I’ve sat through more than a few leadership meetings where someone says, “Let’s list out all the possible AI use cases.” And out comes the usual grab bag: chatbots, predictive maintenance, personalization, fraud detection.

Idea generation isn’t the hard part. The real challenge is figuring out what’s worth pursuing—what will drive meaningful value and can realistically be implemented.

That’s where most efforts stall. The list gets longer, not sharper. And six months later, it’s still sitting in a slide deck.

The Real Problem: Misaligned Priorities

Too often, teams prioritize use cases based on what’s trendy or what they wish they could do, rather than what the business is ready for.

I’ve seen examples like:

• Pushing for advanced forecasting before the data pipeline is stabilized
• Investing in generative AI tools without a content workflow to support them
• Starting pilots with no internal owner, limited funding, and no clear success metrics

In each case, the issue isn’t lack of ambition, it’s misalignment between value, readiness, and execution.

A Better Way to Prioritize

When I work with teams to sort through competing AI ideas, I use a simple but effective framework that scores each use case on two dimensions:

1. Business Impact
   Does it create value that matters? This could mean increasing revenue, improving customer experience, reducing cost, or enabling strategic advantage.
2. Feasibility
   Can the organization realistically execute this with the data, tech, talent, and alignment it currently has?

I often map the results in a simple 2×2 grid:

The Process I Use with Clients

Here’s how I typically guide teams through prioritization:

Step 1: Start broad.
Begin with a working session to generate a range of use case ideas—across departments, processes, and pain points.

Step 2: Score each use case.
Using a rating criteria (usually a 1–5 scale), the team rates each idea on both impact and feasibility. While the framework is simple, I guide participants through what actually goes into each dimension—things like data availability, internal skillsets, regulatory complexity, and strategic alignment. These conversations help uncover blind spots and bring clarity to the trade-offs between different scenarios.

Step 3: Map and discuss.
Plotting the ideas on a 2×2 helps reveal what’s immediately actionable, what needs enabling investment, and what can wait.

Step 4: Build the first wave.
Focus on a few high-impact, feasible use cases to kick off your roadmap. These “early wins” help build credibility and unlock capacity for more complex efforts later on.

A Composite Scenario

Let’s say a company has brainstormed 12 use cases, from automating invoice processing to enhancing customer churn prediction.

When we walk through the framework:

• Invoice automation ranks high on both impact and feasibility. If the data exists, and the cost savings are measurable.
• Churn prediction is high impact but low feasibility if there is fragmented customer data and unclear ownership.
• A few others—like AI-powered meeting notes—are easy to implement but low value, and may distract from bigger priorities. It is good to have a “quick wins” or two in the roadmap, but think through how it benefits the bigger picture.

Within a few hours, the team has a clearer sense of where to focus, what to defer, and which foundational gaps need to be addressed.

Final Thought

You don’t need a longer list of AI ideas; you need a smarter way to sort through them.
By combining business value with feasibility, you can move from scattered pilots to a focused roadmap with a real chance of success.

Not sure where to start?

If you’re exploring AI use cases and want a practical way to align them with business value, I’m happy to share more. Contact me, Kate Wade, Managing Director of Wade Strategy, LLC and Founding Partner of The AI Strategies Group (AISG), to explore how your organization can take the first strategic step toward responsible, practical, and profitable AI adoption.

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