Enterprise AI Adoption Starts With Workflows, Not Models

Every enterprise AI rollout I've been close to starts with the same question, asked in the wrong order: "which model should we use?" That question assumes the hard part is model selection. It almost never is. The hard part is understanding the workflow the model is supposed to sit inside: who touches the data, in what sequence, with what exceptions, and who is accountable when the process breaks.

I've sat in enough discovery sessions to notice a pattern. The team can describe their workflow in a slide with five boxes and four arrows. The actual workflow has fifteen steps, three of which live in someone's inbox, two of which are "ask Dave," and one of which only happens during month-end close. AI adoption fails when it's designed against the five-box version and deployed into the fifteen-step reality.

The fix isn't more sophisticated modeling. It's slower, less glamorous discovery: sitting with the people who run the process today, watching them work, and mapping the workflow as it actually happens, including the workarounds. Only once that map exists does model selection become a tractable decision, because now you know what "good" looks like at each step, where human judgment has to stay in the loop, and where the current process is already broken independent of AI.

The organisations that get this right treat the workflow map as the deliverable, not a throat-clearing exercise before the real work. They also revisit it, because a workflow drawn accurately in January can be wrong by June once the AI layer changes what people actually do day to day. Adoption isn't a launch, it's a maintenance discipline.

If there's one habit I'd recommend to anyone starting an enterprise AI initiative, it's this: before you evaluate a single model, spend a week shadowing the process you're trying to improve. Everything downstream (architecture, governance, change management) gets easier once you're solving the real problem instead of the one on the slide.