ROI of AI
The ROI of AI is the measurable business return from an AI initiative relative to its full cost. Returns come from labor hours saved, faster cycle times, fewer errors, higher conversion or retention, or new revenue; costs include building or licensing, integration, usage fees, change management, and upkeep. It is measured against a baseline taken before launch.
01why it matters for a business
Many AI initiatives cannot show a return because nobody defined one. The project started as an experiment, success was measured in demos or usage, and the baseline was never recorded. When budgets tighten, those initiatives are hard to defend, even when they help. Starting from the P&L, with a specific metric, a baseline, and a target, makes AI spending a business decision instead of a technology bet.
Realistic ROI also counts the less visible costs: operations staff time during the build, process changes, review of AI output, and ongoing tuning. And it attributes value honestly. Hours saved only become money if they are redeployed to valuable work or avoid a hire. Pinned Golf is a concrete case: as the engineering need dropped from five engineers to one, the payroll line that never got created was the return.
02what it looks like in practice
A claims operation wants AI to pre-fill claim intake from submitted documents. Before building, it measures the baseline: average handling time per claim, error rate on keyed fields, and backlog at month end. The business case states the target improvement, what the freed capacity will be used for (absorbing growth without new hires), and the full cost over two years. After launch, the same metrics are tracked monthly against the baseline, and a quarterly review decides whether to expand, adjust, or stop.
03common mistakes
- No baseline measurement before launch.
- Counting hours saved as dollars saved without a plan for the freed time.
- Leaving out integration, change management, and maintenance costs.
- Measuring adoption instead of outcomes.
04related terms
- Total cost of ownership (TCO)Total cost of ownership (TCO) is the full cost of a technology decision over its useful life, not just the purchase price or build quote.
- AI readiness assessmentAn AI readiness assessment is a structured review of whether a company is prepared to get value from AI and where to start.
- Proof of concept vs productionA proof of concept (PoC) is a quick, limited build that shows an idea can work, usually on sample data without real users.
- Inference costInference cost is what it costs to run an AI model to produce outputs, as opposed to the cost of training it.
- Build vs buyBuild vs buy is the decision between developing custom software, including AI systems, and purchasing or subscribing to an existing product.
05where insomnia club fits
Insomnia Club scopes AI implementations against a business case: the P&L first, a fixed budget you can take to the board, and working software every two weeks so returns can be measured early.
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Scoped by the people who ship it. Priced before we start.
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