Beyond Code: Why AI Demands an AI Digital Asset System (ADAS) How change and project leaders must reinvent delivery and governance for the age of AI agents For decades, project and change leaders have rallied teams around a familiar center of gravity: “The code is the product. Documentation supports it.” In the world of AI agents, that assumption quietly breaks. An AI agent doesn’t just run on code. It runs on digital assets : prompts, policies, data scopes, tools, and guardr

For decades, “computing” meant something very specific: we told machines exactly what to do, step by step, and they did it—fast, consistently, and without ever coloring outside the lines. Today, with AI, we’re crossing into something very different: systems that don’t just execute instructions, but interpret situations, choose among options, and act with a degree of initiative. That in-between space is what many people now call semi-autonomous AI. It’s not a robot overlord

From Automation to Agency: Why 2026 Will Break Your Project Playbook Perspective from a CIO with 40+ years in the chair In more than four decades in technology leadership, I’ve ridden every major wave: mainframes, client–server, ERP, internet, cloud, mobile, analytics, and now AI. Each one forced us to adjust how we run projects. Agentic AI — systems of semi-autonomous agents that can perceive, decide, and act — is different. Beginning in 2026, many of your “digital transfor

No More Secret Rulebooks: What AI Will Expose About Your Culture Why serious AI projects force change leaders to confront the gap between stated values and real behavior—and how to turn that into an advantage. If you lead change, you’ve always had to navigate two versions of your organization: The one in the slide decks, values statements, and town halls. And the one people quietly describe over coffee: “Here’s how things really work.” Until now, those two versions could coe

Why AI Is More Like Having a Baby Than Building a House Missteps and hallucinations are natural growing pains, not a verdict on the technology. Most of the public conversation about AI still sounds like a construction meeting. We talk about “building a model,” “deploying a system,” “rolling out a solution.” We expect that, after some testing and debugging, the system will do what it’s supposed to do, just like a new building is expected to stand straight, keep the rain out, a