We spent six weeks arguing about which of our existing hires should "own" AI-agent readiness, until I realized we were solving a staffing problem with an org chart when it was actually a hiring problem in disguise. Nobody on the team had ever been tested for the skill, because until this year, the skill didn't exist to test for.
This isn't a title problem, it's a skill problem
Every founder I've talked to about this has floated a different name for who should own it: the growth marketer, the GTM engineer, the RevOps hire, or just the founder for now. That debate misses the actual question. Titles don't absorb new skills automatically, and none of those three roles were hired against this skill last year because it didn't exist as a hiring criterion.
At most early-stage companies there are really only three candidates for this: your growth or demand-gen marketer, your RevOps or GTM engineer, or you. Picking based on who has the most free time this quarter is how you end up with a person who owns the responsibility but can't actually do the work.
The four skills the role actually requires
Before you can interview for this, you need to know what you're testing for. It comes down to four things: reading server logs or analytics well enough to spot non-human traffic patterns, enough structured-data literacy to have an intelligent conversation with an engineer about schema markup, the judgment to prioritize a fix when there's no existing playbook to copy, and comfort operating without a dashboard that tells you if it's working. That last one trips up more people than the other three combined.
The interview questions that actually test for it
These are the six questions I now run on any candidate, internal or external, before handing them this scope:
- "Walk me through how you'd find out, this week, whether an AI purchasing agent has ever visited our pricing page." This tests whether they have an actual method or just an opinion about the topic.
- "What's the difference between why a page ranks in Google and why an AI agent would cite or shortlist it?" A candidate who answers with "better SEO" doesn't understand that these are different systems with different requirements.
- "If our pricing page loads fine for a human but an agent still can't parse the price, what's the first thing you'd check?" This is the structured-data question. You're listening for JSON-LD, Schema.org, or at minimum an instinct to check what the page looks like without CSS and JavaScript rendered.
- "You have zero budget and one engineering hour this month. What's the single highest-leverage fix you'd ask for?" There's no canned answer to memorize here yet, so this tells you whether they can prioritize under real constraints instead of reciting a listicle.
- "How would you know if this work is paying off, given there's no 'AI agent conversion' report in your analytics tool yet?" This is the one that separates people who need a finished dashboard from people who can build a proxy metric and defend it.
- "Tell me about a time you had to get good at something that didn't exist as a job a year earlier." This is behavioral, and it's the best predictor of whether they'll still be useful on this the next time the underlying technology shifts, which it will.
Red flags in the answers
Watch for three patterns. First, a candidate who treats the whole problem as a content or SEO exercise and never mentions the technical layer at all, that's a half-skill. Second, someone who can talk about agent traffic in the abstract but can't name one concrete way to check for it this week, that's theory without a method. Third, and the one I'd weigh heaviest, a candidate who wants to wait until the tooling matures before doing anything. This space is moving fast enough that waiting for a mature dashboard means you're building for last quarter's version of the problem.
Who should actually own it, by stage
Pre-seed and seed: this should sit with the founder or an existing growth hire, with light engineering support pulled in as needed. Don't create a standalone role yet, the volume doesn't justify it and the work changes too fast for a narrow job description. Series A and beyond with real enterprise pipeline: fold it explicitly into your GTM engineer or RevOps scope rather than leaving it as an unassigned side project nobody prioritizes when the roadmap gets tight.
If nobody on your current team clears at least four of the six questions above, that's your actual answer. It's not a delegation problem you can solve by reassigning a title, it's a skill gap you need to hire for directly.
Run this on your current team first
Before your next hiring cycle, run these six questions on the people already on your team, not just new candidates. You might find you already have the right person and just never asked them the right questions. Or you'll know exactly what to screen for the next time you're hiring, instead of hoping a title absorbs a skill nobody actually checked for.
Frequently asked questions
Do we need a dedicated AI-agent-readiness hire?
Not usually before Series A. It should be a slice of an existing growth, GTM, or RevOps role until agent-driven traffic is a measurable share of your pipeline, not a headcount line item on its own.
Should marketing or engineering own this?
Neither exclusively. The audit and interpretation work is a growth skill, but the fixes, schema markup, llms.txt, crawl rules, need an engineer's cooperation. Whoever owns it has to be able to talk credibly to both sides.
What if nobody on my team scores well on these questions?
That's useful information, not a failure. It means this is a skill to hire for externally, rather than a responsibility you can assume already lives inside an existing title.