AI Strategy
How to Identify High-ROI AI Automation Opportunities in 30 Days
A repeatable diagnostic for finding the processes where AI pays back fastest — without a six-month discovery phase.
Most AI opportunity assessments start with the technology and work backwards. They produce a long list of plausible use cases and a short list of shipped ones. Flip the order — start with the cost structure of the work itself — and the high-ROI opportunities surface in weeks, not quarters.
Begin with volume, not novelty
The first filter is volume. A process that runs 40,000 times a month at $3 of effort each is a $1.4M annual cost base. Automate 60% of it and you have an $850k outcome before you have chosen a model. Novelty is a poor proxy for value; frequency is a reliable one.
Ask each department head for three processes they would eliminate tomorrow if they could. Cross-reference the answers with transaction counts from your systems of record. The overlap is your shortlist.
Score on four dimensions
For every candidate, score four things: annual cost, data availability, decision complexity, and blast radius if it goes wrong. A simple 1–5 scale is enough. The opportunities that score well on cost and data but low on complexity and blast radius are where you start.
- Annual cost: headcount × frequency × unit effort.
- Data availability: is the input already structured or digitised?
- Decision complexity: how much judgement does the task require?
- Blast radius: what happens to the customer if this fails silently?
Pressure-test with a kill criterion
Before you build, write down the number that would make you stop. If the pilot cannot hit 70% of the target accuracy at the target cost within four weeks, you kill it and move to the next item on the shortlist. A kill criterion is what separates a portfolio from a graveyard.
Run this diagnostic across two or three functions and you will have a costed, ranked roadmap in 30 days — and the confidence to defend it to a finance committee.
