The Job Description That Speeds Up AI Adoption 5x
The fastest way to speed up AI adoption isn't a better tool. It's a job description most companies haven't written yet.
A few months ago I watched a team demo fifteen AI tools in a two-hour session. Every tool was impressive. Every tool worked. At the end of the meeting, nobody knew which one to start with, who owned the rollout, or how they'd measure if it was working.
That meeting is why AI adoption stalls. Not the tools — the gap between the tool and someone whose job it is to close it.
The role nobody has written a job description for
Most companies approach AI adoption as a procurement problem. Buy the right tool, distribute access, and wait for usage to climb. When it doesn't, they buy a different tool.
What's actually missing is a role I've started calling the Agent Orchestrator. Not a data scientist. Not an IT administrator. Not a consultant who comes in for a quarter and leaves a deck.
The Agent Orchestrator is the person inside the organization who sits at the intersection of what the business needs to do and what the AI stack can actually do. They run experiments, kill what doesn't work, scale what does, and — most importantly — translate between two groups who don't speak the same language: the operators who own the workflows and the engineers who build the infrastructure.
This is not a senior hire. I've seen this work best as a mid-level operator who's a fast learner and deeply trusted by the teams whose workflows they're rewiring. The authority isn't positional — it comes from being the person everyone calls when they want to try something.
What they actually do on a Tuesday
Procurement gives everyone access to a tool. The Agent Orchestrator makes that access irreversible — meaning they get a workflow so embedded that removing the AI from it would visibly break something.
In practice, that looks like three things.
First, they do the integration work nobody else has time for. Connecting a customer support queue to a summarization model, writing the prompt that makes it useful for the specific team, building the feedback loop that improves it over three weeks. This is not glamorous work. It's the work that determines whether the tool gets used or abandoned.
Second, they run a bias-to-delete process on the AI portfolio. Every six weeks: which tools actually got used, which ones got forgotten, which ones ran but generated nothing measurable. This is harder than it sounds because tool vendors are good at selling activity metrics that look like impact. The Agent Orchestrator's job is to be the skeptic in that meeting.
Third, they build the internal playbook. Not a SharePoint page nobody reads — a living set of prompts, workflows, and integration patterns that a new team member can actually pick up and use. The test is simple: if the Agent Orchestrator left tomorrow, could the teams they've worked with keep going?
Why it maps to how AI adoption actually compounds
I use a framework I call PLANT → BUILD → HARVEST to think about how AI value accumulates inside a company.
PLANT is the cheapest phase. It costs almost nothing — a changed question in a meeting that already exists, a workflow experiment that takes a week. Most companies skip it because it doesn't feel like real work. They want to get to HARVEST: the productivity gains, the EBITDA impact, the number on a slide.
HARVEST only shows up if PLANT happened first. And PLANT only happens at scale if someone owns it.
That's the Agent Orchestrator. They're the person who keeps the organization in the PLANT phase long enough to actually BUILD something durable — a capability that compounds quarter over quarter instead of stalling after the first pilot.
The companies I've watched move fastest on AI aren't the ones with the most sophisticated models or the largest AI budgets. They're the ones with someone whose job is to make the tools irreversible, team by team, workflow by workflow, until the question stops being "should we use AI here" and starts being "how did we ever do this without it."
That job description is worth writing before you buy the next tool.
If this was useful — more like it, every two weeks.
AI, building, and owning — biweekly, for operators who ship.