AI ROI Begins With Workflow Understanding

I see a lot of AI business cases that start with the model. Token cost, accuracy, latency, vendor pricing. Those matter. But they are not the ROI case. The ROI case starts with a simple question: what problem are we actually solving, and what does the current workflow cost to run?

Step one is technical feasibility. Can AI actually solve this problem with acceptable quality? Sometimes you need a prototype or proof of concept to find out. That is fine. Just do not confuse a working demo with a funded business case.

Step two is future state cost. What will it take to build and operate this at scale? Model consumption and tokens are part of it. So are infrastructure, engineering, integration, monitoring, and ongoing support. If the business owner cannot see a credible cost estimate for running the solution, the conversation is not ready for approval.

Step three is current state economics. What are people doing today? How many hours does the work take? How many handoffs are involved? What systems does the workflow touch? What does it cost in labor, rework, delays, or risk? What exactly is being automated or improved?

This is where many AI initiatives stall. The technology team can describe the model. The business team can describe the ambition. But nobody has mapped the workflow in enough detail to compare before and after. Without that, ROI becomes a slide with directional arrows instead of a decision the CFO can defend.

The comparison itself is straightforward in concept. Current state cost versus future state cost, adjusted for build and run expenses. The hard part is getting honest numbers on the current workflow. That usually means sitting with the people who do the work, not only reading the official procedure.

ROI is not just a technology calculation. It depends on understanding the business workflow, what it costs today, and what will change when AI is in production. Get that clear first. Then the model economics have something useful to attach to.