── ── AI & agents
AI ROI Business Case
AI business cases are usually inflated the same three ways: they count only "hours saved," they omit the costs that arrive after the subscription (inference, integration, human review, change management), and they assume full adoption on day one. The result is an ROI that looks decisive on the slide and never shows up in the P&L — and, with no…
Run AI ROI Business Case on a real problem
Bring something you're actually deciding — free, in the browser.
How it works
1. Measure the baseline. Monthly volume, time per instance, fully loaded labor cost, error rate, cycle time — from timing or system records. Gate: no baseline data → record 2–4 weeks first; stop. 2. Pick value levers. Hours saved · incremental revenue · errors avoided · speed. For each, write the realization path (hire fewer, redeploy, take more volume). Gate: freed time has nowhere to go (no cost cut, no extra output) → count it as zero. 3. Cost everything. License/subscription · inference (estimate tokens/calls by volume) · integration build and maintenance · human review time · training and change management · vendor switching cost. Gate: review time not counted → stop. 4. Apply the adoption ramp. Set adoption by month (pilot → partial → full) and discount value by it. Gate: 100% adoption in month 1 → redo. 5. Compute payback and ROI. The month cumulative net cash flow turns positive; 12-month ROI. 6. Run sensitivity. Pessimistic / base / optimistic on at least adoption rate, inference unit price, and review share. Gate: pessimistic payback exceeds the acceptable ceiling → shrink scope or don't do it. 7. Write the kill line. In advance: "if metric Y is not met by month X → stop or downgrade," plus who decides. 8. Stop-rule: if the value rests mainly on unmeasurable "strategic importance," this method does not apply → run a small, option-style pilot instead.
Output template: Workflow · Baseline (volume/time/errors) · Lever + realization path · Monthly full cost · Adoption ramp · Payback (pess/base/opt) · 12-mo ROI · Kill line + decider
→ Method in Action: Klarna's AI assistant — the $40M estimate and the quality reversal (2024–2025)
When to use it
- user says 'is this AI project worth doing?', 'calculate the ROI of AI', 'how long until this agent pays back?', 'build the AI business case for my boss/investors', 'what does this AI tool really cost us?', or is about to pay for an AI tool/agent or replace a manual workflow with one
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Klarna's AI Assistant — The $40M Estimate and the Quality Reversal (2024–2025)
Klarna's customer-service assistant is the most widely quoted public AI business case of the period, and it is unusual in that the company published its headline numbers. Fifteen months later the CEO publicly said the evaluation had leaned too hard on cost. Both statements are on the record, which makes it a clean test of the ROI table: what the published case contained, and which line of the table the reversal came from.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
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