Enterprise AI & Technology Executive · Raleigh-Durham, NC

Ken Dickinson

I help organizations turn AI into durable business capability, moving emerging technology out of experimentation and into governed, measurable, production reality.

Vice President of Engineering, Jewelers Mutual · Constellation Research AI 150

Ken Dickinson, wearing a charcoal blazer and open-collar white shirt, photographed against a warm gray backdrop.

Throughout my career I’ve been drawn to periods of transformation: modernizing engineering organizations, rebuilding technology platforms, and helping executive teams navigate disruptive technology. The work I care most about isn’t implementing any particular tool. It’s building the operating models, engineering organizations, and executive alignment that let a new capability become a durable one. Most recently, that has meant building an enterprise AI function from the ground up for a century-old regulated insurer: defining strategy and governance, presenting directly to the board, and bringing production agentic systems into an environment where security, trust, and reliability are non-negotiable.

Before Jewelers Mutual, I led engineering, cloud infrastructure, and quality organizations at Pelago, SingleStore, and SAS, scaling global teams, modernizing platforms across AWS, GCP, and Azure, and carrying products from biannual releases to continuous delivery. Different industries, different technologies, but the same thread runs through all of it: technology becomes a competitive advantage when an organization changes how it works, not just what it buys. I believe AI will prove as foundational to business as the internet or the cloud, and that the lasting advantage will belong to organizations that learn faster, adapt faster, and redesign how people and technology work together.

A few outcomes I’m proud of. Each one is the result of strategy, governance, and engineering working as a single system.

  • 7 → 356 Enterprise AI adoption

    Scaled enterprise AI access from 7 to 356 users across 11 business units, moving a regulated insurer from ad hoc experimentation to governed, enterprise-wide adoption.

  • 334% Engineering velocity

    Improved engineering delivery velocity by 334% through platform modernization, AI-assisted development, and operating-model transformation.

  • 47% Cloud economics

    Reduced cloud operating costs by 47% through architectural modernization and operational optimization.

  • ~200 hrs Agentic automation, monthly

    Designed a production agentic workflow that eliminated approximately 200 hours of manual work each month, built with responsible-AI governance from day one.

  • Enterprise AI strategy

    Turning executive intent into a working operating model: build, buy, and hybrid decisions, responsible-AI governance, data classification, and adoption paths that let business teams innovate without waiting on a central bottleneck.

  • Engineering transformation

    Building engineering organizations that deliver: squad-based operating models, DevSecOps and observability standards, AI-assisted development practices, and the cultural change that makes velocity sustainable rather than episodic.

  • Agentic systems

    Designing and shipping production AI agents in regulated environments, where explainability, evaluation, and ethical controls are engineering requirements, not afterthoughts.

  • Executive & board alignment

    Translating emerging technology into the language boards make decisions in: investment priorities, risk posture, governance, and honest progress reporting.

  • Named to Constellation Research’s AI 150 (2026 to 2027)
  • Speaker, Constellation AI Forum 2026 and AllDayDevOps
  • U.S. patent: System and Method for Dynamically Generated Reports (US20170142548A1)
  • Duke University, Fuqua School of Business: Chief AI Officer executive program (in progress, 2026)
  • University of New Haven: Computer Science

The conversations I enjoy most are with leaders working through the same questions I work on every day: how to turn AI from a promising experiment into a real organizational capability. If that’s the conversation you’re having, I’d welcome hearing from you.