As the engineering leader behind Xceptor's AI-powered Product Development Lifecycle (PDLC), Mike shares how his team moved beyond experimentation to build an engineering organization that delivers software faster while maintaining the governance, quality, and accountability demanded by financial services. Their conversation explores why AI transformation isn't about deploying more tools or building more agents—it's about solving real business problems, measuring outcomes instead of activity, and helping teams evolve alongside rapidly changing technology.
Much of the AI conversation today revolves around developer productivity metrics—more pull requests, more code generated, more AI agents deployed.
Mike argues that these metrics miss the point.
When Xceptor began introducing AI into its software delivery process, the team didn't define success by how much AI-generated code they produced. Instead, they focused on a single business outcome: reducing the time it took to deliver working software to customers. Every experiment, every workflow, and every new capability was measured against that goal.
Like many engineering organizations, Xceptor initially explored AI inside its product before expanding its use into software development.
Rather than chasing the latest trend or trying to automate everything at once, the team deliberately stepped back and identified one practical problem they knew AI could solve. Starting small allowed them to learn quickly, prove value early, and build confidence before expanding AI across the broader development lifecycle.
Working in financial services means every decision must be traceable, auditable, and accountable.
That doesn't change when AI enters the picture.
Mike explains why human oversight remains essential throughout the development lifecycle and how governance, quality standards, and measurable controls have become even more critical as AI accelerates software delivery. Faster development doesn't remove responsibility—it increases the need for thoughtful engineering practices.
As AI automates more of the repetitive work involved in software development, engineering roles begin to evolve.
Rather than spending time on implementation details, developers can focus on architecture, system design, customer challenges, and product thinking. Mike shares his vision of future engineering teams built around smaller, multidisciplinary cells where people work alongside AI to solve increasingly complex problems.
One of the most candid moments in the conversation comes when Mike discusses the human side of AI adoption.
Rapid technological change creates uncertainty for every engineering organization. As roles evolve and new capabilities emerge, many people naturally begin questioning what the future holds for their careers.
Mike believes leaders have a responsibility to acknowledge those concerns openly, bring people along on the journey, and create an environment where learning becomes part of the culture rather than something to fear.
Asked what advice he would give organizations just beginning their AI journey, Mike keeps it remarkably simple.
Start with a small, focused team.
Choose one problem with clear business value.
Set ambitious but measurable goals.
Most importantly, measure outcomes. not activity, and allow each experiment to inform the next.
For Xceptor, that iterative approach has helped transform AI from an emerging technology into a practical capability that's reshaping the way engineering teams build software.
Stay tuned for more conversations with technology leaders exploring how AI is changing software engineering, product development, and enterprise technology on CTO2CTO.