What new skills do you believe project and PMO professionals need to develop to remain relevant in an AI-enabled delivery environment?

Appreciate the clarification! And I agree that Responsible AI is a built-in component of AI initiatives and inherently multi-disciplinary. From your experience, what are the most effective ways PMs or PMOs can ensure these Responsible AI processes and stakeholders are properly integrated into delivery governance?

Thank you for the perspective. You’re absolutely right that AI has been present in project environments for decades, and that generative AI is only one subset of the broader AI landscape. I appreciate the callout on Responsible AI, as governance, ethics, and risk management are critical capabilities moving forward.
Building on your point, I’d be interested in your view on which specific Responsible AI competencies PM and PMO professionals should prioritize (e.g., governance frameworks, data privacy, model risk management, human-in-the-loop controls). Your experience would add valuable depth to this discussion.

If I step back from frameworks and look at what’s happening in practice, the most important new skill for PMO and project professionals in an AI-enabled environment is operational literacy with AI-augmented work.

Not “AI strategy.” Not prompt engineering. But understanding how work actually changes when parts of planning, analysis, forecasting, and reporting are no longer purely human.

That shows up in a few very practical ways:

• Knowing where AI output is reliable — and where it isn’t. PMOs need to understand confidence ranges, data bias, and model limitations well enough to prevent false certainty from entering plans, forecasts, and executive conversations.
• Integrating AI into existing delivery workflows without breaking accountability. When estimates, risk signals, or status narratives are AI-assisted, teams still need clarity on who owns the decision, who validates it, and who is accountable when it’s wrong.
• Translating AI outputs into human-actionable guidance. AI can surface patterns, probabilities, and scenarios — but PMOs still have to turn those into priorities, sequencing decisions, and trade-offs that teams can execute against.
• Managing adoption without over-rotation. Many organizations are either over-automating prematurely or resisting change entirely. PMOs increasingly act as stabilizers — helping teams experiment safely without disrupting delivery.

From that angle, the skill gap isn’t philosophical — it’s practical.
The PMOs that struggle aren’t the ones who “don’t use AI,” but the ones who can’t explain how AI fits into day-to-day delivery without increasing confusion or risk.

That, more than any specific tool, is what will separate relevant PMOs from ornamental ones in the next few years.

What I've noticed in this new AI-age is that organisations are expecting their employees to be more productive and effective. This is important beacuse as project managers, we can leverage AI to help make us a little bit more productive and so I think it's important for people to explore how they can leverage AI for these tasks.

Some use cases that I've started doing is automating note taking, sending note summaries, etc. which is really easy to do now especially since most video conferencing tools will record and transcribe for you.

In short: use GenAI for time consuming admin tasks (those that require less brain cells) and leverage the unique expertise, thinking capacity and INTUITION that machines can´t replace (yet).

Just understanding what Responsible AI is. No more than that.