AI Use Case Planning & Deployment Starts By Understanding Your People & Your Processes

Generative AI is everywhere right now. But while the hype is high, the success rates are not. Most companies rush into pilots without a clear plan, only to find that the project stalls, fails to scale, or produces little measurable value. In fact, 95% of GenAI projects fail because they start with the technology instead of the business problem.

The good news: you don’t have to be in that 95%. With the right process, you can systematically identify generative AI use cases that fit your business, align with your strategy, and deliver measurable ROI.

This guide walks step by step through how to identify, evaluate, and prioritize AI opportunities—using frameworks, canvases, and process intelligence. And most importantly, we’ll show why task mining and process discovery are the foundation for building AI agents grounded in how your business really operates.

Why Most AI Pilots Fail (and What to Do Differently)

Generative AI pilots tend to fail for a few consistent reasons:

• Hype-driven starts: Projects are launched because “we need to do AI,” not because they solve a business problem.
• No process visibility: Teams don’t actually know how work is done today, so the AI solution doesn’t fit into daily workflows.
• Poor data grounding: Without access to the right operational data, agents make unreliable or irrelevant decisions.
• Lack of prioritization: All ideas are treated as equal, wasting resources on low-impact use cases.

The difference between the 95% that fail and the 5% that succeed comes down to one thing: a structured approach to AI use case identification, deployment and grounding.

The Business Value of Systematic Use Case Identification

Identifying GenAI use cases systematically ensures:

• Strategic alignment – Every AI project ties directly to efficiency, customer experience, compliance, or revenue.
• Higher ROI – You focus resources on the few use cases that deliver measurable impact.
• Faster adoption – Use cases are grounded in real workflows employees already understand.
• Reduced risk – You avoid wasting effort on projects that sound exciting but don’t map to business needs.

Step 1: Capture Ideas Across the Organization

Start broad. Potential AI use cases exist everywhere:

• Frontline employees who see repetitive, manual work daily.
• Customer feedback that reveals friction in service.
• IT and data teams that know which systems are overloaded with requests.
• Leaders who need better insights and faster decision-making.

Workshops, surveys, and ideation sessions are a great way to collect inputs before narrowing them down.

Step 2: Frame Ideas with an AI Use Case Canvas

Not every idea is worth pursuing. That’s where the AI use case canvas comes in.

A simple one-page canvas helps you answer key questions:

• What problem are we solving?
• What data sources are required?
• What business value will this create?
• What risks or compliance issues exist?
• How will we measure success?

Framing ideas this way prevents “AI for AI’s sake” and keeps discussions anchored in outcomes.

Step 3: Evaluate with a Framework

Once you’ve framed ideas, evaluate them systematically with an AI use case framework.

Common frameworks include:

• Business value vs feasibility: Will this move the needle, and can we realistically implement it?
• Risk vs reward: Does the potential upside outweigh compliance, reputational, or technical risk?
• Strategic alignment: Does this support your organization’s top priorities?

Clear scoring criteria help you quickly eliminate distractions and double down on high-value opportunities.

Step 4: Score with Impact–Feasibility Matrices

Now it’s time to prioritize. Use an impact–feasibility scoring matrix to rank opportunities:

• Quick wins: High impact, easy to implement.
• Strategic bets: High impact, harder to implement—worth planning for.
• Low-value distractions: Drop them.
• Long-term plays: Keep on the radar, but don’t prioritize now.

This step turns a long list of ideas into a focused portfolio of use cases that can actually move the business forward.

Step 5: Map the Current State with Task Mining and Process Discovery

Here’s where most AI initiatives fall apart: companies don’t actually know how work is done today. Without visibility into real workflows, any AI solution is built on guesswork.

This is where task mining and process discovery come in.

• Process discovery maps out your workflows end-to-end by analyzing user activity, and business data.
• Task mining captures user-level actions—clicks, copy/paste, form fills, research steps—so you see the granular details of how work actually gets done.

Together, they create a clear picture of your current state operations.

Why this matters for GenAI

Task mining isn’t just about efficiency; it’s the foundation for AI agent design.

• Grounding in reality: AI agents can only be effective if they’re trained on how your business actually operates, not on assumptions.
• Workflow detail: By capturing every step of a process, task mining provides the blueprint for agent workflows.
• Error reduction: Agents trained on real-world task data make fewer irrelevant or risky decisions.

With ClearWork, this process is automated. Instead of relying on consultants or manual workshops, ClearWork captures and analyzes your real operational data—building a foundation for AI that is trusted, contextual, and grounded in how your teams truly work.

Step 6: Build and Prioritize the Portfolio

By combining:

• Canvases to frame ideas,
• Frameworks and scoring to evaluate, and
• Process intelligence + task mining to ground them in reality,

…you now have a ranked list of AI use cases with a clear business case and technical foundation.

Quick wins can be piloted immediately. Strategic bets can be planned as your data and governance mature.

Step 7: Pilot, Measure, Expand

Don’t roll out GenAI everywhere at once. Start small:

• Choose 1–2 high-value, low-risk use cases.
• Define clear metrics (time saved, error reduction, CSAT improvements).
• Pilot with a limited group of users.
• Capture feedback, refine, and scale.

The goal is to prove value quickly while building the muscle for broader adoption.

Common Pitfalls to Avoid

• Jumping straight to technology without use case identification.
• Ignoring the current state of processes and tasks.
• Chasing hype instead of aligning with business priorities.
• Skipping governance and controls, leading to compliance risks.

Conclusion: From Hype to High-Value AI

Identifying generative AI use cases isn’t about brainstorming cool ideas—it’s about building a systematic, grounded approach that ties directly to business value.

By capturing ideas, framing them with a canvas, scoring them for impact, and grounding them in task mining and process discovery, you create a pipeline of AI opportunities that are achievable, measurable, and transformative.

ClearWork helps enterprises make this shift by automatically mapping processes, capturing task-level activity, and providing the foundation for AI agents grounded in real operational data.

If you want to be in the 5% of companies that succeed with GenAI, the path is clear: identify the right use cases, ground them in your processes, and scale with confidence.

FAQ

Q: How do I identify generative AI use cases in my business?
Start with an AI use case canvas to frame ideas, then evaluate with impact–feasibility scoring. Ground the results with task mining and process discovery.

Q: What is an AI use case canvas?
A one-page template to define problem, data, value, risks, and success metrics for each AI idea.

Q: Why is task mining important for AI?
Task mining captures the actual steps employees take in their daily work, which becomes the blueprint for AI agent workflows.

Q: Where should I start?
Begin by identifying use cases with high business value and data availability, then pilot quickly with a focus on measurable outcomes.