023 AI for Operators

Hi there, 

Welcome back to AI for Operators. Here’s what we’ve got for you this week:

• The Operator: Julian Hodari (Co-Founder at Fluency Labs - listen here)

• The Essay: The Rise of the AI Automation Engineer?

• The Links: 10 links, including an inside look at Cursor, ROI on AI is positive, actually, and running your business analytics stack inside Cursor

Friend of the community (and co-founder of Clarinet) Diane Sadowski-Joseph posted last week that she thinks that “Internal AI Automation Engineer” will be THE role of 2026. I’m not so sure.

First of all, what team will this new role sit on? Taking a quick tour through the open “AI Automation Engineer” roles on LinkedIn showed a mix of pure engineering roles, quasi-bizops roles, and some job descriptions that were just confusing. The title may be used with more frequency, but if there’s lack of agreement on the definition, that will limit its usefulness.

Second, what skills will these AI Automation Engineers need? Several of the roles asked for 5+ years of AI or ML-focused engineering experience, while others simply asked for experience with Zapier or a similar no code stack.

But a lack of a shared understanding of the role isn’t the only reason it faces an uphill climb to ubiquity.

While some level of understanding of the technology behind these systems is helpful, AI systems problems are fundamentally human problems, unless the company plans on an org-wide purge of employees who don’t fall in line. The skills that will be most useful, I think, are organizational context, a keen eye for processes and how to make them more efficient, storytelling ability and the communications skills necessary to win trust from a skeptical workforce, and a commercial instinct to guide them to the highest impact projects.

That sounds a lot like what most PMs, Chiefs of Staff, and Biz Ops teams specialize in.

Don’t get me wrong, it will absolutely be helpful to most organizations to have technical experts work alongside, for example, a Chief of Staff to launch new AI initiatives. And some of the generalists in these roles won’t be sufficiently technical (or interested in learning) to grok how these systems can be strung together to maximize ROI while solving for real data quality, governance, and security issues.

But if the success or failure of AI transformation efforts is mostly about people, then organizations should optimize for that when creating AI Automation roles.

In any case, this will be a temporary title - everyone will have to be an AI engineer to some extent (the same way that if you’re a regular ol’ software engineer at a decent company, you’re getting left behind if you’re not using Cursor or Claude Code at least some of the time).

So while Diane’s right that we’re almost certain to see a rise in the number of AI Automation Engineer roles posted, the most successful ones are likely to be internal hires or super-talented generalists who are technical enough to be effective.