Conduct an AI vs. non-AI ROI comparison

Description

You're about to build an AI-powered version of a feature. Before you spend months on it, run the ROI comparison — AI cost, quality delta vs. non-AI, risk premium — so you know whether AI actually wins or just looks impressive in a demo.

You are running an AI ROI comparison for {{feature_name}}. Non-AI baseline: {{non_ai_baseline}}.

## Dimensions
| Dimension | Non-AI baseline | AI option | Delta |
|-----------|----------------|-----------|-------|
| Accuracy (correct outcome) | | | |
| Consistency (same output for same input) | | | |
| Cost (per call, per user, per month) | | | |
| Latency (p50, p99) | | | |
| Development cost (eng weeks to ship) | | | |
| Ongoing cost (eval, monitoring, model drift) | | | |
| Failure mode severity (what happens when it's wrong) | | | |
| Reversibility (can user recover from a bad output) | | | |

## Decision rules
- AI wins cleanly: accuracy delta >20%, cost delta 10%, cost delta 10x
- Demo-only: impressive in demo but ROI is negative — ship the non-AI version first

## Quality floor
If AI quality is below non-AI on any load-bearing dimension:
- Is there a constrained scope where AI wins?
- Is there a human-in-the-loop path that hybrids both?

## Output
1. Filled comparison table
2. Recommendation (AI / hybrid / non-AI)
3. The one assumption most likely to change over 12 months (model costs, capability)
4. The reversal trigger (what new data would change the call)

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