

Thanks for reading. We aim to provide our readers with the freshest and most in-demand content. Come back next time for the latest news here on Philnews.

BASAHIN RIN: Kabanata 28 Noli Me Tangere – “Mga Sulat” (BUOD)

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Filipino Master's Theses

Theses/dissertations from 2024 2024.

Sipat-danas ng court interpreters sa Metro Manila: Perspektiba, praktika at polisiya , Meryn Lainel B. Moya

Theses/Dissertations from 2023 2023

Narito ako, umiibig: Si Regine Velasquez-Alcasid at ang konstruksyon at signipikasyon ng lokal na pop diva sa lipunang Pilipino , John Christopher Casayuran Avelino

Politika ng kulay: Kalakaran, kampanya, at impluwensiya ng mga piling presidential candidates sa eleksyon 2022, pagsipat sa kilos pampulitika gamit ang kani-kanilang facebook page , John Lloyd O. Canones

Ari at manoro: Semiyolohikal na pagsusuri sa mga piling pelikula mula sa Pampanga , Jericho B. Dela Cruz

Atityud at saloobin sa mga email communication ng mga piling kawani ng La Salle Green Hills: Gawi, isyu, at mungkahing protokol , Michael Bobita Gurung

Si Totoy bato, ang pulosa, at ang kaakuhang Kapampangan: Pagsusuri sa buhay at pulosa ni Totoy bato gamit ang pantawang pananaw ni Rhoderick Nuncio , Oliver Z. Manarang

Halili ng tahanan: Saloobin, pananaw at danas ng limang panganay na anak sa konsepto ng pagpaparaya sa pamilyang Pilipino , Jio S. Orense

Pinoy thirst trappers: Pagkakaiba-iba at paglalarawan ng thirst trapping ng mga piling Pilipinong babae atl lalaking you tuber sa Pilipinas , Dexter B. Raymundo

Ang lalaki sa pelikula: Ang toxic masculinity sa mga piling pelikula , Nixon Paul J. Sumaoang

Kuwentong pag-aalahas: Ang dinamika ng mga kapital sa danas ng mga mag-aalahas ng Meycauayan, Bulacan , Vladimir Bating Villejo

Theses/Dissertations from 2022 2022

Tatlong mukha ng pamamantas-wika, isang talastasang pangwika: Diyalohikong pagbasa kina Bonifacio P. Sibayan, Ernesto A. Constantino, at Andrew B. Gonzalez, FSC sa lente ng hermeneutika ni Hans-Georg Gadamer , Jay Israel B. De Leon

Nakapagpapabagong pinoy boys’ love: Diskurso ng transpormasyon sa anyo ng palabas sa Pilipinas , Marco V. Javier

Semyotikong pagsusuri sa mga rebisyonistang presentasyon ng batas militar ng rehimeng Marcos sa YouTube , Clarissa Mae E. Paranas

Angkla sa pagsampa: Mga kuwento at danas ng mga piling Pilipinang tripulante sa cruise ship , Franz Louise F. Santos

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Ang pagkatha ng adarna sa kasarian: Pagmamalay sa representasyon ng babae sa mga kuwentong pambata ng Adarna House (2009-2019) , Pearl Diane Centeno Asuncion

Ang lipunan sa pelikula, ang pelikula sa lipunan: Sipat-saysay sa mga pelikulang nagwagi sa piling yugto ng Metro Manila Film Festival (2010-2019) , Mariz S. Autor

Pagtuklas-pagbagtas-pagmulat: Isang pagsipat sa danas ng kamuwangang pansarili at kamulatang sosyal ng mga piling mag-aaral ng senior high school (SHS) sa naratibo ng mga piling coming of age film ng Cinemalaya Philippine Independent Film Festival , Christopher Bryan A. Concha

Pagsusuri sa sistema ng apat na proseso ng pagpapatuyo sa kapeng barako ng Lipa gamit ang activity theory , Maria Elena M. Dator

Hibla, habol, hablon: Ang pagpadayon ng kulturang Miagaowanon , Faye N. Fuentes

Ang angas bilang lakas: Mga kuwento ng kabataang babae ng Mataas na Paaralang Pambansa ng Muntinlupa (Senior High School) , Lynette V. Mandap

Pagsusuri sa uri ng inkulturasyong isinusulong ng Apostolic Catholic Church gamit ang lente ng kaisipan ni Anschar Chupungco , Abbygale C. Pinca

NAVOTA(A)S: Pagmamapa ng bentahan sa pamilihang fish port complex gamit ang pag-aaral na socio-economic geography , Reneille Joy M. Tayone

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Ang alternatibo sa alternatibong pamamahayag ng altermidya - people's alternative media network , Christian P. Gopez

Theses/Dissertations from 2015 2015

Pilosopiyang pilipina: Bulungan ng mga kaisipan nina Quito at Mananzan , Leslie Anne L. Liwanag

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Meet Eleanor Wikstrom, a Fil-Am Harvard Summa Cum Laude who pushed for the university's historic Filipino language course

By Yoniel Acebuche Published Jun 03, 2024 8:19 pm

Eleanor Wikstrom's work uplifting the Filipino and Filipino-American communities has just begun. 

The California-based Fil-am recently graduated Summa Cum Laude from Harvard with a degree of Bachelor of Arts in Social Studies. She also gained the Sophia Freund Prize, which is the highest GPA in her college.

Her senior thesis on US colonialism in the Philippines won numerous awards including the Alexis de Tocqueville Prize for Best Thesis in Social Studies, the Senior Thesis Prize in Ethnicity, Migration, and Rights, and the Thomas Temple Hoopes Prize.

Speaking to PhilSTAR L!fe , Wikstrom considers these milestones incredibly meaningful because they represent a "win" for the Filipino and Filipino-American community.

"I understand that graduating Summa Cum Laude and having such distinctions on my thesis mean that it represents something more than myself, it's also a win for the Filipino community and the Filipino American community, and also for the community where I grew up in California. And so I think it's very meaningful for me," she told L!fe .

"[These awards are] the beginning of a lot of work. In some ways, it's the culmination of an academic career and a really extensive project that I've been working on for several years."

But her passion for the communities she represents doesn't end with her graduation. She added that it's work that she'll continue throughout the rest of her career and academics.

After Harvard, the Oakland native is set to study at the University of Oxford in the United Kingdom in 2025 as she's one of the recipients of the American Rhodes Scholarship. At Oxford, Wikstrom will study imperial and global history.

Representing Filipinos at Harvard

At Harvard, Wikstrom found that the Philippines and its communities in the US were underrepresented relative to its population in the country. About 4.5 million Filipinos and Filipino-Americans live in the United States according to a 2021 census .

"Going into a setting where there are very few Filipinos can be very difficult because it means that you don't really know if you're going to be able to learn more about your heritage or learn, learn more about your motherland or culture. But at the same time, it also means that you have to be very intentional about forming strong bonds with the other Filipinos that are there."

She continued, "[Since] I had the opportunity to meet other people who are equally as passionate about celebrating and uplifting the Philippines and their Filipino and Filipino American heritage, that ended up being one of the most meaningful parts of my time at Harvard, was connecting with other members of that community."

Wikstrom even played a pivotal role in pushing for a Filipino language course at Harvard as she wrote an opinion piece about its absence.

In 2023, the university began offering a course on Filipino for the first time in its nearly 400-year history. Cavite teacher Lady Aileen Orsal was hired as the subject's instructor.

"There's a really rich and important history behind the reason why Tagalog was so widely spoken in the United States, but that there was also a very conspicuous ignorance and gap about the Philippines in American education systems," she explained to L!fe.

Filipino is the fourth most spoken language in the United States behind English, Spanish, and Chinese.

With her senior thesis, she wanted to be able to trace back how colonialism in the Philippines changed what knowledge was considered valid and valuable. She also studied how this colonial rule, in turn, affected the United States, too. 

"I grew up in the United States, and that's the setting that I'm most familiar with, I wanted to make sure that that side of the history was also being exposed and being taught that we could start reckoning with it and start teaching Tagalog at places like Harvard, or start teaching more Filipino history, because it's such a big part of the United States history as well, it just often gets obscured."

'The most important thing is to be genuine'

Wikstrom, also the co-president of Harvard Philippine Forum, implied to L!fe that her academic journey at Harvard wasn't smooth sailing.

"I wouldn't want to give the impression that it was still smooth sailing the whole time because I want to make sure that especially other Filipinos who go to Harvard understand that it's okay to struggle," she noted.

Wikstrom added, "[There's] a lot of the other things that they had in order to make sure that I could be successful, like knowing that it was okay to ask for help if I didn't know how to navigate something or if I thought I needed additional assistance. I was definitely living in the current learning curve."

When asked about tips for those hoping to get into Harvard, Wikstrom said, "I would say the most important thing is to be genuine."

"Harvard interviewers are reading thousands of applications a year. They know people who are trying to craft their narrative in order to seem like the most impressive thing. But if you genuinely love what you do, and you genuinely want to make a difference in the world—which sounds cliche, but I actually want to do that and there [are] many other people who actually want to do that," she shared.

Lastly, Wikstrom said that when entering universities like Harvard, one should know one's priorities, which include self-love and well-being.

"It's easy to think that if you just [grind] all the time, and [sleep] three hours a night, [spend] all of your time doing extracurriculars, that it'll lead to success. But if you're not taking care of yourself, it's not sustainable."

TAGS: Philippines education Filipino-American harvard university Thesis eleanor wikstrom american rhodes scholarship us colonialism

Yoniel Acebuche

Yoniel Acebuche is a journalist by profession and a beauty queen by passion. She has constantly joined beauty pageants in different cities and fiestas across the Philippines ever since she was 15. Writing and pageantry are her empowering ways to connect to other people as well as to understand herself.

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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