Of everything in the field guide, the talent tier framework generates the most pushback. Worth working through it in public.
Three tiers. Builders make AI systems exist. Orchestrators put existing models to work, deploying, governing and scaling them. Enhanced professionals fold AI into work they were already doing.
The claim is that most organizations need far more of the second and third than the first, and hire as though the reverse were true.
It arrives in roughly the same form every time. "That framework is fine in general, but our situation is genuinely novel. We need builders."
Sometimes that's right. More often it's a description of ambition rather than a description of the work.
Three questions usually resolve it.
Are you training or fine-tuning models, or are you configuring and deploying them? This sounds obvious and isn't, because "we're building an AI product" describes both. A product built on a vendor's model through prompting, retrieval and orchestration is a real product and an impressive engineering achievement, and it doesn't need a research scientist.
If you hired a machine learning engineer tomorrow, what would they do in month one? If the honest answer involves data pipelines, evaluation harnesses and integration work, you've described an orchestrator and you're about to pay a premium for someone who will be bored by March.
Does your data support what you're planning to build? Most novel-model ambitions run aground here before they run aground on talent. If the data engineering isn't done, hiring a builder puts an expensive person in a queue.
It isn't only budget, though the comp difference is real.
The builder pool is small and the searches are long. Chasing the wrong tier can cost you two quarters before anyone reassesses, and the guide's observation that this usually surfaces around month five matches what we see.
Then there's retention. A researcher hired into a deployment role leaves, and they leave faster than a normal bad-fit hire because the gap between the pitch and the work is obvious in week two. You've spent the premium, absorbed the search time, and now you're restarting.
Less discussed and more common: organizations under-hire orchestrators because the titles don't sound senior enough to justify the comp.
An MLOps engineer or an AI governance lead often costs less than a machine learning engineer and does more to determine whether the initiative reaches production. That's an uncomfortable thing to put in a headcount request, because it reads as asking for less.
The reframe that works in our experience: if 88% of pilots die on governance, data readiness and observability, then this is the tier that gets you to production. Everything else is upstream of a problem you haven't solved yet.
Look at the requisition list.
If it's weighted toward builders and you are not building models, the plan and the org chart have come apart somewhere. It's the fastest diagnostic in the guide and it takes about ninety seconds.
Section 2.3 of the field guide has the full framework, including where each tier is sourced from.