How To Choose the Right AI Use Case

TL;DR: Most teams waste time on AI because they start with use cases that sound impressive rather than ones that actually work. This piece shows you a practical way to choose where generative AI fits, where traditional AI is a better choice, and when you need both. You’ll see how to make the decision in minutes instead of weeks and avoid pilots that stall because they never suited the problem in the first place.

 
Teams usually ask me where they should “start with AI.”
They expect a tool list. What they really need is a decision method.

Too many early projects fail for the same reason: the wrong type of AI got matched to the wrong type of problem. It’s avoidable, and it has nothing to do with technical skill. It comes down to making the choice based on the output you need, not the hype around the tool.

This is the part nobody tells beginners clearly enough.

Where generative AI belongs

If the outcome you want is written, summarised, explained or restructured, you are in generative AI territory.

It fits cleanly when the task involves:

• writing drafts or emails
  
• summarising long documents
  
• explaining complex content
  
• responding to questions in plain language
  
• extracting meaning from messy text
  
• helping developers with documentation or tests

Generative AI works because it handles unstructured information. If your work sits inside documents, notes, chat transcripts, reports or PDFs, this is usually the right fit.

Where traditional AI is still the better choice

Some teams assume generative AI replaces everything.
It doesn’t.

Traditional AI still outperforms generative models when the task is structured and predictable:

• classifying issues
  
• detecting defects
  
• forecasting demand or revenue
  
• predicting risk
  
• segmenting customers

These tasks rely on historical data and patterns. Prediction, detection and scoring belong here. If you need a label, a probability or a forecast, use traditional AI and avoid unnecessary complexity.

Where the best use cases sit: the overlap

Most real work needs more than one model.

Examples you’ll see in practice:

1. A churn model predicts risk → generative AI explains the reasoning to the service team.
   
2. A defect model flags an issue → generative AI drafts the customer message.
   
3. A forecast model predicts demand → generative AI builds the summary for the planning meeting.

The pattern is simple:

• Traditional AI gives you the signal.
  
• Generative AI gives you the explanation.

This combination removes hours of manual interpretation and a simple way to choose the right approach.

Here’s the decision method I teach in my AI masterclasses because it works for beginners and it cuts through they current AI hype void pretty fast.

Ask four questions:

1. What outcome are we trying to achieve?
Time saved, fewer errors, improved service, cost reduction.

2. What format does the output need?
Text, summaries, explanations → generative AI
Predictions, scores, labels → traditional AI

3. Do we have training data?
Yes → traditional models perform better
No → generative AI with clear prompting is faster and safer

4. Do we need a win quickly?
If yes, choose generative.
It avoids long training cycles and gets value on the board early.

That’s it.
This is what stops teams from over-building their first project or picking a use case that never had a chance.

What organisations learn once they apply this

Once teams start matching the work to the right model, they notice the same things:

• Workflows get easier to improve because the decisions make sense.
  
• Pilots don’t collapse halfway through.
  
• People stop arguing about tools and start talking about outcomes.
  
• Leaders see value without waiting six months for a model to train.
  
• Teams build capability without fizzing out themselves.

This is the difference between adoption and disruption.

A real example: overloaded support teams

Support teams see this problem every day: too many tickets, slow replies, no time to think. So we apply the framework:

Traditional AI → identifies sentiment, classifies issue types, detects patterns.
Generative AI → drafts responses, summarises history, creates tickets with context.

The result is measurable:

• Faster handling.
  
• Lower backlog.
  
• More accurate summaries.
  
• Better handover.
  
• No extra headcount.

This is the type of use case that works immediately because the workflow already exists.

FAQ 

Q: How do I explain this decision to executives who want “generative AI everywhere”?
A: Show the output. If the task needs written content, choose generative. If it needs prediction, choose traditional. Executives respond to clarity, not complexity.

Q: What if my use case needs both?
A: Use traditional AI for the structured part and generative AI for the interpretation or summary. They complement each other.

Q: How do I avoid picking a use case that won’t deliver value?
A: Test it against the four-question method. If it fails any step, deprioritise it.

Q: Who should own this decision?
A: Business leaders set the goal. Domain experts define requirements. Data teams confirm feasibility. All three sign off.

Q: How soon should we see results?
A: For generative AI, days. For traditional AI, weeks. If it takes longer, the use case was scoped wrong.

If your organisation wants a clear, structured way to prioritise use cases and move from “guessing” to confident adoption, my AI Fundamentals Masterclass and AI Bootcamp show you how to choose correctly, move quickly and avoid the traps that stall early AI projects.