Practical Generative AI: From Hype to Measurable ROI
Every company has seen a generative AI demo that impresses for five minutes and disappears from the radar two weeks later. The problem is rarely the model — it's the lack of a real process behind it.
The approach that works starts small: pick a high-volume process with clear rules, like email triage, report summarization, or first-response to customers. Measure how long it takes today, without AI, before any pilot. Without that baseline, any later result is opinion, not data.
The second step is having a human review the model's output for the first few weeks — not to distrust the technology, but to calibrate prompts and spot the cases where it fails confidently, which are the most dangerous ones because they look correct.
Only after that does it make sense to talk about integrating with existing systems (ERP, CRM, spreadsheets) and end-to-end automation. Companies that jump straight to "automate everything" often spend more time fixing exceptions than they saved on the original process.
Measurable ROI, in this context, is simply: hours saved times the cost per hour, minus the cost of running the solution (model, integration, residual human review). If that math doesn't work out within a three-month pilot, the problem isn't the AI — it's the choice of process.