🎯 'How do we pick the RIGHT AI agent use case?
written by Stefan Christoph
🎯 “How do we pick the RIGHT AI agent use case?
This is the question I hear most from customers exploring agentic AI.
Here’s the mechanism I run through together with the customer:
The 4-Quadrant Evaluation
When a customer brings me 5-10 agent ideas, we structure each one across four dimensions:
📊 Business Value & Strategic Fit → What pain does it solve? For whom? How often? → Can we quantify the impact? (Revenue, cost, time, quality) → Which KPI moves if this works for 6 months?
🔧 Technical & Data Feasibility → Do the data sources exist and are they accessible? → How many integrations? What’s the smallest “walking skeleton”? → Can we ship a realistic PoC in 6-12 weeks?
⚠️ Risk, Ethics & Compliance → What’s the worst realistic failure scenario? → Where do we need human-in-the-loop controls? → Any regulatory or privacy constraints?
🤖 Agent-Fit & UX → Is this truly multi-step with tool orchestration? → Does autonomy add value vs. a simple prediction? → What does a day-in-the-life look like with this agent?
The Process Flow Insight
Here’s what I’ve learned: When the use case is about improving existing processes, we first map the current workflow.
That flow diagram becomes the blueprint for the agentic flow—and more often than not, we discover it needs multiple agents working together, not just one.
The visual makes it obvious where handoffs happen, where decisions branch, and where each agent’s responsibility begins and ends.
The Outcome
After scoring each idea (1-5) across these dimensions, we plot them on an impact/feasibility matrix.
The “high impact, high feasibility, high agent-fit” quadrant? That’s where we find our 1-2 priority use cases for the PoC.
No guesswork. No pet projects. Just structured thinking that gets customers from “AI sounds cool” to “here’s what we’re building next week.
What’s your go-to question when evaluating AI agent use cases? Drop it in the comments—I’m always looking to refine this mechanism.
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