Advice on upskilling in the age of data and AI

Every new technology that comes along is first shaped by the work of one generation of human beings, before going on in turn to shape the work and lives of subsequent generations. This is proving to be just as true of the current wave of AI-based change as it was of previous transformations, from the printing press and the steam engine to the internet, smartphone and cloud computing.

All of these technologies had – are having – huge impacts on the way that people live and work for many years after they first appeared. Indeed, the discipline of project management has itself helped shape, and been shaped by, many of these waves.

So, how is the increasing application of AI in projects likely to impact what it takes to be a successful project professional in the months and years to come? With the usual caveat that, as legendary New York Yankees baseball coach Yogi Berra once noted, “Making predictions is hard, especially when they are about the future”, here are a few thoughts from the interviewees of Project journal’s feature on data and AI for our spring 2026 issue. Each of them is in the midst of shaping – and being shaped by – AI-fuelled change in their own organisations.

1. Equip yourself with the fundamentals – walk before you can run

“As project managers, we’re all familiar with the fundamentals around risk, assurance, benefits and so on. I think there needs to be a parity of understanding around the basics of data and AI, and how they can be applied to a project,” says Wayne George, Strategy Director, Place and Assurance, at Local Partnerships.

“That includes things like the basic principles of AI, data modelling, data sharing and data legislation – which cut across a number of different areas – and also ethical considerations. Am I using data for the public good? I think there is still a bit of a gap in the basic understanding,” says George.

2. Learn to talk like a leader

“Senior managers are much more interested in the outcomes you have delivered than in how you got there. They want to know what you’ve achieved, not that it took you 40 hours to do it,” says Lloyd Skinner, CEO of Greyfly.

3. Focus on what AI cannot do, like building relationships

“Projects today are ultimately human endeavours. They still will be tomorrow, so prepare yourself by focusing on the human side: go to networking events and get a mentor,” adds Skinner.

4. Look to augment human intelligence rather than replace it

“Human intelligence is brilliant, but it is generally a function either of one’s direct experience or information that you acquire that is based on the experience of others. Trying to leverage all that information across our portfolio of 10,000 projects is mission impossible – so I don’t want to replace human intelligence but complement it with AI, specifically predictive analytics,” says Rob Lord, Director of Major Programmes at BT International.

5. Embrace the potential of AI, but don’t be consumed by it

“People can be swept along by the AI dream, but you need to be really clear on the problems you are trying to solve – that’s what should be leading you, not the technology,” adds Lord.

6. Be prepared to take calculated risks

“We are pretty clear on how much [implementing project data analytics] is going to cost. We’re less clear on the benefits, because we haven’t got a track record to fall back on. That requires a mindset change on how you view investment risk when not everything can be validated completely,” says Lord.

After more advice?

Here are five key data literacy skills for project professionals, taken from the newly published APM Data Literacy Framework.

Managing project information: The ability to organise, store, retrieve and utilise information throughout the entire project life cycle.
Foundations of data: The ability to develop a strong foundation in core data concepts, empowering individuals to navigate the world of data with confidence.
Interpreting and influencing with data: The ability to extract meaning and derive insight from data that leads to action.
Data visualisation and storytelling: The ability to leverage data visualisation tools that transform complex data sets into clear and impactful narratives that drive informed decision-making.
Decision making with data: The ability to integrate data analytics and AI into the decision-making process.