Identifying and scaling AI use cases
In just two years
39%
39% of U.S. adults have already used AI. In comparison, the internet reached just 20% adoption in its first two years.
AI leaders have seen
1.5x
1.5x faster revenue growth, 1.6x higher shareholder returns, and 1.4x better return on invested capital than their less advanced peers.
Yet only
1%
1% of a recent McKinsey survey believed their AI investments had reached full maturity.
In just two years, 39% of U.S. adults have already used AI.(opens in a new window) The internet reached only 20% adoption in the same time frame. The rise of AI is not only reshaping industries but also creating opportunities for individual employees. AI frees people up to do higher-value work, expand their skills, and advance their careers.
In one study, BCG found(opens in a new window) that in the last three years, AI leaders have seen 1.5x faster revenue growth, 1.6x higher shareholder returns, and 1.4x better return on invested capital than their less advanced peers.
According to McKinsey(opens in a new window), 92% of companies plan to increase their investment in AI. Yet, many organizations still need guidance on how to realize tangible value, with only 1% believing their AI investments have reached full maturity.
We’ve observed firsthand what sets successful AI projects apart. Our insights come from 300 of our most successful implementations, more than 4,000 adoption surveys, and over 2 million business users.
This guide is designed to help your organization find and scale AI use cases that deliver clear value. We break the process down into three steps:
Identifying opportunities to apply AI in your business by understanding what it excels at.
Teaching your employees fundamental use cases that can speed up discovery across every department.
Collecting and prioritizing use cases that will have the biggest impact on your business.
Throughout, you'll find customer stories, practical checklists, and use case examples tailored to different departments to support your team’s progress.
It’s important to recognize that AI adoption means much more than just finding the right use cases. Beyond the scope of this guide are topics such as how you build an AI-first culture, cultivate higher value use cases, and inspire adoption across your company. We’ll share more on those issues in other guides, but for now, let’s zoom in on the process of finding the right use cases for your company.
“This is a time when you should be getting benefits [from AI] and hope that your competitors are just playing around and experimenting.”
Keep these three principles in mind. They're the backdrop to all the practical guidance you'll find ahead.
AI should be led and encouraged by leadership.
Complex use cases can feel impressive, but often slow you down. Instead, empowering employees to find use cases that work best for them, and your company, is often a faster path to success.
Encouraging adoption with hackathons, use case workshops, and peer-led learning sessions is a catalyst for many of our customers.
Let’s walk through the best steps for sourcing use cases for your teams.
Step one is finding parts of your business that can be immediately improved with AI.
One way to do that is to think of AI as a way to create super-assistants for your workforce. AI super-assistants never get tired or lose focus. They’re always available whenever you need help. And they can flex across almost any task, augmenting your employees’ skills.
To identify potential AI use cases, focus on common workplace challenges in these three key areas:
Repetitive low value tasks
Skill bottlenecks
Navigating ambiguity
Let’s walk through the best steps for sourcing use cases for your teams.
Be specific on why AI adoption is key to your company's future, whether it's keeping pace with competitors, responding to evolving customer expectations, or sustaining growth. When employees hear a thoughtful “why,” it creates trust and clarity, helping them see how these changes align with their own work and goals.
“Every time I do something I find annoying, I ask myself, how can I not have to do this again?”
Be specific on why AI adoption is key to your company's future, whether it's keeping pace with competitors, responding to evolving customer expectations, or sustaining growth. When employees hear a thoughtful “why,” it creates trust and clarity, helping them see how these changes align with their own work and goals.
Example
Our product manager use AI to create interactive prototypes without needing to slow down to wait for other teams to help.
Knowledge work often involves ambiguity and open-ended challenges. Employees may struggle to get started or become blocked, causing projects to stall. Here AI can act as a catalyst, helping to generate ideas, analyze data, and propose next steps when the path is unclear.
People across all the companies we spoke to are using AI to kick-start their thinking and unblock new ideas. They’re using it to brainstorm campaign ideas, look for quick insights in raw data, analyze trends, or just figure out next steps when they’re not sure what to do.
Example
Our marketing team brainstorms campaign ideas with voice mode in ChatGPT to unblock creativity and start working towards a brief.
Focusing on these types of work can help you quickly identify high-impact AI opportunities, helping your teams optimize workflows, reduce bottlenecks, and speed up innovation across your organization.
“We formed an AI automation task force with this guiding principle. We asked all finance team members to detail processes that they felt could benefit from AI. We took that list and created a roadmap of projects we wanted to explore.”
Ask your teams to list scenarios and tasks where they:
Struggle to get started or run into blockers
Spend a lot of time on manual work that others don’t always appreciate or value, or isn’t the best use of their time (i.e., their “antito-do list”)
Hit a skills bottleneck until another team can come along and help them (data analysis, design, on-brand writing, and web development are good examples)
Use these lists to start sourcing possible spaces for new use cases.
This can be done at the start of a workshop or a hackathon to help your employees see where to start.
Or, use this prompt to ask ChatGPT for some interesting use cases:
Once you’ve given your teams a framework for identifying new AI opportunities, the next step is to train them on the fundamental ways they can use AI. To help with this, we analyzed over 600 use cases sourced from our customers. Most use cases fall into one of six 'primitives'—fundamental use case types that apply across all departments and disciplines:
These primitives are a quick way to help your employees find the most promising use cases for your business. Each primitive represents hundreds of use cases we’ve seen across industries, roles, and workflows, making them a fast track to scalable value.
Let's take a closer look at each primitive, starting with Content creation:
AI can support content creation across all teams—whether summarizing sales calls or generating first drafts of strategy documents, blog posts, web pages, and even images and visualizations. We see teams using AI to edit and polish their work, then bring it in as a last-minute proofreader.
AI can automatically write in your company’s style and apply your tone of voice guide; follow your preferred document structures; or even provide feedback on writing. It can then translate your work into different languages or repurpose it for different audiences, channels, or programs.
When writing, AI can take in the complete context of a conversation or consider a set of uploaded documents to shape the output. For example, try uploading your writing guide or use your five best blog posts, then prompt ChatGPT to create a detailed writing guide based on those examples.
Marketing | Create campaign strategies, headlines or email campaigns. Generate content outlines and first drafts. Repurpose content for different audiences or channels. |
Finance teams | Create draft policy docs and technical accounting memos for expert review. |
Product teams | Build product requirements docs, generate product descriptions, release notes, launch communications, and user guides. |
Sales teams | Generate account plans, scripts for calls, and follow-up emails. |
Promega scaled messaging across markets and audiences
Promega, the life sciences company, saved 135 hours in their first six months using ChatGPT Enterprise for first-draft email campaigns. They also use it to generate campaign briefs from a message doc, and to translate any copy into paid ads for specific marketing channels.
“The time we get back from aligning on the strategy of emails can be invested into the content generation that improves the email experience. I don’t know the last time I wrote a marketing email without using this GPT”
AI is widely used for research across industries. From quick learning about new concepts (like AI adoption or design thinking); to searching the web for relevant articles or competitive data; to more comprehensive, multistep research projects that scan the web for articles, data points, and insights. We see teams uploading long, internal documents for quick insights too.
One of the biggest advantages of using AI for research is that you can specify the format and structure of how the analysis is presented to you: in table format, bullet points, organized in specific sections, or cross-referenced.
AI’s attention to detail and ability to follow instructions makes it a great research assistant.
Sales and Marketing | Investigate new industries, understand competitors better, and research new audiences. |
Finance | Search for benchmarks from publicly listed companies, M&A targets, or articles and guidance on accounting standards. |
Product | Size new markets, research competitors, identify trends, and analyze user feedback. |
Sales teams | Search the web for new vendors and rate their products’ strengths and weaknesses. |
Software Engineering | Review API endpoints and external documentation. |
Introducing Deep Research
Deep research is a new agentic capability in ChatGPT that conducts multi-step research on the internet, independently. Give it a prompt and ChatGPT will find, analyze, and synthesize hundreds of online sources to create a comprehensive report at the level of a research analyst. Find out more.
Many software engineers are power users of AI. They use it for debugging, generating first-draft code in unfamiliar languages, porting code from one language to another, and rubber-ducking their code. In the past two years, AI’s capabilities in math, science, and coding across many languages have significantly improved, and many tools now even offer real-time code previews.
We’re also seeing many non-coders take up coding with the assistance of AI tools. Just by using natural language, marketers and finance teams are able to build Python scripts to automate processes, SQL queries to retrieve data, or even visualizations with front-end code for websites or internal presentations.
Software Engineers | Debug or rubber duck code, port it to other languages, and research API endpoints. |
Marketing | Build interactive charts and data visualizations to share with web and design teams, or write SQL for data analysis. |
Finance | Create Python scripts to automate parts of the monthly close. |
Product | Build interactive prototypes to quickly flesh out new product ideas. |
Tinder speeds up coding
Tinder’s engineering team uses ChatGPT to generate first-draft syntax when working with non-intuitive languages—like Bash scripts—that require specialized knowledge. ChatGPT improves their coding efficiency, making it easy to reference and query external API documentation, and troubleshoot architectural and design decisions.
“There were tasks in Jira that used to get deprioritized because they felt like a chore. Now I wind up taking them on because I know it's going to be easier to tackle with ChatGPT at my side.”
AI helps anyone harmonize data from different sources, identify insights and trends, and work with complex spreadsheet data without needing advanced Excel, SQL, or Python skills.
You can provide AI with multiple spreadsheets or screenshots of dashboards to support quick analysis. It can interpret spreadsheet data, understand visual charts, and even help format your output for reporting. You can also guide how results are structured, such as specifying preferred chart types, summary formats, or comparison logic.
Marketing | Upload webinar attendance data and quickly visualize it. Summarize key trends from a dashboard screenshot. |
Product | Analyze trends, social media feedback, or upload CRM data on feature requests to surface new opportunities. |
Sales | Review your account lists to find your strongest accounts. Map leads to accounts and score them for intent signals. |
Finance | Quickly analyze expense data and look for trends, or harmonize data from different spreadsheets and databases. |
Poshmark finds more time for insights and strategy
Poshmark, the fashion marketplace, used ChatGPT to generate the Python code that reconciles millions of spreadsheet rows for their business performance analysis. They then use AI to generate weekly performance reports and accounting memos for executives, saving hours of manual work every week.
“We’ve dramatically reduced manual work and improved speed, accuracy, communication and insights. I'm seeing an elevation of everyone's job.”
Ideation and Strategy use cases are popular across all teams, from brainstorming a new blog post to helping structure a document, troubleshooting a strategy, or giving feedback on work based on key goals or stakeholder preferences.
As AI models become more multimodal, we’re seeing teams use voice and vision to interact with AI just as they would with a colleague.
And as models become more capable of thinking through complex problems, we’re seeing many teams build strategic plans with them, taking into account their data, goals, context, constraints, and dependencies.
Marketing | Brainstorm campaign ideas based on new opportunities. Upload your marketing brief and ask what’s missing. Prompt for a go-to-market plan for a product launch. |
Product | Build a market expansion plan for a new geography, taking into account local competitors, risks, size of opportunity, and resource demands. |
Sales | Build launch plans that reflect all dependencies and risks. Upload your PRD and identify areas of weakness before an executive review. |
Finance | Practice your pitch or discovery skills with voice mode. |
Match Group simulates focus groups
Match Group, the global leader in online dating, is experimenting with GPT‑4’s multimodal capabilities to run focus group simulations for product usability. By uploading wireframes and asking ChatGPT to mimic a specific persona, designers can pose questions while they ask the “user” to navigate the interface and provide feedback. The result: new ideas for product innovations without the extra cost and delay.
Many use cases involve automating parts of a task. We’ve seen customers identify repeatable, routine tasks and design ways to hand them off to AI. Automations can be simple, like generating weekly competitive updates, or more complex, like creating a finance report for weekly executive briefings, ready for human review.
Memory and custom instructions are the key to automating these kinds of processes. Custom GPTs are the method for sharing them. By creating a standard set of instructions, uploading the same document, and specifying the same output every time, teams are able to offload lower value tasks.
Today these automations are often individual tasks, but with products like deep research and Operator, we’re moving into a world where AI can take on multi-step tasks independently and on schedule.
Marketing | Create a standard report and visualizations for quick webinar readouts. Or build Slack update summaries from meeting notes or transcripts. |
Product | Create a launch update summarizer. Or summarize and share weekly customer insights. Turn meeting notes into Slack posts for executives that summarize dependencies and next steps. |
Finance | Turn weekly financial data into an executive overview, with alerts for changes that need attention. |
IT | Upload your software architecture as a screenshot and ask for key dependencies, risks, and opportunities for optimization. |
BBVA automates parts of their Credit Analysis work
BBVA’s Credit Analysis Pro GPT helps credit risk analysts accelerate their assessments by pulling unstructured data from a variety of sources, such as annual reports, ESG assessments, and the press.
Teach your teams the fundamentals of each primitive and provide examples for each department.
Next, start brainstorming ideas for new use cases, running hackathons, or company-wide competitions to see who can source the most impactful use cases.
Learn about Bain’s use case olympics for a specific framework.
Set up a spreadsheet, or Slack channel where you can collect all the use cases that your teams come up with.
Estée Lauder’s GPT Lab starts with cross-discipline teams—including a business user, a subject matter expert, and a technical lead—to identify and develop high-impact use cases. Their process is simple and repeatable:
Design: The business user defines the purpose, scope, and audience in a two-page brief.
Prepare: The SME gathers relevant data to shape the use case around best practice.
Build and test: The tech lead builds the GPT, integrates the data sets, and tests the GPT for accuracy and consistency.
Launch: The full team deploys the GPT and creates a user guide.
Pivot and scale: The full team uses feedback loops to iterate and optimize based on GPT performance.
“We’re looking at every business process—from legal to research, to manufacturing, to commercial—and thinking about how to redesign them with AI.”
For more detail, read about the Estée Lauder GPT Lab.
Once teams understand key use cases and begin identifying problems to solve, use cases tend to multiply quickly.
The challenge then shifts from discovery to prioritization. Which use cases can you scale to impact all employees? Which are most likely to deliver cost efficiencies now? Which might lead to a new product or revenue stream?
Our customer success teams use this Impact/Effort Framework to help enterprise customers prioritize use cases. It’s a simple quadrant that scores each use case against the value to the company and the degree of effort it requires.
High ROI focus | Quick wins with strong impact and low effort—often the best place to start building momentum. |
Self-service | The lowest-effort projects that a single user might spin up for themselves as a personal assistant on a given task. Many start as individual solutions, but often become valuable across teams. |
High-value/high-effort | Often transformational (like Moderna’s Dose GPT or Klarna’s customer assistant) but these use cases typically require more time, planning, and resources to build. Many teams start with quick wins to build momentum and use them as inspiration for investing in higher-value projects. |
High-effort/low-impact | These can be safely put aside for now. But new products and capabilities might make them easier to build and deploy, so be open to promoting them. |
Thanks to Jeret Shuck from Softbank for showing us how he uses this simple but powerful tool.
62%
of AI's value lies in core business functions
Evaluating and prioritizing your AI use case opportunities in this way helps accelerate the big wins that create further interest and investment.
Promote the prioritization framework company-wide, encouraging employees to use it in team meetings to identify the best ideas.
For high-value, high-effort use cases, consider deploying a Custom GPT as you scope the work required.
Have your leaders champion use cases that make an impact on whole departments. Top-down support is a key hallmark of successful AI deployments.
Re-evaluate this scoring each quarter as the high-effort use cases you have today might become low-effort as AI capabilities advance.
Most teams begin by using AI for individual tasks: editing blog posts, generating campaign briefs, or drafting policies. It’s easier to think of AI in the context of specific, discrete tasks.
But as we watch power users embed AI into everything they do, we often see them find use cases that start to span multi-step workflows.
Use deep research to explore market trends
Analyze customer data to estimate opportunity size
Brainstorm a launch strategy using voice mode
Generate messaging, campaign assets, and translations
Helping your teams think of AI as something they can embed from start to finish will prepare them for a future where AI agents can complete entire projects on their behalf.
An example:
Using AI across a Marketing workflow
Deep research to understand market trends and opportunities
Data analysis to size the audience and opportunity
Brainstorm on campaign strategy and developing the brief
Content creation to help craft key messages and copy
Automate content localization and channel optimization
Encourage power users to: break workflows into individual tasks, identify core use cases (primitives), and clearly map each step.
AI isn’t like traditional software or cloud apps. Learning to harness its strengths requires a new mindset. But our work with our customers has shown us how quickly people across all disciplines can learn this mindset and start to spot high-impact use cases in their work.
Kickstarting this process comes down to helping your organization take three steps:
Understand where AI adds value
Identify parts of your business that can immediately benefit from AI.Teach your employees fundamental use cases
Help teams explore foundational use cases, and start building their own.Prioritize what to scale
Focus on high-impact, low-effort opportunities using the Impact/Effort Framework.
The more people work with AI to re-engineer tasks and workflows, the more opportunities they uncover.
We hope this guide gives your team a clear way to begin. We’re here to support the journey as you move from ideas to outcomes.
“We’re looking at every business process—from legal to research, to manufacturing, to commercial—and thinking about how to redesign them with AI.”
