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.
Generative AI pilots tend to fail for a few consistent reasons:
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.
Identifying GenAI use cases systematically ensures:
Start broad. Potential AI use cases exist everywhere:
Workshops, surveys, and ideation sessions are a great way to collect inputs before narrowing them down.
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:
Framing ideas this way prevents “AI for AI’s sake” and keeps discussions anchored in outcomes.
Once you’ve framed ideas, evaluate them systematically with an AI use case framework.
Common frameworks include:
Clear scoring criteria help you quickly eliminate distractions and double down on high-value opportunities.
Now it’s time to prioritize. Use an impact–feasibility scoring matrix to rank opportunities:
This step turns a long list of ideas into a focused portfolio of use cases that can actually move the business forward.
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.
Together, they create a clear picture of your current state operations.
Task mining isn’t just about efficiency; it’s the foundation for AI agent design.
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.
By combining:
…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.
Don’t roll out GenAI everywhere at once. Start small:
The goal is to prove value quickly while building the muscle for broader adoption.
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.
Around 95% of GenAI projects fail because they start with the technology, not the business problem. Common issues include lack of process visibility, poor data grounding, weak prioritization, and hype-driven launches. The difference between failure and success lies in using a structured approach to identify and evaluate use cases before deploying AI.
A structured process ensures every AI initiative aligns with strategic priorities like efficiency, customer experience, or revenue. It increases ROI by focusing resources on high-impact opportunities, reduces risk by avoiding “AI for AI’s sake,” and accelerates adoption by grounding use cases in workflows employees already understand.
Organizations can use canvases and scoring frameworks to evaluate ideas:
Most AI initiatives collapse because companies don’t know how work is truly done. Task mining captures user-level actions (clicks, form fills, copy/paste), while process discovery maps workflows end-to-end. Together, they provide the operational “blueprint” to design AI agents that are trusted, contextual, and effective.
Start small with 1–2 high-value, low-risk use cases. Define measurable outcomes (e.g., time saved, error reduction, improved CSAT), pilot with a limited group, and refine before scaling. ClearWork automates process mapping and task capture, giving enterprises the foundation to design, prioritize, and expand AI use cases with confidence.
Successful GenAI projects often times aren't determined by the technology you use - it's about how you choose to use that technology. With a grounded and thoughtful approach to planning you can ensure that your project is a success! Let's chat and discuss how ClearWork's task mining & process intelligence capabilities can ground your program.
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