AI Use Case Prioritization

When you have a long list of AI use cases, how do you pick the right ones?

Typically, AI use case prioritization works like this: brainstorm a bunch of ideas, score them by impact and feasibility, plot a grid, build whatever lands in the “high value, high feasibility” bucket. But after having worked the last 5+ years in applied AI consulting I can say there’s definitely more to AI use case prioritization than this.

Use case prioritization is fundamentally about navigating two competing AI adoption strategies: going for transformational moonshots or quickly accumulating many smaller wins to compound.

Most successful portfolios I’ve seen combine both, but the prioritization mechanics differ depending on which path dominates. There's no universal step-by-step approach to this but I found a few practical principles that help me structure the chaos.

Today, I wanted to share these with you.

Let’s dive in.

One of the key reasons only a fraction of AI use cases meet ROI expectations is duplication and disconnection between projects. Companies try to build the same foundation three times and make the same errors twice because nobody checked what’s happening in the other silo.

That’s why effective prioritization isn’t just a ranking, but also an alignment and communication exercise. I've written about this idea before: it helps to connect the dots.

But I haven’t really written here about the underlying mechanics and principles that make this work.

So here’s what I use:

Prioritization starts even before the impact-feasibility matrix. Not every AI idea deserves a place on the grid. Before prioritizing, ensure every use case clears a minimal expected business value. Because a cluttered backlog quickly destroys strategic focus.

The tool I use for this is the $10K Threshold. If a problem doesn’t clear this initial bar, it doesn’t even land on the list. Perhaps, it could be a case for personal productivity, but that’s another Track of AI ROI.

Merely looking at business functions is typically too siloed for AI projects to really be successful.

Marketing wants a content generator. Sales wants lead scoring. Support wants a chatbot. Three departments, three budgets, three vendor conversations. It’s classic org-chart thinking.

The better lens is to cluster use cases around shared AI capabilities and what I call business value streams.

By capabilities I mean the reusable building blocks underneath specific use cases – things like document intelligence, forecasting, recommendation, knowledge retrieval, classification, workflow orchestration, etc. These cut across jobs and departments. A knowledge retrieval capability built for a customer FAQ chatbot can power internal search, onboarding, and sales enablement. One building block, four applications.

By value streams I mean the end-to-end flow of how your business actually creates and delivers a valuable outcome. Instead of asking "what does the support team want?" ask "where in our customer service value stream do we lose speed or quality on the way to closing a ticket?" That question connects the support agent's ticket backlog to the product team's documentation gaps to the onboarding team's handoff problems. Different functions, same value stream, same outcome. Potentially the same AI capability.

When you cluster use cases this way, the models, the governance, the evaluation methods — they all become reusable across neighboring problems.

I often find that ten "different" use cases are really three capabilities applied to different contexts. That reframe alone changes how you budget and sequence AI work.

Sometimes your list has one or two high-impact, low-feasibility use cases. The big bets. The ones everyone gets excited about but nobody knows how to scope. Don't park them, and don't try to build them in one shot either.

Use them as directional anchors.

Take the ambitious case and decompose it into self-sustained increments – smaller pieces that each deliver value on their own but accumulate toward the larger vision.

For example: customer support automation. The moonshot is fully autonomous ticket resolution – an AI agent that handles issues end to end. Big, hairy, low feasibility if you try to build it directly. (Klarna had to learn this the hard way)

But instead of going for this use case directly, you can decompose it:

→ Increment 1: A simple Q&A chatbot that deflects the recurring 40% of tickets. It finances building the knowledge base, retrieval pipeline, and customer-facing channel – infrastructure you'll reuse in every phase after this.

→ Increment 2: A triage system that classifies and routes what the chatbot can't deflect. It reuses the chatbot's understanding layer and adds routing logic.

→ Increment 3: Agentic resolution for specific ticket types. Using the knowledge base from increment 1 and the routing logic from increment 2, this support agent is now able to resolve certain pre-defined tasks automatically. It starts very narrow – like sending login credentials or checking users’ order status – but it allows you to expand as the solution builds trust.

The moonshot defines the roadmap. It’s not just a single item on it.

The counterpart to the anchor approach is to list as many high-feasibility ideas as possible (important that they all clear the threshold from 1!). This is the “quick win” strategy. But this strategy only works if the quick wins compound. Otherwise you’ll end up with 20 tools across 5 departments and admin overhead that kills ROI.

So instead of picking the easiest use case, shipping it, and moving on, look for which quick wins connect before you even start building.

Say you have five feasible ideas:

Scored individually, the automated meeting summaries case might be the most feasible and might draw your attention.

But zoom out: four of those five need the same knowledge retrieval capability. The meeting summaries don't. That changes the priority. Every project here becomes incremental, not greenfield.

So the decision is clear: Get a vendor for meeting summaries but don’t worry about it too much. Accept an out-of-the-box quality that will be OK, but still need human oversight (like reviewing the summaries).

Focus your attention on building the retrieval capability. This could start with a cleaned-up SharePoint, or a hosted platform that allows fast document ingestion.

The key question here is: "which quick wins, built together, create leverage for everything that comes after?"

If you've followed these principles, you've been doing the same thing over and over: looking for overlaps, evaluating whether they matter, and acting on the ones that compound. I call this the 3S Framework (explained in detail in The Profitable AI Advantage): Spot, Size, Seize.

→ Spot the overlaps: where’s shared data, shared technology, shared processes, or shared customer touchpoints across your use case list?

→ Size the overlaps: does combining them actually increase impact, or improve feasibility? If yes – how? If not, don't force it.

→ Seize the overlaps: bundle the connected cases under shared foundations and sequence them so each phase builds on the last.

AI use case prioritization is portfolio design, not backlog management.

The impact-feasibility grid is a useful starting point. But the real work happens after the plotting: clustering around capabilities, decomposing anchors into increments, connecting quick wins into compounding platforms.

Isolated ROI thinking builds one-off projects, but strategic sequencing and compounding builds long-term capability.

That’s how you move from experiments to AI that actually pays off.

See you next Saturday!

Tobias

PS: The 3S Framework and the full prioritization methodology behind this article are covered in depth in my book, The Profitable AI Advantage. If you're working through your own use case list right now, you might find this helpful: