Engineering Leadership Beyond the Hype Cycle: Practical AI Implementation vs. Executive Theater

Engineering Leadership Beyond the Hype Cycle

Practical AI Implementation vs. Executive Expectations

AI has quickly moved from exploration to expectation. In many orgs, the question is no longer “should we use it?”, but “why aren’t we seeing more impact?”

For engineering leaders, the tension is real: early results look promising, but much of the progress is local, hard to measure, and difficult to defend at the system level.

So what actually holds up and what only looks like progress?

In this roundtable, we’ll compare how leaders are making that call in practice: what they’re choosing to scale, what they’re walking away from, and how they’re maintaining credibility while doing it.

We’ll focus on:

1. How to tell if an AI gain is real or just shifting work elsewhere
2. What leaders are stopping, not just starting
3. Where early wins failed to translate into system-level impact
4. How to justify (or push back on) AI investments with incomplete data

If you’re currently deciding where to double down or where to pull back, this conversation will likely sharpen that call.

Hosted by

Ken Pickering, CTO @ Scripta Insights

Ken is CTO at Scripta Insights, where he applies AI and large-scale data systems to bring transparency to pharmacy pricing—one of the most complex and opaque areas in U.S. healthcare.

With over 20 years of experience leading engineering through platform rebuilds, hypergrowth, and market shifts, Ken has held leadership roles at Hopper, Starburst, Going, and Rue La La. His work spans predictive pricing, distributed data systems (Trino), experimentation platforms, and real-time personalization.

He’s known for a pragmatic approach to AI adoption—focusing on fast but confident delivery, systemic improvement, and applying technology where it drives real-world impact.