Product managers operate at the intersection of customers, engineering, strategy, and execution. They translate customer needs into requirements, prioritize competing investments, coordinate and make trade-offs with engineering teams, outline product specifications, and keep leadership informed about what is being delivered and where resources are being invested.
On the Infrastructure Engineering Services (IES) team within Microsoft Digital, our product managers (PMs) support the tools and services that keep Microsoft’s global network secure, resilient, and available. As Microsoft’s Customer Zero, our larger Microsoft Digital team—the company’s IT organization—is exploring how AI can accelerate individual tasks and help reshape the operating model that our PMs use to plan, govern, and communicate their work.
“One of the key things we do is make sure the most important work is getting done by the engineering teams, because our engineers are a pretty precious resource,” says Martin O’Flaherty, a principal PM manager in IES. “We need to be able to prioritize the work as it flows in. We have to understand what the engineering teams need, make trade-off decisions constantly, and make sure we’re delivering requirements and specifications that are essential for building the right thing.”
That has led O’Flaherty and his team to experiment with AI across a broader Continuous Planning model. Rather than treat planning as a periodic exercise, this approach creates an ongoing view of the work in progress as well as upcoming priorities, deliverables, engineering investment, and strategic alignment. Planning dashboards, engineering-capacity visibility, ADO data quality, recurring leadership reviews, monthly delivery reporting, and AI-generated summaries work together as parts of the same operating model.
“We need to make sure that we’re getting the right level of leadership visibility and support. Senior leaders understand that engineering resources are finite, so they want to make sure we’re investing the right amount of resourcing in the appropriate areas.”
Martin O’Flaherty, principal PM manager, Microsoft Digital
The objective isn’t simply to automate existing PM tasks, but to also create a better feedback loop between planning, execution, data quality, reporting, and decision making, giving our PMs more time to work directly with customers and engineering partners.
Turning planning data into leadership insight
As a product management leader, O’Flaherty often has to gather reporting on his team’s scenarios, including which are active versus closed, how the work aligns to organizational priories, and how engineering capacity is being invested. This not only helps him and his team make better prioritization and trade-off decisions but also provides crucial information for advocating with senior leadership.
“We need to make sure that we’re getting the right level of leadership visibility and support,” O’Flaherty says. “Senior leaders understand that engineering resources are finite, so they want to make sure we’re investing the right amount of resourcing in the appropriate areas.”
Most of that data already exists within systems such as Azure DevOps. The challenge is gathering the information and turning it into easily digestible insights. For O’Flaherty, that became an opportunity to use AI not simply to automate reports but also to connect planning data, data quality, and leadership reporting together into a more continuous system.
To accomplish this, O’Flaherty and his team built an AI-assisted reporting capability that can comb through all scenarios completed during the previous month, organize them around established strategic priorities, and summarize the details into leadership-ready reporting and visualizations. These show where the engineering investment is going and outcomes that the organization is delivering.
Below is an anonymized example of a dashboard O’Flaherty might use in an operational planning discussion or business review to provide near real-time visibility into planning health, delivery progress, resource allocation, and overall execution:
Early on, the AI-generated reporting wasn’t always accurate or complete.
Rather than treating those missing details as an AI problem, O’Flaherty and team used them to improve the system around it. He and the team refined the reporting logic, strengthened the underlying planning data, and improved how this capability weighs the documented evidence of what was actually delivered. Human review remains an important part of the process, but the amount of manual effort required has fallen significantly.
“Last month, we had almost no feedback. We looked at the report and were like, ‘Yep, it’s good to go,’” O’Flaherty says. “We’re talking about a task that used to take weeks to do. It would be the end of August by the time you’d have July’s report. We can now send that report on the first day of August.”
During this process, O’Flaherty also learned the importance of clean, comprehensive data in getting the right outputs. Initially, missing or inconsistent information in Azure DevOps could lead to incomplete insights. And because that same planning data informs leadership reporting, improving the quality at the
So, he built a second AI-assigned “hygiene” capability that continuously checks in-scope scenarios against a defined set of rules. It can identify data gaps and prompt team members with specific guidance on how to fill those gaps within Azure DevOps.
“The agent has driven the right behavior, and that’s honestly the key reason why our end-of-month reports don’t really require much human review anymore.”
Martin O’Flaherty, principal PM manager, Microsoft Digital
Together, the capabilities create a feedback loop: Better planning data produces better AI-generated insights, while AI continuously helps identify the data-quality issues that could undermine those insights.
“At the end of the day, the AI can only be as accurate as the data it has in front of it, and we were potentially making bad decisions based on that data,” O’Flaherty says. “The agent has driven the right behavior, and that’s honestly the key reason why our end-of-month reports don’t really require much human review anymore.”
Upskilling a diverse workforce
A common misconception with agents is that you need to be an AI expert to build one. O’Flaherty wanted to remove that barrier by making the process accessible to all product managers with a wide range of technical expertise. The goal was to teach people about AI and also encourage them to build capabilities against real problems in their own workflows.
As he and other team members developed their skills, they created short how-to videos of their process that broke the learning curve down into manageable steps. He began this effort by assuming the viewers knew nothing about the tools.
“We made a video on how you install Visual Studio Code, how you log into GitHub Copilot,” O’Flaherty says. “And then when you’ve done that, here’s how you access an MCP server.”
Those bite-sized explainers gave PMs a practical path into the tools and helped them to get comfortable applying what they learned directly to their own work. Rather than requiring everyone to become an AI expert first, the approach allowed people to start small, solve something useful, and build their skills through experience.
But as adoption grew, another challenge emerged: Product managers sometimes created multiple agents to solve similar problems. That presented a different scaling question: How do you encourage experimentation without recreating the same solution over and over again?
In response, the team began promoting reusable skills and encouraging PMs to build on existing work rather than starting from scratch.
“Our challenge was, how do we empower people to build without removing all the efficiencies?” O’Flaherty says. “We’ve partially solved that by creating a repository that gives people an easy way to upload stuff and encourages them to reuse where they can. Or to take the skill that’s there and customize it for their organization.”
That shift, from individual experimentation to reusable building blocks, helped turn AI adoption into more of a shared engineering discipline. PMs could still tailor capabilities to their own needs, but successful patterns could be reused, improved, and extended instead of repeatedly rebuilt.
Speeding up product planning and prototyping
For individual PM contributors, agents have made context gathering faster and simpler.
For example, Astha Sinha, a senior product manager on the IES team, says sorting through documents, meeting notes, and the various stacks her team leverages and turning that into reports could take hours every day. That was before she started using Copilot to summarize all the information for her.
Now she uses agentic skills to automate the reports that she sends to different PMs each month, further simplifying her everyday routine.
“One of the biggest benefits has been automation, which I’m empowered to achieve very easily,” Sinha says. “I support multiple partners, all with different reporting rhythms, so I created a set of skills that automatically generate reports for them on the right schedule.”
What used to be a recurring manual task for her now happens in the background. It’s reduced administrative overhead and freed up more time for strategic product work, making her workload lighter.
“Now, by making my agent aware of the code base and incident reporting, and stitching together the logs and the data models, I can get that insight faster without using engineering time. Then I can take it back to my customers and actually get down to what really matters to them.”
Owais Khan, senior product manager, Microsoft Digital
Senior product manager Owais Khan, who also works on the IES team in Microsoft Digital, has also set up agents to analyze usage data.
Previously, it might have taken him a few hours to sit down and look at where customers are clicking, what kind of APIs they’re using, and what their call patterns look like, then actually investigate improvements on that site.
“Now, by making my agent aware of the code base and incident reporting, and stitching together the logs and the data models, I can get that insight faster without using engineering time,” Khan says. “Then I can take it back to my customers and actually get down to what really matters to them.”
AI builder tools like Copilot Studio also provide a low-code solution for product managers that allows them to produce prototypes communicating their vision to engineers.
Nevedita Mallick, a principal product manager on the IES team, owns a lot of the UI experience on our corporate network. Her typical process for prototyping used to be to create a one-pager, then build out the idea manually in Figma.
“With Copilot Studio, it’s much faster,” Mallick says. “Things that used to take a day or two I can now be done in less than an hour, even in minutes. I can give a very rough sketch to Copilot Studio and it’s able to render the mockup really quickly. It also gives me the complete HTML with all the CSS and JavaScript, so I can just click through and explain to my developers, ‘This is what I want,’ or, ‘This is the enhancement I’m looking for.’”
Using AI-generated prototypes, PMs can also show customers interactive experiences earlier in the design cycle. This helps everyone align on requirements faster and reduces the amount of work required later in the development cycle.
The value of AI is amplified through thoughtful prompting and clear context.
“Prompting is everything,” Mallick says. “When troubleshooting a network issue, I asked Copilot to think like a network engineer so it could evaluate the problem through the lens of network topology, dependencies, and operational context. It moved from delivering generic answers to providing insights tailored to the problem at hand.”
The result, she says, is that the quality of your outcome is directly tied to the context and expertise you bring into the prompt.
“What once required specialized expertise and significant development time can now be done in minutes. For example, I recently created a custom skill for a specific business need in under five minutes, allowing me to move from idea to solution almost instantly.”
Astha Sinha, senior product manager,
Microsoft Digital
Practice truly does make perfect. Creating their first agents took some time, but the PMs say that the ramp-up period has significantly shortened the more they do it.
“What once required specialized expertise and significant development time can now be done in minutes,” Sinha says. “For example, I recently created a custom skill for a specific business need in under five minutes, allowing me to move from idea to solution almost instantly.”
The future of PM work
The PMs all agree that human oversight of agentic AI will always be necessary, even as they allow agents to take over some tasks.
O’Flaherty notes that even as his agent can estimate time spent on scenarios, there are still a lot of nuances in how productivity is evaluated.
For example, while the agent can predict how many hours a specific task might take, someone also needs to factor in who’s completing the task and how fast they can work. There’s also the need for a fact-checking pass. Because of these variables, O’Flaherty believes we still need humans to gather the full picture and oversee the process.
“We’ve become agent bosses—the agents do the work and we review and approve it,” he says. “We’re almost like a governance layer for the agents in how we do our work.”
“The real opportunity with agents isn’t just efficiency, it’s prevention. By embedding intelligence throughout the operational lifecycle, we can identify and address issues before they become incidents, fundamentally changing how we operate at scale.”
Nevedita Mallick, principal product manager, Microsoft Digital
PMs are also already seeing how their work has changed, and that it will continue to evolve in the age of AI. Mallick observes how AI is enabling product managers to have more room for deep, critical thinking and broader product vision work. She’s excited to see how agents work proactively down the line.
“The real opportunity with agents isn’t just efficiency, it’s prevention,” Mallick says. “By embedding intelligence throughout the operational lifecycle, we can identify and address issues before they become incidents, fundamentally changing how we operate at scale.”
The team’s ultimate goal isn’t to automate product management, but to make it easier for PMs to apply their expertise where it matters most.
As Microsoft Digital continues its important Customer Zero work, that mission is helping shape how product managers use AI to scale their impact while maintaining human judgment, collaboration, and accountability.
Key takeaways
Organizations looking to improve product manager productivity with AI should:
- Start with repetitive workflows. Begin with simple tasks that take time but don’t need heavy oversight, such as reporting, planning, status updates, and context gathering.
- Leverage existing systems. You don’t have to create entirely new data for AI to be effective—just guide it toward existing documentation and encourage it to pull from there.
- Be detailed in your requests. Telling AI who your audience is, how you like slides formatted, and what sources of truth should be incorporated are all important pieces of information when prompting a chatbot or creating an agent.
- Build off available skills. Consider whether an agent created by another team member could be tailored to your own needs, rather than building one from scratch.
- Establish governance and sharing practices early. The sooner compliance is incorporated, the more likely you’ll avoid duplication and error and be able to scale your workflows.
- Have an open mind. Like any new technology, these tools come with a learning curve. Understand that it takes time to familiarize yourself with agents; with practice, they will become easier and faster to use.
Try it out
- Try out Microsoft Copilot Studio for prototyping new features and fixes, or for gathering context quicker.
Related links
- Check out a guide for deploying AI agents, based on our own experiences at Microsoft.
- Delegate reporting and summarize dense, complex information with Microsoft 365 Copilot.
- Read about how we’re thinking about the value of agentic AI in the enterprise.
- Learn how AI agents are reshaping network operations at Microsoft.
- Get started with Azure DevOps and see how you can ship faster and plan smarter.
- Explore how GitHub Copilot can accelerate coding.
