Free Tool: Start Prioritising your AI Use Cases

Every organisation I work with has the same problem. They don't lack AI ideas, instead they're drowning in them.

The executive team comes back from a conference buzzing about agentic AI. Someone in finance saw a demo of automated invoice processing. Customer service wants a chatbot. IT has a backlog of "AI opportunities" that grows faster than anyone can evaluate them.

And yet, more often than not, they don't have a clue where to start. This isn't a technology problem; it's a business problem.

The Four Failures I Keep Seeing

After spending the last year almost exclusively talking about AI, I've watched the same patterns play out repeatedly:

They can't prioritise. Twenty use cases on a whiteboard, but no framework for deciding which ones matter. Everything is "high priority." Nothing gets done.

They lack business rigour. AI initiatives get evaluated on vibes and vendor demos, not on value, risk, confidence, and effort. When the CFO asks for a business case, everyone sort of looks at each other blankly.

They jump to solutions. "We need RAG" or "We need an agent" just because they're fashionable. Well before anyone has properly articulated what problem they're solving, for whom, and what success looks like.

They can't communicate value. Even good ideas die because teams can't explain them clearly. The gap between technical possibility and business justification is vast.

So I Built Something

I wanted a tool that would force the right conversations. Not a spreadsheet. Not a slide deck that gets filled in once and forgotten. Something interactive that teases out the information that actually matters.

The AI Use Case Value Framework does four things:

It structures the problem. Every use case goes through SCI (Situation, Complication, Implication) and NABC (Need, Approach, Benefits, Competition) frameworks. These aren't bureaucratic exercises, they're ultimately the questions your CFO will ask and I find it's much better to answer them upfront.

It scores across five dimensions. Value. Confidence. Effort. Risk. Time-to-value. Each dimension has specific inputs that force you to confront reality: Do you actually have baseline metrics? How sensitive is this process to AI errors? Who's sponsoring this?

It prioritises automatically. Use cases get sorted into Now, Next, and Later bands. A 2x2 matrix plots value against feasibility. No more arguing about gut feelings because the scores create a defensible rationale.

It recommends AI patterns. Based on what you're trying to do, the tool suggests whether you need classification, RAG, summarisation, drafting, or agentic workflows. It also flags cautions: "Low error tolerance without human review? Reconsider."

The output is something you can actually take to leadership: a ranked portfolio with clear reasoning.

Platform-Agnostic by Design

I work primarily in the ServiceNow ecosystem, but I deliberately built this tool to work anywhere. The problem of "too many AI ideas, not enough prioritisation" isn't unique to any platform. Whether you're evaluating use cases for Salesforce, SAP, Microsoft, or a custom build, the underlying questions are the same:

• Is this valuable enough to pursue?

• Are we confident we can deliver it?

• What's the real effort involved?

• What risks are we accepting?

• How quickly will we see results?

The framework doesn't care what technology you're using. It cares whether you've thought the problem through.

Guess what? I Built This With AI

Naturally, because I haven't got a shred of development experience, I built this tool with Claude. Not just because I like the experience, but also to prove exactly what's possible in modern times.

The entire application: React front-end, scoring engine, pattern recommendation logic, PDF export, and deployment configuration were developed in a conversation with an AI assistant. I provided the requirements, made design decisions, and directed the build (often through dictation). Claude wrote the code.

I'm not trying to be one of those many AI influencers that have cropped up all over LinkedIn. I'm trying to prove that we're entering an era where the barrier to building isn't technical skill, it's clarity of thought. I knew exactly what problem I wanted to solve and how the solution should work. The AI handled the implementation.

That's the same shift your organisation needs to make with AI use cases. Stop asking "what can AI do?" Start asking "what problem are we solving, and is AI the right approach?"

the companies that do the best won't be the ones with the most AI projects. They'll be the ones who picked the right projects and executed them with discipline.

Try It Yourself

The AI Use Case Value Framework is free to use. No account required.

Add your use cases, work through the scoring, and see where they land. If nothing else, the process of answering the questions will sharpen your thinking. And if it helps you kill a bad idea before you waste six months on it, even better.

[Try the AI Use Case Value Framework → here]