Project and Program Managers have traditionally spent considerable effort gathering information, consolidating status, identifying risks, preparing reports, coordinating stakeholders and supporting decisions. AI can increasingly assist with many of these activities, allowing managers to spend more time on judgment, leadership and intervention where human experience matters most.

From Reporting to Intelligent Decision Support

Project reporting often involves collecting information from multiple teams, identifying deviations and presenting an understandable picture to stakeholders.

AI can help analyze large volumes of project information, summarize progress, identify emerging patterns and highlight areas requiring attention. Instead of merely producing status reports, managers can potentially use AI to ask more useful questions:

• Which milestones are showing early signs of delay?
• Which risks are becoming more significant?
• Are dependencies creating a potential delivery bottleneck?
• Which issues require management intervention?
• What has changed since the previous reporting cycle?

The value is not simply faster reporting. It is the possibility of moving from reporting what has happened to identifying what may require attention next.

Strengthening Risk and Issue Management

Risk management is another area where AI can provide useful assistance.

A Project Manager may be managing dozens of risks, issues, assumptions and dependencies across multiple workstreams. AI can support the analysis of this information, help identify recurring themes, highlight relationships between risks and dependencies, and assist in developing possible mitigation approaches.

However, AI-generated recommendations should not automatically become management decisions.

The Project or Program Manager still needs to understand the business context, assess consequences, challenge assumptions and decide the appropriate response.

Supporting Planning and Delivery Monitoring

AI can also assist managers during planning and execution.

It can help organize activities, examine dependencies, summarize historical information, identify potential planning gaps and analyze delivery information as execution progresses.

For experienced managers, the opportunity is not to hand planning over to AI. It is to use AI as an additional analytical capability while applying professional experience to determine whether the output makes sense in the real delivery environment.

Improving Stakeholder Communication

Different stakeholders require different information.

A senior executive may need a concise view of business impact and decisions required. A delivery team may need detailed actions and dependencies. A customer may need clarity on progress, risks and commitments.

Generative AI can help managers structure and tailor communication for different audiences, summarize lengthy discussions and prepare initial drafts.

But communication is more than generating well-written text. Understanding stakeholder expectations, organizational sensitivities and the appropriate tone still requires human judgment.

Governance Cannot Be Delegated to AI

As AI becomes more deeply integrated into project and program activities, governance becomes even more important.

AI can support governance activities, but accountability cannot simply be transferred to an AI tool.

The Project Manager's Role Is Evolving

The emerging Project or Program Manager therefore does not need to become an AI engineer.

What becomes increasingly valuable is the ability to understand where AI can improve delivery, where it should not be relied upon without validation, and how human expertise and AI capabilities can work together effectively.

The future may not be AI versus Project Managers.

It is more likely to be Project and Program Managers who know how to work effectively with AI.