Kevin Johnson - KJ - leads the AI and automation practice at Pinnacle Financial Partners, a bank that closed a merger of equals in January between two institutions north of $60 billion each. He has been doing this hand-to-hand since ChatGPT launched in November 2022. Chris and Sash brought him on to answer the question every enterprise team is quietly stuck on: what actually has to be true before generative AI works in production.

His answer is that almost none of it is about AI. Pinnacle's ability to move fast on generative AI traces back five or six years, to a modernization journey that ripped workloads out of a traditional data center, rebuilt the data and networking and security layers, and reorganized teams into pods around the new work. That unglamorous foundation is the reason the AI work has anywhere to land. KJ's sharper point is that the same modernization has to happen to people, and that half gets far less attention: reinventing yourself as a contributor after two decades of doing the job one way is hard, and the organizations that win will be the ones that move fluidly through that change. As he put it, that is as human as it gets.

The back half is about discipline. Value in generative AI is genuinely hard to measure, and KJ is blunt that if you are not already measuring your KPIs well, layering AI on top makes measurement harder rather than easier. He and Sash land in the same place on tooling: automation and AI are two different tools in the toolbox, deterministic solutions never actually died, and you do not need a cannon or a bazooka for a mosquito. Chris connects that back to skills as the middle ground - a way to get an agent to follow the same process every time, even when the wording varies - and notes how often his skills end up becoming automations. The advice KJ would give his 2023 self is not technical at all: get real leadership buy-in, make continuous innovation part of the DNA, and meet the organization where it actually is.

Key takeaways:

• Do the modernization work first. Cloud, data, networking, and security foundations are what make a generative AI program possible, and skipping them is the same mistake teams made pushing BI projects ahead of data cleansing.
• Budget for team member modernization, not just technology modernization. The hard part is people reinventing how they work, and that is a change management problem rather than a tech problem.
• Fix your measurement before you add AI. If your KPIs are not instrumented well today, proving generative AI value on top of them gets harder, not easier.
• Match the tool to the problem. Deterministic automation and RPA still solve plenty of things more cheaply and more reliably than a frontier model, and orchestration plus observability matter more than horsepower.
• Use skills to put determinism back into agent work. Define the pattern once so the process runs the same way every time, and expect the good ones to graduate into automations.
• Ship tools that make someone's day measurably easier and adoption becomes organic. Finding an answer in ten seconds instead of two and a half minutes is the kind of win a frontline team feels immediately.

Watch the video version on YouTube · Full show notes, transcript + takeaways