Mcd79 has always been about bringing two things together: what Microsoft technology can offer and the challenges people actually have. The technology has changed dramatically since I started writing. The need to make it useful has not.
This new site gives that work a clearer home. It preserves the original modern workplace archive, creates a dedicated space for current writing about AI, brings my conference sessions together, and provides a place to develop the AI Productivity Framework in public.
Why rebuild now?
Copilot and agents are changing quickly. New models, new interfaces, and new capabilities arrive before most organisations have made full use of the previous wave. It is easy to respond by chasing announcements. It is harder—and far more valuable—to understand where a tool fits into real work.
That is the gap I want Mcd79 to address. A feature matters when it helps someone complete a meaningful task, make a better decision, reduce friction, or create something that would otherwise have remained out of reach. Adoption matters when that improvement survives beyond the first demonstration.
Four connected bodies of work
The new site separates content so its purpose stays clear:
- Current writing explores Copilot, agents, and productive AI practice.
- The legacy archive preserves lessons from Microsoft 365, Viva, Syntex, search, and the community around them.
- Conference sessions collect talks and demonstrations that work better when shown than described.
- The AI Productivity Framework connects tool evaluation with sustained behaviour change at personal, team, department, and organisation levels.
Practical before promotional
I am interested in the point after the impressive demo. What happens when someone opens Copilot on a busy Monday morning? How do they decide whether an agent is the right answer? What support helps a useful experiment become a dependable habit? How does a team learn together without forcing everyone into the same workflow?
Those questions require more than feature summaries. They require examples, honest limitations, patterns that can be tested, and a willingness to separate activity from value. That will be the editorial standard for the new writing here.
Built in public
The framework will evolve as the evidence and practice develop. I will connect new ideas to older lessons rather than pretending AI adoption began with the latest product name. The legacy archive is important for exactly that reason: today’s questions about change, governance, purpose, and employee experience have deep roots.
Welcome to the new Mcd79. Start with the problem in front of you, choose the smallest useful experiment, and build from there.