The AI Hype Problem
Every vendor is adding "AI-powered" to their marketing. Your team is asking about ChatGPT. Your board wants to know your AI strategy. But the real question isn't "should we use AI?" — it's "where does AI actually create value for our specific business?"
The Value Framework
Not all AI use cases are created equal. Evaluate potential use cases across three dimensions:
Volume
AI excels at tasks that happen frequently. If your team processes 500 support tickets a day, AI-powered triage is valuable. If you handle 5 tickets a day, a human is probably fine.
Consistency
AI is great at tasks that need to be done the same way every time. Data extraction, classification, formatting — these benefit from AI's consistency. Creative strategy or nuanced negotiation? Less so.
Cost of Errors
Consider what happens when AI gets it wrong. Auto-categorizing support tickets with 90% accuracy? Fine — humans catch the mistakes. Generating medical diagnoses with 90% accuracy? Dangerous. Match AI to use cases where errors are correctable.
High-Value Use Cases We See
Based on our consulting work, these use cases consistently deliver ROI:
- Document processing and data extraction
- Support ticket triage and initial response drafting
- Internal knowledge base Q&A
- Code review assistance for development teams
- Report generation from structured data
- Email and communication drafting
Low-Value Use Cases (For Now)
These sound exciting but rarely deliver:
- Replacing experienced decision-makers
- Fully autonomous customer interactions (without human review)
- AI for the sake of AI (no clear business problem)
- Complex multi-step processes with high error costs
Start Small, Prove Value, Then Scale
Pick one use case that scores high on volume and consistency, and low on error cost. Build a proof of concept. Measure the impact. Then decide whether to scale.
The organizations that succeed with AI aren't the ones that try to do everything at once — they're the ones that start with a clear problem and prove value before expanding.