Last week, PMI released Leading AI Transformation: Organizational Strategies for Project Professionals, and if you’re a project leader, it’s worth your attention. Not because it’s packed with futuristic jargon or vague hype — but because it speaks directly to what we are grappling with right now: how to navigate real, messy, fast-paced change.
Let’s face it — AI isn’t just a shiny new toy. It’s forcing us to rethink how we manage projects, lead people, measure success, and yes — mitigate risk.
Here’s my take on the guide, what stood out, and where we go from here.
AI Isn't a Toolset — It's a Mindset Shift
One of the guide’s core messages hit home:
Leading AI Transformation is not a technology implementation project. It’s an organizational transformation.
That means leaders need to stop thinking of AI as “something IT handles” and start treating it as a company-wide evolution — affecting culture, strategy, process, and performance.
What this means for us as PM’s or within the PMO…… or anyone managing change, is simple: the old playbook won’t cut it.
We can’t just “overlay” AI onto existing systems. We need to dig into foundational questions:
How do we upskill people across functions?
Where does AI fit in our strategic roadmap?
What are the ethical lines we won’t cross?
Organizations that succeed with AI, PMI notes, will treat it not as a new app, but as a catalyst for change.
The best part?
Project professionals are already wired for this. We know how to lead change — we just need to apply that muscle to AI itself by leading AI transformation in our organizations!
The PMO's Moment to Step Up
Traditionally, the PMO has been seen as the function that tracks tasks, enforces templates, and reports up the chain. Functional, yes — but often reactive.
The AI era offers an opening to flip that script.
PMI’s guide urges PMOs to evolve from taskmasters to strategic enablers — integrating AI at every phase of project delivery. This includes:
Shaping governance frameworks for AI.
Supporting AI capability mapping across teams.
Tracking performance metrics aligned with business value, not just deadlines.
But here’s the nuance I think matters most: we don’t need to know everything about AI tech.
We just need to be the connective tissue between vision and execution.
And that’s where the PMO can shine — bringing structure, clarity, and measurement to what often feels like chaos.
Have you read this book by Laura Barnard?
Sounds like she has been aligned with this way of thinking for some time now!
Risk Management Needs a Reboot
You can’t talk about AI without talking about risk — and thankfully, PMI doesn’t shy away from it. In fact, the guide dedicates a significant portion to AI-specific risks like:
Data privacy
Algorithmic bias
Model transparency
Ethics and compliance
As a risk-focused professional, this section spoke volumes. But here’s what I’d add: we need to expand our definition of risk management in the AI era. This is no longer about identifying risks once and reporting them at the next stage gate. We need:
Living risk registers that evolve as models learn.
Cross-functional teams including data scientists, legal, and ops.
A balance between speed and responsibility.
And just like any transformation, managing AI risk requires building a culture of awareness. People need to know what to flag, what good AI looks like, and when to raise their hand. That’s a leadership and education issue, not just a policy one.
Not Saying AI Doesn't Come with Opportunity type Risks!
AI will open new doors for conducing risk management within organizations.
Using past projects by building something like a historical database within a local AI could become an opportunity for project managers. Historical data can help us build project plans, risk registers, benefits management, stakeholder lists, etc.
And then it can help us identify High Level Risks early, leaving us room to work around them with our stakeholders early. Saving time, money, and a lot of headaches!
What’s Missing? Capability Mapping + Role Readiness
While the guide is comprehensive, there’s one thing I wish it tackled in more depth: how to assess your current state.
Too often, organizations don’t know where to start — or they assume AI readiness means hiring a bunch of data scientists. (Spoiler: it doesn’t.) What’s missing is a structured way to evaluate how ready your teams, processes, and culture are to support AI.
That’s why we created a quick-read tool you can use with your team.
👉🏼 [Take the AI Readiness Assessment Tool] – It’ll help you:
Identify which parts of your PMO or TMO are ready for AI.
Uncover capability gaps across governance, ethics, and tech.
Start conversations with leadership using real benchmarks.
Think of it as a diagnostic — because until you know where you stand, it’s hard to plan where to go.
AI Readiness Assessment Tool
Want to know where your organization stands with AI?
Try our free 👉🏼 AI Readiness Assessment Tool.
It’s built to help PMOs and TMOs benchmark their current state and identify quick wins for the road ahead.
Check It OutAI Transformation Isn't One-and-Done
One of the guide’s most important takeaways is buried toward the end:
AI integration is not a one-time event — it’s a cycle.
Organizations that succeed with AI adopt a continuous improvement approach, like Plan-Do-Check-Act (PDCA), to:
Monitor AI performance over time.
Adjust governance based on outcomes.
Integrate feedback loops from users and stakeholders.
This isn’t just about tuning models. It’s about treating AI the same way we treat any mission-critical system — with care, iteration, and attention.
Final Thought
Start Before You're READY
Here’s the truth: most organizations aren’t “ready” for AI. But that doesn’t mean you should wait.
- Pick one project.
- Find a motivated team.
- Define a small use case.
- Track results.
- Learn.
- Share.
That’s how momentum builds — and that’s how transformation happens. This guide from PMI is a fantastic starting point.
But the next step? That’s yours to take.
The Risk Blog is a subcomponent of Forty-Four Risk PM, LLC and connected with 44PM Training Academy.
