Project delivery in a constantly changing World
Communities from Sydney to Virginia reject data centres, not from hostility to AI, but over water, energy bills, pollution and noise. From a data leader’s view, this piece argues they must earn a social licence, proposes five tests, draws lessons for the UK and challenges data teams to cut demand.
The rapid discovery of decade-old vulnerabilities by AI agents has transformed technical debt from a manageable financial metaphor into an immediate security crisis. As software supply chain risks explode, organizations must abandon the illusion of indefinite deferral and adopt enterprise-wide visibility, rehearsed outage plans, and human-centric engineering judgment to survive the blast radius.
AI agents are now table stakes. The bottleneck has moved to rollout design, and that makes it a delivery problem, not a technology problem.
Robotics in 2026 is no longer one story. Warehouses, battlefields and disaster zones are all turning into test beds at once.
If your AI workflow still starts with pasting a meeting transcript into a browser tab, it’s time for an upgrade. Discover how project managers are shifting to local, open-weight models like Llama 4 and Qwen 3 in 2026 to get powerful AI assistance while keeping sensitive client data completely private.
Skip the roadmap. Here are the agentic workflows a working PM can actually ship this week — and the ones to leave alone for now.
As AI labs struggle to contain their own autonomous agents, project managers cannot rely on vendor safety reports or regulatory optimism. To safely deploy AI, organizations must take back control. Every autonomous agent on your roadmap requires an independent monitoring layer, least-privilege access, and a client-owned infrastructure kill-switch.
Executives rotate through roles every two to five years; the projects they inherit or start tend to outlast their tenure. This piece asks what it means for a PM to act as a fiduciary carrying a programme to completion— serving the outcome rather than whoever’s currently sponsoring it — and what habits make that honesty survivable, not just admirable.
As AI control becomes more capable of managing complex systems its tempting to remove human inefficiency by automation. High-stakes industries prove “deliberate inefficiency” isn’t waste—it’s a vital safety mechanism. This post discusses why relying solely on automation creates dangerous blind spots, and how keeping “hands on the controls” prevents catastrophic failures in complex projects.
In something akin to a Jedi mind trick frontier AI models have managed to escape the constraints their human masters thought were in place. What actually happened and what does this mean?
Drawing on Uri Levine’s “Fall in Love with the Problem, Not the Solution,” this piece explores why complex delivery demands failing fast, holding direction, and trusting the process over the deadline.
Artificial Intelligence. AI Ethics Project Management Articles Internet of Things (IoT) GDPR Security