Where does AI automation actually pay off — and where does it quietly cost more than it saves? A clear-eyed model for the costs, returns and risks behind the hype.
Beyond the hype
Automation has a real cost structure: build, integration, model inference, monitoring and the human oversight that rarely goes to zero. ROI is the return net of all of it, not the headline hours saved.
Where the returns are
High-volume, rules-heavy, error-prone work — document processing, triage, reconciliation — is where AI compounds. The economics get marginal on low-volume or high-judgment tasks where a person is faster anyway.
The hidden costs
Inference at scale, eval and monitoring infrastructure, and the long tail of exceptions all add up. The cheapest pilot can become an expensive product if you don't model these from the start.
A framework for the call
Score each candidate on volume, error cost, judgment required and integration effort. Fund the ones with a clear payback; be honest about the ones that are cheaper left as they are.