PMO Roundtable: How Are People Really Leveraging AI
In our latest Change & Transformation roundtable, we brought together PMO and transformation leaders from retail, aviation, insurance, technology and manufacturing to discuss a question many organisations are currently wrestling with: how are people actually using AI today?
While the hype around AI continues to accelerate, the discussion revealed a more pragmatic reality. Most organisations are still early in their journey, experimenting with use cases, proving value and trying to strike the right balance between innovation and governance.
Here are the key themes that emerged.
1. AI Adoption Starts with Problems, Not Tools
A common theme throughout the conversation was the importance of solving real problems rather than buying AI for the sake of it.
One participant observed that many organisations are rushing to procure tools without first establishing the business case behind them. Another reflected on a conversation with a colleague who had enthusiastically adopted Claude but couldn’t clearly explain what problem it was intended to solve.
As one leader put it, organisations should avoid “jumping on the bandwagon” and instead focus on implementing AI where there are genuine use cases and measurable benefits.
Several attendees highlighted that they are deliberately steering their businesses away from “spray and pray” adoption and instead prioritising AI investments that remove manual work and deliver tangible value.
2. Most PMOs Are Starting with Productivity
For many teams, the first wave of AI adoption has focused on improving day-to-day efficiency.
Participants described using Copilot and similar tools to:
- Summarise meetings and actions.
- Generate status reports and executive summaries.
- Review large data sets and surface insights.
- Create risk logs and lessons learned repositories.
- Produce dashboards and reporting packs.
- Automate reminders, chasers and administrative activities.
One PMO leader described having over 3,500 lessons learned collected across seven years. Rather than searching through cumbersome spreadsheets, AI is now being used to identify relevant lessons and generate pre-populated risk logs at the start of every project.
Another participant explained that AI-generated summaries have transformed programme reviews, allowing stakeholders to focus only on key decisions instead of reviewing every project line-by-line.
The consensus was that AI is helping teams spend less time producing information and more time acting on it.
3. Resource Management Is Emerging as a Major Opportunity
Beyond productivity, several organisations are beginning to use AI to optimise resources and portfolio performance.
One participant described how AI analyses resource utilisation, project updates and governance adherence, identifying where plans haven’t been maintained or risks have gone unaddressed.
The system also highlights over-allocation and recurring patterns where certain skills are consistently overestimated.
Rather than simply asking whether there are enough resources, the organisation has started asking a different question:
“Where are we overcommitting?”
By reducing unnecessary demand on key technical resources, they have already saved nearly 1,000 development hours and expect to realise millions in cost savings over the next year.
Others spoke about using AI to identify portfolio hotspots, capacity constraints and resource bottlenecks, helping teams prioritise interventions before problems emerge.
4. Measuring Value Is Still a Work in Progress
Although many participants are seeing clear benefits, most admitted they are still figuring out how to measure success.
Some are tracking time savings, while others are focusing on broader objectives such as reducing manual effort.
One organisation has committed to a double-digit reduction in effort across the business and simply agrees time savings with departments as new capabilities are introduced.
Another PMO has set a target of reducing manual activity by 20%.
Importantly, many leaders emphasised that AI shouldn’t automatically translate into headcount reduction.
Instead, the goal is to redeploy that capacity into higher-value activities.
As one participant put it:
“What if that 50% of time could generate five times someone’s salary in benefits elsewhere?”
The focus should be on elevating people rather than replacing them.
5. Governance Matters, But Few Have a Mature AI Strategy
Despite rapid adoption, many organisations admitted they still lack a formal AI strategy.
Most are relying on policies, governance forums or lightweight frameworks while the technology continues to evolve.
Some organisations have established review boards to evaluate AI tools and ensure compliance with information security requirements. Others are taking a more experimental approach, allowing teams to explore capabilities before industrialising successful solutions.
A recurring concern was avoiding a future landscape full of expensive, unused licences.
Several leaders stressed that the pace of AI development makes it difficult to commit to a single platform too early.
Instead, many are adopting a “tool agnostic” approach while maintaining strong oversight around security and data management.
6. Data Quality Matters More Than the Tool
Another interesting theme was that AI is exposing the importance of good data.
As one participant noted:
“As long as the data is there, someone will figure out how to use it.”
Several organisations are focusing less on finding the perfect platform and more on ensuring information is accessible and structured.
Some have created repositories by scraping PowerPoint reports, Teams channels and retrospective outputs into central stores that AI can query.
Others are designing data lakes that sit above existing systems, enabling leaders to ask questions directly of their portfolio data.
The message was clear: AI cannot create insight from poor-quality information.
7. The Human Trust Gap Still Exists
Perhaps the most surprising topic was the issue of trust.
Multiple participants noted that executives often champion AI adoption but react differently when AI-generated reports appear in front of them.
One leader joked that the immediate response is:
“This is clearly generated by AI. Rewrite it.”
Another reflected that AI-generated outputs can sometimes feel like “cheating”, despite improving efficiency.
To overcome this, some teams are openly acknowledging when AI has been used, explaining the prompts that were applied and demonstrating how human judgement shaped the final recommendations.
The group agreed that AI should enhance expertise rather than replace it.
The value lies not in the output itself, but in what people do with it.
Final Thoughts
Despite the headlines, most organisations are still at the beginning of their AI journey.
The conversation highlighted that adoption is far less about replacing people and far more about freeing them to focus on work that creates value.
Whether through smarter reporting, lessons learned repositories, resource optimisation or automated administration, the common ambition was clear:
Spend less time producing information and more time delivering outcomes.
As one participant concluded:
“Project managers shouldn’t be spending their lives chasing updates. They should be thinking strategically and driving value.”
And perhaps that’s the biggest opportunity AI presents to the PMO.
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