Artificial intelligence is changing how enterprise technology teams plan, build, and deliver products and platforms.

Much of the early conversation around AI in software delivery focused on developers writing code faster. In Agile environments, attention quickly expanded to areas such as backlog refinement, documentation, testing, retrospectives, and sprint reporting.

But there is another layer of enterprise delivery where AI can have an equally significant impact: program management.

For a Program Manager responsible for coordinating multiple teams, managing dependencies, tracking risks, communicating with stakeholders, and maintaining delivery predictability, AI can become more than a productivity tool.

Used well, it can become a delivery intelligence layer.

The goal is not to replace program-management judgment with AI. It is to reduce the administrative friction surrounding delivery so that Program Managers can spend more time on decisions, alignment, risk management, and business outcomes.

1. From Reporting to Delivery Intelligence

A significant part of enterprise program management involves turning large amounts of information into something people can act on.

A Program Manager may need to bring together:

  • backlog and work-item information
  • sprint and release metrics
  • dependency updates
  • risks and issues
  • delivery milestones
  • engineering feedback
  • stakeholder decisions
  • resource constraints
  • changes in business priorities

Traditionally, much of this information has been collected, reconciled, summarized, and reformatted manually.

That work is necessary, but it does not necessarily require the Program Manager to perform every step manually.

AI can help summarize large volumes of delivery information, identify patterns, organize unstructured updates, and prepare first drafts of reports or communications.

The important distinction is between generating information and making decisions.

AI can help answer:

What changed?
What appears to be at risk?
Which dependencies are becoming important?
What information is missing?
What should I investigate further?

The Program Manager still needs to answer:

What should we do about it?

That distinction becomes particularly important in complex enterprise programs where technical, organizational, commercial, and business considerations intersect.

2. AI-Assisted Program Planning

Planning is another area where AI can reduce administrative effort.

Enterprise programs rarely begin with perfectly structured information. Requirements evolve. Priorities change. Dependencies emerge. Teams discover technical constraints. Stakeholders introduce new expectations.

A Program Manager therefore spends significant time turning incomplete information into an executable delivery plan.

AI can assist with parts of that process.

For example, AI can help analyze work items and identify:

  • incomplete requirements
  • ambiguous acceptance criteria
  • duplicated work
  • potential dependencies
  • missing information
  • inconsistencies between related items
  • areas that require clarification before execution

This does not mean allowing AI to create a plan and accepting it without review.

A better approach is to use AI as a planning assistant.

The Program Manager remains responsible for validating the assumptions, challenging the output, and deciding what should actually enter the delivery plan.

That human review is particularly important because an AI-generated recommendation can appear reasonable while still being based on incomplete or incorrect information.

3. Making RAID Management More Proactive

Risk and dependency management is one of the areas where I see significant potential for AI-enabled program management.

Traditional RAID management can become reactive.

A risk gets added to the register after someone identifies it. An issue gets escalated after it starts affecting delivery. A dependency becomes visible when another team cannot proceed.

By then, the Program Manager may already be managing the consequences.

AI can potentially help move this process toward early detection.

Consider a program with multiple Agile teams working on interconnected capabilities.

Signals may exist across:

  • delayed work items
  • changing delivery dates
  • blocked dependencies
  • repeated defects
  • unresolved decisions
  • changing priorities
  • unexpected changes in throughput
  • overdue actions

Individually, each signal may not look significant.

Together, they may indicate that a delivery milestone is becoming increasingly risky.

This is where AI can be useful: not necessarily by making the decision, but by helping the Program Manager see the pattern earlier.

The responsibility for assessing the risk, discussing it with stakeholders, agreeing mitigation actions, and deciding when to escalate remains human.

AI can surface the signal.

The Program Manager provides the judgment.

4. Turning Azure DevOps Data Into Better Delivery Conversations

Tools such as Azure DevOps already contain a substantial amount of information about how a technology program is progressing.

Work items, backlogs, bugs, priorities, dependencies, states, and delivery metrics can provide a valuable picture of execution.

The challenge is not always the availability of data.

The challenge is turning that data into useful decisions.

This is where AI-assisted workflows can become interesting for Program Managers.

Instead of spending significant time manually assembling information for every status discussion, a Program Manager can use AI to help summarize the underlying delivery data and identify areas that deserve attention.

For example:

  • Which work items have remained blocked?
  • Which dependencies have not moved?
  • Which commitments are showing signs of delay?
  • Which backlog items need clarification?
  • What changed since the previous reporting cycle?
  • Which areas should be discussed with engineering or product leadership?

Microsoft is increasingly bringing Copilot capabilities into Azure DevOps-related workflows, including natural-language interaction with work-item information and AI-assisted planning and governance scenarios.

The real value, however, isn't the novelty of asking questions in natural language.

It is the possibility of spending less time collecting information and more time interpreting it.

5. AI Can Improve the Quality of Executive Reporting

Executive reporting is another area where Program Managers can spend considerable time.

Senior stakeholders generally don't need every detail contained within the delivery system.

They need to understand:

  • Are we on track?
  • What has changed?
  • What is at risk?
  • What decisions are required?
  • What dependencies could affect the outcome?
  • Where does leadership need to intervene?

The Program Manager acts as the bridge between detailed delivery information and executive-level decision making.

AI can help create the first layer of that translation.

For example, a Program Manager could use AI to analyze delivery updates from multiple teams and produce a structured summary covering:

Progress

What has been completed?

Risks

What could affect upcoming milestones?

Dependencies

Which external factors require attention?

Decisions

What requires stakeholder intervention?

Next steps

What should happen before the next reporting cycle?

The output should still be reviewed and rewritten by the Program Manager.

Executive communication requires context, judgment, and an understanding of organizational dynamics that cannot simply be delegated to a language model.

6. AI Does Not Replace Stakeholder Management

This is where I believe discussions about AI in program management can become overly optimistic.

Program management is not just about moving work items through a system.

It is about people.

A Program Manager may need to negotiate competing priorities between teams, resolve disagreements, coach stakeholders through difficult decisions, escalate risks, manage expectations, and build alignment when there is no obvious answer.

AI can help prepare for those conversations.

It cannot replace the relationship behind them.

For example, an AI system might identify that two teams have a dependency that threatens a milestone.

It can summarize the dependency.

It can potentially highlight historical delivery patterns.

It can help prepare questions for the discussion.

But someone still needs to bring the teams together, understand why the dependency exists, negotiate options, and establish accountability.

That is program management.

7. The AI-Augmented Program Manager

The biggest change may therefore not be the disappearance of the Program Manager.

It may be the disappearance of some of the administrative work surrounding the role.

A traditional delivery cycle might look like:

Collect → Consolidate → Report → Discuss → Decide

An AI-enabled delivery cycle can move closer to:

Analyze → Surface → Validate → Discuss → Decide

That difference matters.

If AI can reduce the amount of time spent collecting and consolidating information, the Program Manager can spend more time on the parts of the role that require experience:

  • strategic planning
  • stakeholder alignment
  • risk management
  • dependency resolution
  • organizational change
  • coaching
  • decision making
  • business outcomes

This is consistent with the broader direction of AI adoption in project and program management. PMI now explicitly frames AI as something project professionals need to apply responsibly while retaining human leadership, judgment, and governance.

8. The Human Skills Become More Important

There is an interesting paradox in AI-enabled program management.

As AI becomes better at handling administrative work, the distinctly human capabilities of the Program Manager become more important, not less.

A strong Program Manager needs to understand both sides.

The technology side:

  • AI capabilities
  • data quality
  • automation
  • Azure DevOps
  • delivery metrics
  • Agile practices
  • engineering workflows

And the human side:

  • leadership
  • communication
  • negotiation
  • coaching
  • stakeholder management
  • organizational change
  • business context

The combination is what makes AI useful.

Without delivery expertise, AI can generate impressive-looking information without meaningful context.

Without AI literacy, Program Managers may continue spending valuable time performing work that could increasingly be assisted or automated.

The opportunity is to combine the two.

9. Responsible AI Matters in Enterprise Delivery

There is also a less visible responsibility for Program Managers as AI becomes embedded in delivery workflows.

AI-generated information should not automatically be treated as fact.

Program teams need to consider:

  • data quality
  • confidentiality
  • access controls
  • accuracy
  • traceability
  • human review
  • governance
  • appropriate use of organizational data

This becomes particularly important when AI is connected to enterprise systems containing sensitive project, customer, or operational information.

The right question isn't simply:

"Can we use AI for this?"

It is:

"Can we use AI for this responsibly, and what level of human oversight does the decision require?"

That mindset is essential for enterprise adoption.

Conclusion: From AI-Assisted Delivery to AI-Enabled Program Management

AI is changing enterprise delivery, but I don't believe its most important contribution is simply making existing processes faster.

The bigger opportunity is to change how Program Managers interact with delivery information.

Instead of spending significant time collecting, formatting, and summarizing information, Program Managers can increasingly use AI to identify patterns, surface risks, improve planning, and prepare for better conversations.

That creates an opportunity to move program management further away from administrative coordination and toward delivery intelligence, strategic alignment, and business outcomes.

AI should not replace the Program Manager.

It should give the Program Manager more time to do the work that requires judgment.

The future I see is not AI versus Program Managers.

It is AI-augmented Program Managers who combine delivery experience, Agile practices, enterprise technology, and AI-enabled workflows to make complex programs more predictable and more outcome-focused.