The AI Enablement Engineer: The Highest-Leverage Role Nobody Is Hiring For Yet
Your company bought every AI tool on the market and productivity didn't move. The problem isn't the tools. It's that nobody built the scaffolding. Meet the role that fixes it.
A CTO I know spent €180,000 last year on AI tools. Cursor for everyone. Claude Code seats. A couple of enterprise Copilot licenses. An internal RAG system built by a consultancy. A Notion AI subscription for the product team.
Six months later, he ran the numbers. Velocity: flat. Lead time: slightly worse. Developer satisfaction: mixed. Two senior engineers were using the tools brilliantly. Everyone else was copy-pasting prompts from Twitter threads and getting mediocre output.
He called me, frustrated: “I bought Ferraris and my team is still walking.”
He didn’t have a tool problem. He had an enablement problem. And the role he needed to hire didn’t exist on his org chart, because two years ago it barely existed anywhere.
The Role Quietly Eating the AI Hiring Market
Open LinkedIn today and search for “AI Enablement Engineer.” Cognition is hiring. Mercury is hiring. Nordstrom is hiring. InterSystems. Fortive. ModelOp. Preset called it “the highest-leverage role in tech.”
Twelve months ago this title barely existed. Now it’s one of the fastest-growing job categories in engineering, and most of the companies that need one don’t know it yet.
The confusion is in the name. People hear “AI Engineer” and assume it’s the person who builds AI products — the one training models, wiring RAG pipelines, fine-tuning LLMs for customer features. That’s a different role.
The AI Enablement Engineer does not build AI for your customers. They build AI leverage for your own team.
The DevOps Analogy That Makes It Click
Ten years ago, DevOps Engineers showed up to solve a specific problem: developers were great at writing code and terrible at deploying it. Infrastructure was a dark art. Every team reinvented CI/CD badly. Nobody wanted to touch production.
DevOps engineers didn’t write the product. They built the scaffolding so everyone else could ship safely at speed. Self-service infrastructure. Paved roads. Guardrails. The rest of the company suddenly became 3x more productive without hiring a single extra developer.
AI Enablement is the same story, one layer up.
If DevOps was about making infrastructure self-service, AI Enablement is about making intelligence self-service.
Most developers today are the equivalent of someone using a Formula 1 engine to drive to the supermarket. The capability is sitting right there. They don’t know how to access the full power safely. So they use 5% of it and complain the tool is overhated.
The AI Enablement Engineer is the person who builds the track.
What They Actually Do on a Tuesday
Forget the LinkedIn fluff. Here’s what this role looks like in practice:
Monday morning. The platform team is stuck. They need to query a legacy Oracle database through a custom agent but the MCP server doesn’t exist. The AI Enablement Engineer spends two hours writing one. Now the whole team can query Oracle by asking a question in plain English.
Monday afternoon. Sits with the payments squad. Their agents keep hallucinating field names because the internal API documentation is a mess. Writes a CLAUDE.md that anchors the agent’s context to the actual schema. Hallucinations drop to zero. The squad ships three features this week instead of one.
Tuesday. Builds an internal eval harness so product managers can test prompt changes before shipping them. No more “it felt better on my machine.”
Wednesday. Runs a workshop with the support team. Teaches them how to build their own small-scope agents for ticket triage. By Friday, support has built three agents nobody engineering had to write.
Thursday. Reviews the token budget dashboard. Notices one squad is burning 40% of the monthly spend on a workflow that could be replaced with a cached template. Saves €4,000/month.
Friday. Writes a retrospective on what worked and what didn’t. Updates the internal “paved road” docs. Goes home.
One person. Five teams multiplied. Zero customer-facing features shipped.
And this is the whole point.
The Leverage Math That Breaks Traditional Hiring
CTOs love hiring senior engineers because the math feels obvious: one great engineer ships more than three average ones. So the instinct is to keep adding great engineers.
AI Enablement breaks that equation.
One AI Engineer working 10x faster with AI tools still only ships what one person can ship. A great engineer with Claude Code can output, let’s say, 3 or 4 people’s worth of work on a good week. Impressive. Real. Bounded.
One AI Enablement Engineer who makes 50 people 2x more productive generates the output of 50 additional engineers. Without the headcount. Without the onboarding. Without the communication overhead.
This isn’t a clever slide. It’s the reason companies that figure this out early will destroy the ones that don’t.
The trap is that most leaders can’t see enablement work on a velocity chart. The AI Enablement Engineer doesn’t close tickets. They don’t ship features. Their pull requests are small and unsexy — an MCP config, a prompt template, a governance doc. If you measure them like a regular developer, you’ll fire them within three months.
And then you’ll wonder why your team’s productivity collapsed.
The Role That Sits Next to Founder Mode
Readers of this blog know the Founder Mode argument: traditional hierarchical management is broken, product managers as middlemen are becoming obsolete, the best companies are flattening and pushing product obsession down to the people actually building things.
AI Enablement fits that thesis like a missing puzzle piece.
This is not a “head of AI” role. It’s not a VP with a strategy deck. It’s a hands-on engineer who walks into your squad, pairs with the team that’s stuck, and leaves behind concrete capability. Skip-level by nature. Direct-engagement by design. Zero PowerPoint.
The best AI Enablement Engineers I’ve seen share a specific personality profile:
- Teachers at heart. They genuinely enjoy watching a junior developer’s face when the agent suddenly does exactly what they wanted.
- Allergic to gatekeeping. Their instinct is to open access, not control it. Governance exists to protect, not to slow down.
- Platform thinkers, product builders. They treat their own team as the customer and iterate on the internal tooling the same way a product engineer iterates on a feature.
- Comfortable being invisible. The win isn’t their code. It’s the team they enabled shipping something great.
If any of those traits describe someone on your team, you may already have your first AI Enablement Engineer. You just haven’t given them the title or the budget yet.
What They’re Not
Three quick clarifications, because the title causes confusion:
They’re not the AI Ethics Committee. They build things. They don’t write 40-page policy documents.
They’re not the “AI Champion” who posts articles on Slack. That’s advocacy. This is engineering.
They’re not a trainer. Training is part of the job, but if all they do is run workshops, they’re a consultant. The core output is code, infrastructure, and tooling.
The cleanest way to test the role: if you removed them tomorrow, would the AI capability of the rest of the org degrade? If yes, you have an AI Enablement Engineer. If no, you have a very expensive teacher.
How to Hire One
You almost certainly can’t hire this role externally yet. The market is thin, the salaries are spiking, and the good ones are all already employed by Cognition or Anthropic.
The realistic path is to promote internally.
Look for the developer on your team who:
- Already builds small internal tools nobody asked for — a Slack bot, a custom script, a quick eval harness.
- Is the person everyone DMs when their Cursor isn’t working.
- Has shipped at least one agent or MCP server for their own productivity.
- Gets visibly energized when a teammate finally “gets it.”
Give them 50% of their time back. Give them an explicit mandate. Measure them on team-level metrics, not individual ones. Protect them from the velocity dashboard.
Six months later, you’ll wonder how you ever operated without the role.
The Founder Mode Parallel
Brian Chesky didn’t fix Airbnb by hiring more managers. He fixed it by getting closer to the product and the people building it. Steve Jobs didn’t obsess over the Apple organizational chart. He obsessed over the team’s ability to ship things customers loved.
AI Enablement Engineers are a product-obsession move, disguised as a platform move. They exist to shorten the distance between your team’s potential and your team’s actual output. They remove the friction between “we bought the tool” and “the tool is compounding our leverage every week.”
The companies that hire for this role in 2026 will look back in 2028 and realize it was the single best organizational bet they made this decade.
The companies that don’t will keep buying Ferraris for people who are still walking.
Your team has someone who could be your AI Enablement Engineer. The question is whether you’ll give them the title before your competitor does.
John Macias
Author of The Broken Telephone