How do you boost developer velocity in a highly regulated industry—without sacrificing safety or customer trust? Charles Williams, Senior Vice President and Software Engineering Director at Truist (formed from the BB&T and SunTrust merger), shares how his team elevates developer experience to ship faster and more reliably. Charles breaks down shifting quality “left” with automation, measuring success with both DORA metrics and developer sentiment, and why human-in-the-loop is non-negotiable for AI in finance. He details Truist’s governance model—steering committees, enterprise architecture, and clear guardrails—to avoid tool sprawl while building a purpose-built AI ecosystem: Microsoft Copilot for productivity, GitLab’s AI-enabled DevSecOps platform for engineering, and separate consumer-facing capabilities. Expect practical insights on starting with low-risk, high-yield use cases (unit tests, docs, security triage), tracking AI utilization, and upskilling teams in prompt engineering so developers can “manage” AI agents effectively. Charles also explores the path to personalized experiences balanced with privacy, why branches should be enhanced—not reduced—by AI, and the cultural skills leaders need now: empathy, neurodiversity awareness, and change management. He closes with where AI is driving ROI first—developer onboarding and pipeline productivity—with code quality gains following close behind.

Timestamps

[00:02] – Truist overview and Charles’s mandate: improving developer experience at scale

[00:56] – AI as a strategic priority; shifting quality left with automation to remove bottlenecks

[02:19] – Measuring success: DORA metrics plus sentiment—eliminating toil to drive happiness

[04:33] – Human-in-the-loop AI for high-stakes finance; customer and internal use cases

[07:25] – How Truist evaluates tools: personas, pain points, and starting with tests, docs, security

[08:35] – The stack: Microsoft Copilot, GitLab’s AI gateway approach, and tracking utilization

[10:36] – New skills and culture: prompt engineering, “managing” AI agents, and strong governance

[20:45] – What’s next: personalization vs privacy, fintech agility + bank stability, and where AI pays off now

Takeaways

- Shift quality left with automated checks so developers catch issues early without human gatekeeping.

- Measure DORA metrics and developer sentiment; remove mundane toil to increase speed and satisfaction.

- Keep humans in the loop for AI-assisted coding and customer answers—trust but verify in regulated contexts.

- Build an AI ecosystem with clear purposes (productivity, engineering, consumer) and a steering committee to avoid duplication.

- Start with low-risk, high-yield AI use cases—unit tests, documentation, and security triage—to build confidence and momentum.

- Upskill teams in prompt engineering and AI oversight so developers can effectively direct and review AI “agents.”