Kiplot ads

AI is making it much easier to produce the reports, summaries and first-pass analysis portfolio teams rely on. But someone still has to decide whether the right question is being asked and how the evidence should shape the decision. The Strategic PMO’s Guide to Making Decisions When AI Does the Analysis explains the capabilities PMO leaders need to develop as analysis gets easier but accountability does not.

Agentic AI can already take on much of the administrative work performed by the PMO. The implication is not a smaller role for the PMO. It is a different one, with greater responsibility for portfolio truth, governance and investment decisions. The Strategic PMO’s Guide to Agentic AI examines how the PMO’s role changes as agentic AI takes on more administrative work.

Beneath the agentic fanfare, talk of self-driving portfolios and the apparently imminent extinction of the project manager, several more consequential shifts were taking place in how portfolios were actually being run. Across enterprise organizations, attention was moving back towards reliable data, clear ownership, consistent financial and capacity information, and the controls needed to make better investment decisions. AI did not make these fundamentals less important. It made their absence harder to ignore. Kiplot’s latest Market View examines the trends that defined the first half of 2026, including renewed scrutiny of resource cost, the return of planning discipline, Jira gaining ground in work coordination, and enterprise AI moving beyond embedded assistants. Read the full review: https://lnkd.in/emXJtHaQ

Who should be accountable for delivering an initiative? It’s a simple question, but one that many organizations struggle to answer. A common assumption is that Transformation should own delivery - after all, they define the strategy, align resources, and troubleshoot execution. But this is a critical mistake. Transformation is essential, but it should never be accountable for execution. When organizations misplace accountability within Transformation – or within delivery functions like IT – initiatives are completed, but the benefits aren’t realized. We explain more in this article.

When the portfolio is managed through disconnected views, the same questions keep coming back.

The promise: AI-generated portfolio intelligence. Real-time. Consistent. Strategic. The reality: Confident-sounding nonsense, faster. Portfolio data goes in. Plausible-sounding output comes back. It references the right initiatives. It uses the right language. It reads like intelligence. But that is the problem. The output is not obviously wrong. And because it looks authoritative, it is harder to challenge. The issue is not that AI cannot process portfolio data. It is that portfolio data doesn't speak for itself. A status, a priority score, a resource forecast - none of it means much without the logic behind it. This is a solvable problem. Just not a technology one. The organizations getting real value from AI didn't start with the technology. They started by building a layer of codified business logic, consistent definitions, and institutional knowledge between raw data and interpretation. A context layer. This article explains what that layer consists of and why you can't AI your way out of a data problem.