How to Evaluate Whether AI Will Actually Help Your Business

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.