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AI ROI. Past the hype.
Labor saved vs inference cost vs setup. The honest business case for any generative-AI feature.
Inputs
Results (annual)
Hours saved
8,250
all users / year
Labor saved
$495,000
hours × rate
Inference cost
$1,320
tokens × price
Payback
0.5 mo
setup / monthly net
Benchmark
Clear winner
Payback 0.5 months — deploy.
How it works.
Always measure the baseline before deploying. 'Time without AI' is the single most-skipped step — and the one that makes ROI numbers credible.
FAQ.
How do I measure AI ROI?+
Time saved × hourly rate + error reduction value − (inference cost + integration + maintenance). Track per use-case, not per model — averages hide the losers.
What's the biggest AI deployment mistake?+
Skipping the baseline. Without 'time without AI' measured cleanly, every post-AI number looks like a win. Always benchmark before deploying.
When does an AI feature pay back?+
Customer-facing AI (recommendations, scoring, search): 3-6 months. Internal AI (summarization, drafts): often within weeks. Build-your-own LLMs: 18+ months — rarely worth it.
Should I use GPT-4, Claude or open-source?+
Start with hosted APIs (GPT, Claude, Gemini). They're the fastest path to validated ROI. Move to open-source only when cost-per-token genuinely matters at scale (>$10K/month inference).
How do I account for hallucinations?+
Subtract the cost of bad outputs: support hours fixing wrong answers, reputational risk, opportunity cost. For low-stakes use-cases this is small; for legal/medical it can wipe out all savings.