To identify and scale AI use cases, start by targeting work that is repetitive, bottlenecked by scarce skills, or blocked by ambiguity. Then teach teams six reusable “use case primitives” (content creation, research, automation, coding, data analysis, and ideation/strategy). Finally, collect ideas broadly and prioritise a small set for measurable pilots.

Most organisations don’t struggle to find AI ideas. They struggle to pick the ones that deliver value—and then scale them without turning AI into another disconnected tool.

OpenAI’s guide on identifying and scaling AI use cases shares a useful pattern from early adopters: make discovery easy for employees, then apply a simple prioritisation method. (cdn.openai.com)

This post distils that guidance into a practical approach you can run as a workshop, a hackathon, or a departmental rollout.

Step 1: Identify where AI can have immediate impact

OpenAI recommends starting with three “opportunity zones” where AI consistently helps knowledge workers:

1) Repetitive, low-value tasks

These are the tasks people do constantly but wish they didn’t: summarising meeting notes, drafting standard documents, pulling trends from spreadsheets, or answering the same questions repeatedly.

A practical prompt: ask teams to create an anti to-do list—everything they find annoying or low-value and would rather not do again.

2) Skill bottlenecks

Work slows when someone needs an expert (data, design, writing, engineering) and has to wait. AI can bridge some gaps by helping people draft, prototype, analyse, or translate specialist thinking into a usable first pass.

3) Navigating ambiguity

Many projects stall because people don’t know how to start. AI can help teams brainstorm, structure ideas, propose next steps, and turn raw inputs into a direction.

Action to run in 15 minutes: Ask each person to list:

• where they get blocked and struggle to start

• where they do manual work that isn’t the best use of their time

• where they hit a skills bottleneck and need another team to help

Step 2: Teach teams the six AI use case “primitives”

A common reason AI programmes stay small is that discovery relies on a handful of enthusiasts. OpenAI proposes a fix: teach teams a handful of repeatable patterns that cover most real-world use cases.

After analysing 600+ customer use cases, OpenAI found most fall into six fundamental types (or “primitives”):

1. Content creation

2. Research

3. Automation

4. Coding

5. Data analysis

6. Ideation / strategy

The power of this approach is speed. Instead of asking “What should we do with AI?”, you ask “Which primitive maps to our work?”

Example: Content creation (a high-volume primitive)

OpenAI highlights content creation across teams: drafting documents, summarising conversations, polishing writing, translating, and repurposing content for different audiences and channels.

The guide also recommends using AI to learn your organisation’s tone and structure by providing examples (like your best blog posts or your writing guide).

A referenced customer example: Promega used ChatGPT Enterprise for first-draft email campaigns and reported time savings (135 hours in six months) while scaling messaging across markets.

Step 3: Collect and prioritise use cases (without killing momentum)

OpenAI’s approach combines breadth and discipline:

• Breadth: encourage ideas via workshops, hackathons, and peer-led learning.

• Discipline: prioritise a small set of use cases that can be measured and scaled.

A simple prioritisation checklist

Use these criteria to filter use cases quickly:

• Frequency: does this happen daily/weekly across roles?

• Time saved: can you estimate minutes saved per occurrence?

• Quality improvement: will it reduce errors or rework?

• Risk level: what’s the impact of a wrong output, and can humans review?

• Data readiness: do people have the context AI needs (documents, taxonomy, source of truth)?

• Change impact: will people actually adopt it inside their workflow?

Tip: Avoid the temptation to start with the most complex, impressive use case. The guide explicitly notes that complex use cases can slow progress, and empowering employees to find what works for them can be a faster path to success.

The next move: workflow mapping by department

Once you’ve piloted a few wins, OpenAI recommends moving from isolated use cases to department workflow mapping—a structured way to see where AI can reduce friction across an end-to-end process.

This is where AI stops being a tool and becomes a capability: you’re redesigning how work moves, not just speeding up tasks.

What to do this week (a practical starting plan)

If you want progress without a six-month strategy deck:

1. Run a 60–90 minute workshop using the three opportunity zones (repetitive work, bottlenecks, ambiguity).

2. Teach the six primitives and ask teams to map ideas to them.

3. Pick 2–3 use cases with clear measurement and low-to-medium risk.

4. Pilot for two weeks with human review, then document what worked.

5. Expand by mapping a single department workflow and identifying the best AI “inserts”.

Next steps

AI value comes from the boring bits done well: repeatability, measurement, and adoption.

Start with use cases that remove friction today, then scale toward workflow-level redesign. If you’d like support, we typically start by mapping your workflows, selecting high-impact use cases, and designing guardrails so pilots can scale safely.

FAQs

What’s the fastest way to find useful AI use cases?
Target repetitive tasks, skill bottlenecks, and ambiguous work where people struggle to start—then run a workshop to collect ideas and prioritise a small set for pilots. (cdn.openai.com)

What are the “six primitives” of AI use cases?
OpenAI groups most business AI use cases into: content creation, research, automation, coding, data analysis, and ideation/strategy. (cdn.openai.com)

Should we start with one big AI transformation project?
Usually not. OpenAI notes complex use cases can slow you down, and empowering employees to find practical use cases is often faster—then you scale the winners. (cdn.openai.com)

How do we prioritise which use cases to build first?
Prioritise work that’s frequent, measurable, and low enough risk to run with human review. Ensure the data and context exist so AI can be accurate.

What comes after the first pilots?
Workflow mapping by department—so you redesign an end-to-end process rather than bolting AI onto isolated tasks. (cdn.openai.com)