Conduct an AI vs. non-AI ROI comparison

AI Is Not Automatically Better

The default assumption in product orgs — if a feature can use AI, it should — produces features that demo well and ROI poorly. Reforge's AI research documents the pattern: AI wins cleanly in some workflows (unstructured text synthesis, fuzzy matching), loses to deterministic code in others (exact calculations, regulatory-required correctness), and breaks even with higher cost in many middle-ground cases. GitHub's developer productivity research reaches similar conclusions on AI coding tools.

How the Conduct an AI vs. non-AI ROI comparison Prompt Works

The prompt runs a structured comparison across 8 dimensions with quantified targets and produces one of four recommendations — AI wins cleanly, wins with risk premium, loses, or is demo-only. The 12-month assumption output keeps the analysis updatable as model costs and capabilities evolve.

When to Use It

• An AI-powered feature is being proposed and the analysis is vibes-based.
• Leadership is asking why AI features take longer than expected.
• A non-AI baseline exists and AI is being considered as a replacement.
• A cost-conscious quarter requires ROI discipline.
• A previous AI feature flopped and the team wants better diligence.

Common Pitfalls

• Comparing only accuracy. AI accuracy can be higher while cost, latency, and failure severity make it a loss overall.
• Ignoring ongoing costs. Eval, monitoring, and drift handling are not free. Include them.
• Demo-driven decisions. Feature looks impressive in demo and produces negative ROI in production — a common pattern worth naming.

Sources

• AI Adoption in Product Orgs — Reforge
• AI use at work has nearly doubled in two years — Gallup
• GitHub Developer Research — GitHub
• The Product Strategy Stack — Reforge