John McIntyre
Founder, HotPMO
Every month I trawl through the research papers, reports, and podcasts so you don’t have to. Here’s what caught my eye this month.
March threw up some genuinely useful material. Portfolio visibility, productivity measurement, AI in decision-making, and upcoming events for April…
The UK infrastructure pipeline: something to aspire to?
The UK Government has updated their Infrastructure Projects pipeline – now including details of 734 projects worth £718bn over the next ten years. If you are involved in construction of schools, hospitals, railways, and clean energy projects, this is a great place to see what’s happening when so you can start to plan your capacity accordingly.Â
The version released this month is a big uplift to the previous one – it includes resource forecasts – showing how many workers will be needed, and what skills will be required. It also shows where in the country those resources will be required.Â
The pipeline is an incredible resource for people directly involved and will allow bodies and private businesses to spot problems (and opportunities!) early.
But it is also should serve as inspiration to PMO teams across all sectors. Forward-looking portfolio views that consider demand, constraints, workforce pressures and the regional dynamics are massive undertakings – both in terms of development, and also maintenance. NISTA have done a great job on this, and there is plenty for PMO teams to draw inspiration from. Browse through the dashboards and understand what data has been compiled, how it has been presented, and how useful it will be to key stakeholders. Consider how your future-facing reporting compares, and whether there are lessons to be extracted from NISTAs approach that can be applied in different sectors and organisations.Â
Are your metrics doing anything useful?
Most PMOs produce the same metrics and measures. The challenging question is whether or not those measures change anything. If a metric doesn’t help someone decide or act, it’s just reporting noise.
That question gets a rigorous treatment in a paper published this year in the Project Management Journal. What Is Project Management Productivity? by Pollack, Anichenko, and Crawford is based on 55 interviews with project practitioners and uses prototype theory to identify 21 characteristic cues of productivity in project environments. I raised it in the PMO HotHouse news slot, but it’s worth going deeper.
The most immediately useful thing for PMOs is how the authors organise those cues. They distinguish between lag indicators (measures that tell you how productive you’ve been), and lead indicators (conditions that signal whether your team is set up to be productive going forward).
They also separate hygiene factors (things that drag productivity down when absent, but don’t significantly lift it past a baseline) from motivators (things whose presence actively drives productivity).
|
|
Hygiene Factors
|
Motivators
|
|---|---|---|
|
Lead indicators |
Governance, Adequate resources, Quality & accessibility of information, Clear direction, Decision-making, Meeting quality |
Engagement, Urgency, Stakeholder communication, Collaboration, Transparency |
|
Lag Indicators |
Rework, Efficiency, Costs |
Meeting performance metrics, Schedule, Outcomes, Outputs, Effectiveness, Quality |
Framework adapted from Pollack, Anichenko & Crawford (2026), ‘What Is Project Management Productivity?’, Project Management Journal, Vol. 57(2), pp. 240–259.
The practical read-across is straightforward enough. Cost, efficiency, and rework minimisation are hygiene factors. Manage them well enough and you’ve met the bar. Investing heavily beyond that point produces diminishing returns. Engagement, collaboration, transparency, and clear direction are lead motivators. Harder to measure, but these are the conditions that actually drive future performance. Most PMO dashboards focus almost entirely on lag hygiene cues (cost and schedule), and leave the rest untracked.
The paper’s recommendation: make sure your measurement covers all four quadrants. Lag indicators tell you what happened. Lead indicators tell you what’s likely to happen next. Covering only half of that picture means you’re always looking backwards.
Talk of leading and lagging indicators is nothing new for experienced PMO professionals, but having the framework to articulate it, and peer-reviewed evidence to back it up, makes it a more credible conversation to have internally when pushing for better measurement.
AI in decision-making: Research (not hype)
There’s no shortage of AI commentary right now – most of it cheerleading, some of it doom-saying, very little of it grounded in actual evidence. So it was genuinely refreshing to come across a peer-reviewed paper that has done the work.
Who’s Steering Whom and in What Direction? by Kögel, Meile, and Canos-Daros, published in volume 57 of the Project Management Journal, looks specifically at how AI and senior managers make project decisions differently. The researchers presented the same decision cases to 37 experienced top managers and to ChatGPT, then analysed the responses across multiple dimensions. The differences were statistically significant and worth understanding.
AI tends to bring more logical, structured thinking to a decision. It generates more creative options, introduces less bias, behaves less politically, and produces more objectively consistent outputs.
Humans bring something different: context awareness, an understanding of the politics, experience-based judgement, and the ability to navigate ambiguity.
Neither profile is better in isolation – and that’s precisely the point. Using both in decision making is a sensible strategy.
- Context awareness; Understanding the politics; Experience-based judgement, Navigating Ambiguity
- Logical/structured; less bias; More creative options; Less political behaviour
But the paper does come with a clear warning – Small, incremental uses of AI across a decision chain can accumulate quietly. Consider the number of emails, options papers, reports and change requests that can feed into a single strategic decision. If AI is being used for decision making at every step in the chain, then there is a risk of it shifting the dial imperceptibly initially, but then having a greater combined effect until projects are being steered in a direction no one consciously chose. The authors call this “AI-induced deviation.“
For PMOs who design governance, build decision packs, and control what information reaches senior stakeholders, it’s a legitimate concern. The message isn’t to avoid AI. It’s to be explicit about where it’s been used. Expect the same transparency from your project managers, BAs and other project team members so usage is traceable. A rationally tidy analysis doesn’t automatically become a better real-world decision once politics, stakeholder readiness, and organisational context come into play. That’s the human bit. And it matters.
April: worth leaving your desk for
Finally, April has some good reasons to be in a room with other PMO people.
Project Forum is on 21 April at the Business Design Centre in London. Free to attend, full day, mix of seminars, roundtables, and exhibition. We’ll be there, hosting a couple of Birds of a Feather small-group discussions, no slides, no pre-registration. Just practitioners working through real problems. Do swing by and say hello!
The evening before — Monday 20 April at 6pm — is the Pre-Project Forum meetup at The Angelic, 57 Liverpool Rd, London N1. Theme is “What’s Making Delivery Harder (or Easier?)”. I’ll be kicking off the discussion. If you’re heading to Project Forum the next day (or even if your not!), come join us for fresh thinking and project/PMO socialising.
This roundup is based on my monthly PMO News segment from the PMO HotHouse, run by the House of PMO. If you’re not already part of that community, it’s well worth a look.
 If your PMO is going through its own evolution – whether that’s starting from scratch, expanding your mandate, or figuring out where AI fits – get in touch. It’s what we do.
