AI ROI
AI ROI is the money an AI system returns compared with everything it costs to build and run it: savings and new revenue weighed against licences, integration, data work and human oversight.
No jargon, no math
In plain words, what is it?
ROI starts with an honest baseline: what the process costs today in hours, errors and delays. Then you stack the real costs: licences, integration, data cleanup, training, the people who supervise the system, and the maintenance nobody budgets for. On the other side you count only banked benefits: hours actually converted into fewer shifts or more throughput, error rates pulled from production logs, not from the demo. Divide what you gained by what you spent, and give it a time window. Where it breaks: soft productivity gains that evaporate, costs hidden in IT tickets, and people who measure the pilot while enthusiasm is high and call it the steady state.
- 01
Baseline. Record what the process costs today: hours, errors, delays.
- 02
Cost. Count licences, integration, data work, oversight and maintenance.
- 03
Benefit. Track only banked gains: shifts saved, errors avoided, throughput up.
- 04
Decide. Returns above costs: scale. Below: fix it or shut it down.
Terms that live nearby.
AI Strategy
An AI strategy is the plan that turns AI from scattered experiments into a coordinated capability: which processes you automate, in what order, with what data, budget and governance, and when you stop.
ReadAI Audit
An AI audit is a structured review of how an organisation actually uses AI: every tool, model and workflow is inventoried, then tested for accuracy, data exposure, cost and compliance, with findings ranked by risk and a remediation plan attached.
ReadAI Readiness
AI readiness measures how prepared an organisation's data, processes, people and governance are to run AI in production, and it is assessed before any model is deployed.
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We design and ship AI systems with permissions, logs and human approval. It starts with a 30-minute scoping call.