Guessing is not a strategy: How to build decision velocity with AI and real-time data
June 10, 2026 | Live Webinar
Categories: ai
AI was the talk of last year, and this year seems like it’s still up there in terms of things we need to know, learn about and absorb into how we do our jobs as project professionals. The AI pressure feels relentless – there’s so much to learn. And yet we are picking it up as we go along – familiarity with AI is rising because it has to.
I talk to project managers who are worried about falling behind. Learning new tech shouldn’t feel like a second job. However, gone are the days when you could go on a 3-day classroom Microsoft Project course and feel like you knew what to do. These days, tech in the cloud is updated regularly and new features come out all the time. And, of course, AI is no different – if anything, the rate of development and adoption feels like it’s significantly faster.
What do we actually need to know about AI?
The good news is that you don’t have to know it all. It’s worth focusing your learning on where AI fits into delivery work and where it doesn’t. You don’t need to code anything, in fact, even experienced coders are coding less these days as AI responds to natural language prompts as well as it does to code inputs.
Low-effort learning areas
Let’s say that you do want to put some effort in. You should start with PMI’s courses in AI, which are great, tool-agnostic learning resources which will help you build your vocabulary and the basics.
Then think about where you could use the tools you already have access to – likely a generative AI office ‘companion’ type chat tool.
Then think about where you could advance your use of AI for specific use cases like decision support, scenario exploration, drafting communications or identifying risks.
Boundaries and risks to be aware of
Learning all the cool stuff you can do with AI is one thing, but you also have to balance that with boundaries and risks. Think about how bias and overconfidence might show up in your data or processes. What might data sensitivity look like for your data sets? Is there a risk of over-automation, and what does that mean for the humans in the process?
A sustainable learning approach
Think about learning in small loops, on a just-in-time basis so you can apply knowledge immediately. Then it’s more likely to stick and also to feel worthwhile, not like you’ve just spent time watching a video on AI theory knowing you can’t put it into practice.
Let the experts in the business focus on the larger, complex projects – you’ll be brought in to support with project management best practice as and when you are needed, so ignore the pressure of becoming a subject matter expert in AI if it doesn’t feel like a natural fit for you.
You’ll learn better through doing, so think about how you can develop AI literacy over AI mastery. You want to be competent in the tools that you have so you can make the best use of them, but there’s no need to be at the cutting edge, trying every product in your spare time.
Organisations are typically quite slow to change, so you’ve got time and the fact that you’re even asking yourself the question about how best to learn AI puts you ahead of some of your colleagues!
Posted on: June 03, 2026 12:00 AM |
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