Why AdTech Needed a Chief AI Officer

 By Ken Rona, Chief AI Officer, JWX

Sixteen months ago, JWX created a role we can't find precedent for anywhere else in ad tech: Chief AI Officer. This is the first in a five-part series where our CAIO, Ken Rona, walks through what that job has actually looked like: what worked, what didn't, and where the transformation stands today.

If you're a media or ad tech leader wondering whether “AI transformation" is a real operating discipline , this series is the concrete version: named tools, real timelines, and the mistakes along the way.

I think I am the first Chief AI Officer in AdTech. Other people now have the title, but I am 16 months in and I have had some time in-seat. I thought this would be a good time to explain our approach, experience, and what we think is coming down the pike.

Now to our story…

When John Nardone started as CEO of JWX in March of 2025, he came in with a thesis that AI was going to transform how software companies would operate. Specifically, it was clear that AI was going to supercharge software development. The organization was using AI in an ad hoc way, with just a few individuals effectively using AI for software development. The company was feeling its way through adoption; AI adoption was not managed and no one was responsible for a successful  “AI Transformation."

John's thought was that someone had to be directly responsible for the company's AI transformation. He knew that I was actively using AI in my consulting practice and was an expert in digital advertising. Also, I was consulting for JWX and we spoke fairly frequently about AI and its impact. On a couple of occasions, we would chat about some product idea on a Friday, and then I showed a working demo on Monday. I was the most sophisticated user of AI that he knew.

And so he asked me if I would take it on and lead the AI transformation effort. Creating internal tools, training, development methodology, and rapid prototyping were all in the remit. But I would not be responsible for the use of AI in our products; that sits with product management Though we might help with some aspect of using AI in our products, deciding where AI touches our customers is in the domain of the individual product managers.

At the time, there were no real playbooks for AI transformation. But I had led other digital transformation initiatives and thought it was a good role for me. Best job I have ever had.

So what was the original mandate? We came up with three priorities, and in order:

1. Development speed: We wanted to greatly accelerate the pace of software development. It seemed like AI could give us leverage there, but software developers were (and still are) still learning how to use AI as a coding tool.
2. Rapid prototyping: We wanted to be able to have a rapid prototyping capability where we could bring new-to-world products into market as minimally viable products, very quickly. As part of increasing our development speed and rapid prototyping, we took on building software development tools for the organization.
3. Business workflows: We wanted to be able to help the business staff, what I call the SG&A, to re-engineer their workflows.

Those three priorities are still the operating model today, and each of the next four posts takes one artifact from that work and shows exactly how it played out: the internal tool family that pushed engineering output up 3x, the assistant that escaped its lane and ended up running Slack-based feature development company-wide, the prototype built in three days that became a real product, and the training program that succeeded where a straightforward tools rollout failed.

Key Takeaway: If you're evaluating whether a vendor's “AI-first”  claim is real, this is the kind of detail worth asking for.

Next up:  “The Bot Family" blog is coming up on the 29th of September