How To Score Your AI Use Cases To Drive Prioritization

When it comes to Generative AI, most organizations don’t suffer from a lack of ideas—they suffer from a lack of focus. Everyone has a list of AI experiments they’d love to try, but without a structured approach, those experiments often lead nowhere.

That’s why AI use case identification is one of the most critical steps on the journey to enterprise AI adoption. The right frameworks—an AI use case canvas, structured AI use case frameworks, and impact feasibility scoring—help you separate high-value opportunities from hype.

This article walks through how to evaluate before you build, so you can avoid wasted effort and scale GenAI with confidence. For the broader process, see our step-by-step guide to identifying generative AI use cases.

Why AI Use Case Identification Matters

Too many AI pilots fail because they start with technology instead of business fit. Teams launch projects because “we need to do something with AI,” rather than asking, what problem are we solving and why does it matter?

Without structured evaluation, companies end up with:

• Pilots that stall because they don’t tie to real outcomes.
• Low adoption because workflows don’t reflect how people actually work.
• Wasted budgets on shiny demos that can’t scale.

The antidote is a systematic, repeatable method for ai use case identification—one that keeps your business strategy front and center.

The AI Use Case Identification Process

Here’s a proven four-step process to filter the noise and build a portfolio of meaningful opportunities:

1. Capture ideas broadly
   • Collect from leadership, frontline employees, IT teams, and customer feedback.
   • Use workshops or surveys to surface pain points and opportunities.
2. Frame ideas systematically
   • Use an AI use case canvas or framework to document each idea consistently.
3. Score and prioritize
   • Apply impact–feasibility scoring to identify which ideas are worth pursuing.
4. Build a roadmap
   • Distinguish quick wins from long-term bets, creating a phased plan for adoption.

The AI Use Case Canvas (Practical Tool)

An AI use case canvas is a simple but powerful tool: a one-page template that ensures every idea is framed in terms of business value, feasibility, and risk.

Typical sections include:

• Problem statement – What issue are we solving?
• Data required – What inputs are needed? Do we have them?
• Business value – What measurable outcome will this create?
• Risks and constraints – Compliance, security, reputational considerations.
• Success metrics – How will we know it worked?

Benefits:

• Keeps discussions grounded in outcomes, not hype.
• Makes it easy to compare different use cases apples-to-apples.
• Acts as documentation for governance and prioritization.

👉 Want a head start? Get access to our AI Use Case Canvas Template (CTA).

AI Use Case Frameworks: Structuring the Evaluation

Beyond the canvas, ai use case frameworks provide structured lenses for evaluating potential opportunities. Three common ones include:

• Business Value vs Technical Feasibility
  • Does this use case deliver real impact?
  • Can we realistically implement it with available data, systems, and skills?
• Risk vs Reward
  • High reward but high risk? Worth considering, but only with strong governance.
  • Low reward and high risk? Drop it.
• Strategic Alignment
  • Does the use case directly support corporate goals (efficiency, compliance, customer experience)?

These frameworks aren’t one-size-fits-all—you can adapt them by industry or function. For example, healthcare organizations may weight compliance more heavily, while sales teams may focus on revenue lift.

Scoring with Impact–Feasibility Matrices

Frameworks are useful, but scoring makes prioritization real. An impact–feasibility scoring matrix gives you a visual way to stack-rank ideas.

Impact can include:

• Cost savings
• Revenue lift
• Efficiency gains
• Risk reduction

Feasibility often looks at:

• Data availability and quality
• Technical maturity
• Change management readiness

Once scored, plot them into quadrants:

• Quick wins – high impact, high feasibility.
• Strategic bets – high impact, lower feasibility; worth planning for.
• Long-term plays – promising but not ready yet.
• Distractions – low impact, low feasibility; eliminate.

Best practice: scoring should be done collaboratively by business, IT, and compliance stakeholders. This ensures balanced decisions and avoids bias.

Putting It All Together

When you combine the canvas, frameworks, and impact feasibility scoring, you get a portfolio of use cases that:

• Are grounded in business outcomes.
• Have a clear picture of risk vs reward.
• Prioritize resources where they matter most.

Mini-case example: A finance team starts with 15 potential AI ideas (forecasting, reconciliations, report automation, etc.). Using canvases and scoring, they narrow down to three pilots: automated expense classification, forecasting support, and compliance audit checks. Each was chosen for clear value and feasibility, while higher-risk ideas were put on a roadmap for later.

The result: faster wins, higher adoption, and organizational confidence in scaling AI.

Next Steps: From Evaluation to Execution

Identification is just the first stage. Once you’ve narrowed your portfolio, the next step is to ground those use cases in real operational data using process intelligence and task mining.

That’s where ClearWork comes in—helping you capture the actual steps employees take, so GenAI agents can be built on workflows that reflect reality, not assumptions.

FAQ

Q: What is an AI use case canvas?
A structured, one-page template to frame potential AI use cases in terms of problem, data, value, risks, and metrics.

Q: How do you prioritize AI opportunities?
Use a combination of frameworks and impact feasibility scoring to identify which use cases are high-value and realistic to implement.

Q: What is impact feasibility scoring?
It’s a way to rank opportunities based on business impact (savings, revenue, risk reduction) and feasibility (data, tech, readiness).

Q: How do I know if an AI use case is worth pursuing?
If it scores high on business impact, aligns with strategy, and is feasible with available data—move it forward. If not, deprioritize.

‍