The most common reason AI projects fail isn't technical. Models are good. APIs are cheap. Infrastructure is commoditised. What kills most AI initiatives is the absence of a measurable problem statement before a single line of code is written.
We use a simple framework across every engagement. First: identify the current state metric. How long does the task take today? How many errors occur? How many FTE hours are consumed? If you can't answer this, you can't measure improvement.
Second: define the target state. What does success look like in 6 months? A 70% reduction in processing time? Zero manual re-entry? Consistent output quality across all cases? The target must be specific, measurable, and agreed before discovery ends.
Third: back-calculate the cost of inaction. If the current process takes 4 hours per report and your team produces 200 reports per month, that's 800 hours. At $60/hour fully loaded, you're spending $576,000 per year on a workflow that AI can compress to 20 minutes per report. The ROI maths become obvious — and so does the budget for the build.
