AI Won’t Fix a Bad Project. But It May Tell You It’s Going Bad Before You Know It.

For the past several years, much of the conversation about AI in project management has focused on productivity. AI can summarize meetings, draft communications, develop project artifacts, analyze documents, and reduce administrative work. Those are valuable applications, but they may ultimately be among the least interesting ways AI changes project delivery.

The larger opportunity is using AI to create an intelligence layer across a project: connecting information that traditionally lives in separate systems, identifying weak signals, recognizing patterns, and helping leaders understand where intervention may be needed before a problem becomes a crisis. This shift is already happening more broadly across organizations. KPMG’s latest AI Pulse found that while 55% of leaders report productivity gains from AI, nearly as many—49%—are already realizing value through faster decision-making. As KPMG’s Todd Lohr put it, "AI’s value story is getting sharper.”

From Project Reporting to Project Intelligence

Complex projects generate enormous amounts of structured and unstructured data across schedules, RAID logs, meeting transcripts, requirements, decisions, stakeholder feedback, change requests, budgets, vendor deliverables, training records, and status reports. Traditionally, project teams have had to collect that information, organize it, summarize it, and determine what matters. The larger the project, the harder it becomes to see the relationships among all of those signals.

AI changes what is possible. Rather than simply asking AI to summarize last week’s steering committee meeting, imagine comparing months of meeting transcripts against the RAID log and identifying issues discussed repeatedly but never formally captured as risks. An AI-enabled project environment could recognize decisions that continue to slip, connect them to downstream milestones, identify changes in stakeholder sentiment, detect recurring requirements changes, and surface combinations of signals that warrant attention.

This moves us beyond using AI to produce project information and toward using AI to interpret it.

What This Looks Like in Practice

We are already seeing an early version of this on one of our large technology implementations. Thousands of employees need to complete training, and while we had completion data, traditional reporting wasn’t giving us enough information to actively manage training as an implementation risk.

Using AI-supported analysis, we created much better visibility into which departments were falling behind. But knowing who was behind wasn’t the most important insight. We needed to understand why. Was it employee availability? Competing projects? Managers who weren’t reinforcing the priority? Confusion about expectations? Resistance to the new system?

Each explanation requires a different intervention. Some groups may need protected training time, while others need leadership support, clearer communication, or additional change-management attention. Instead of treating low completion as one organization-wide problem and sending another reminder to thousands of employees, we can identify where risk is concentrated, investigate what is driving it, and determine what action is most likely to change the outcome.

That is the difference between project reporting and project intelligence.

The Next Frontier: AI as an Early-Warning System

The more compelling opportunity is what happens when we connect more of the project. Imagine an intelligence layer continuously evaluating schedule performance, risks, decisions, resource constraints, change requests, vendor performance, stakeholder feedback, and meeting sentiment. A single signal may mean very little; several signals moving in the wrong direction simultaneously may indicate something very different.

A milestone slipping by two weeks may be manageable. A milestone slipping while decision cycle times increase, change requests accelerate, a critical resource becomes overallocated, and stakeholder sentiment deteriorates could indicate a systemic problem well before a traditional status report turns red.

This is where predictive analytics, generative AI, and increasingly agentic AI become particularly interesting for project and program management. The objective is not to have AI declare that a project will fail. It is to identify patterns that deserve attention earlier, help leaders investigate likely root causes, model downstream implications, and evaluate potential interventions while there is still time to influence the outcome.

Early evidence suggests this matters. PMI found that 89% of high GenAI adopters in project management reported improved problem-solving, compared with just 46% of low adopters. High adopters also reported significantly better outcomes in schedule, cost, scope, and quality management.

The Value of Human Judgment Increases

None of this eliminates the need for experienced project leadership. In fact, I believe it makes judgment more important.

AI may recognize that a decision has been open for six weeks. An experienced project leader may know that two departments fundamentally disagree about the underlying business process. AI may identify deteriorating stakeholder sentiment, but a change leader still needs to determine whether the cause is poor communication, lack of capacity, change fatigue, or genuine disagreement with the solution.

The project leader of the future should spend less time assembling information and more time interrogating it. What is changing? What patterns are emerging? Which assumptions no longer appear valid? What happens if we do nothing? Where should we intervene? And perhaps most importantly, what questions aren’t we asking?

The most compelling application of AI in project management may ultimately have very little to do with writing status reports faster. It may be our ability to recognize earlier that a project is telling us something, understand what it is saying, and act while we still have time to change the outcome.