AI in business: from a problem to a return
The hard part of an AI project is rarely the model. It is deciding which problem is worth solving, and being honest about what the solution returns. Put in a real process — its volume, the time each case takes, the cost of an error — and see what automating part of it is actually worth. Then change the accuracy assumption and watch the return move, often much further than expected, because the manual review that a wrong answer triggers eats the saving. This is the arithmetic behind every AI business case, and doing it before the project rather than after is the single habit that most improves the odds.
About this tool
What it does
Pick an industry to see a problem, the AI approach to it and its impact — then size the opportunity with an ROI calculator you control.
Who it is for
Managers and executives building a case, rather than engineers. No technical background assumed.
Example
You enter:Manufacturing · annual cost 2,000,000 · 30% automated · implementation 250,000.
You get:The modelled annual saving and payback period, recalculated as you move any input.
What it will not do
A generic model, not a quote. It uses the numbers you type and one simple formula — it knows nothing about your systems, data quality or change costs, all of which usually dominate. Treat the output as an order of magnitude for a conversation, never as a business case.
Your data
The calculator runs entirely in your browser. The figures you type are not sent anywhere or stored.
Next step
AI in Business
From problem to ROI
AI only matters when it moves a business metric. Pick an industry to see a concrete Problem → Solution → Impact story, then size the opportunity with the ROI calculator.
An illustrative estimate, not a guaranteed outcome. It uses the numbers you type and one simple model; it knows nothing about your data quality, your systems or your change costs, and those usually decide whether a project pays back.
See a real engagement and what it measured
Discuss your use caseAutomating work does not hand you the money. Redeployed staff, slower rollout and lost productivity during the change all take a share — this is the fraction you keep.
As a share of the build cost — licences, infrastructure, retraining and the people who keep it working. A model is not finished when it ships.
Tie every AI initiative to a metric and a payback period — that's how it gets funded.