Visualizing success: Steering your AI deployment with a strategy council

The pace of change when it comes to AI’s impact on business today is astounding. Companies are scrambling to develop and maintain a cohesive strategy for managing this impact and getting the most out of this revolutionary technology.

At Microsoft Digital, the company’s IT organization, we’re using a set of employee councils to guide how we deploy and adopt AI across our organization. We took this approach for a simple reason: We need a model that can keep pace with technological change while staying grounded in business value.

Our baseline expectation for AI at Microsoft is practical.

Our AI initiatives need to deliver value every quarter, and we track progress through KPIs reviewed monthly at the leadership level. That standard creates healthy pressure. It also exposes a common gap many organizations experience in the beginning stages of their AI efforts: It’s easy to generate a lot of activity without producing business results.

“Our strategy council is how we separate signal from noise in our AI acceleration. It identifies the top scenarios with the greatest enterprise leverage, sharpens our executive focus on what truly matters, and enforces a one-to-one alignment between the work we resource and the outcomes we’re accountable to deliver.”

Don Campbell, principal group technical program manager, Microsoft Digital

In our council-based approach to AI, different councils focus on different needs. Together, they help us move from experimentation to repeatable, enterprise-grade outcomes. We think of these councils as building blocks that we can combine and evolve as the technology, the business, and our operating model change.

In this model, AI strategy needs its own council to help guide the overall approach and align our efforts across the enterprise. At the highest level, the strategy council is where we prioritize what matters most, decide how it maps to the outcomes we’re accountable for, and determine how we’ll judge progress month over month.

“Our strategy council is how we separate signal from noise in our AI acceleration,” says Don Campbell, a principal group technical program manager in Microsoft Digital. “It identifies the top scenarios with the greatest enterprise leverage, sharpens our executive focus on what truly matters, and enforces a one-to-one alignment between the work we resource and the outcomes we’re accountable to deliver.”

Strategy keeps our AI conversation at Microsoft from getting bogged down in discussions of tools and technology and forces us to keep our focus on the main goal: What are we trying to change in the business, and how will we know if we’ve succeeded?

“We need a single cohesive story to bring together what’s happening across the organization and how those efforts contribute to real impact. The goal is to stitch that story together and solve for redundancies—if one part of the org has already solved a problem, another team shouldn’t have to reinvent the solution.”

Mohit Chand, principal group engineering manager, Microsoft Digital

AI strategy in action: Focus, alignment, and a monthly cadence

As our AI work at Microsoft accelerates, we continuously balance two truths at the same time. We want broad experimentation, because it’s how teams and employees learn fast. At the same time, we want our people to focus on what matters most to our enterprise and to ensure we are identifying and reducing potential redundancy.

Maintaining this balance is the core work of our AI strategy council. It helps us identify the AI-enabled scenarios that will deliver the most value, then keeps us honest about whether we’re delivering against the outcomes we’ve committed to.

“We need a single cohesive story to bring together what’s happening across the organization and how those efforts contribute to real impact,” says Mohit Chand, a principal group engineering manager in Microsoft Digital. “The goal is to stitch that story together and solve for redundancies—if one part of the org has already solved a problem, another team shouldn’t have to reinvent the solution.”

We have a detailed process that relies on engaging with our subject matter experts to keep the most impactful AI portfolio visible and actionable. We use it to summarize and track our top scenarios. Our AI strategy council views this process as work that’s always in process—a living view that changes as products ship and priorities shift. Delivered items come off, emerging bets go on, and the continuing discussion stays anchored to our goals.

“The pace right now is incredible. There’s a lot of excitement, but there’s also a risk if it’s not sustainable. A big part of our focus is figuring out how to take churn out of the system and make this work long‑term—for the business and for our people.”

Myron Wan, principal group product manager, Microsoft Digital

A tight rhythm and monthly cadence ensures that our conversations stay focused on whether the biggest bets are moving the needles we care about. That cadence helps us answer the questions leaders and customers are asking on a regular basis:

• Where are you investing?
• Why?
• What’s working?
• What would you do differently next time?
• What did you learn along the way?
• Where are we reinvesting and creating additional agency or capabilities for our employees?

When these questions frame the conversation, the outcomes naturally align to the direction our enterprise wants to go.

Structuring strategy and execution

To make our strategy council effective, we needed more than just a monthly meeting. We needed a way to organize work, assign accountability, and compare progress across very different teams without forcing everyone into the same mold.

We use three practices to accomplish this:

• Group work into clear focus areas
• Rely on product owners to drive execution
• Use a shared approach for measuring value

“The pace right now is incredible,” says Myron Wan, a principal group product manager in Microsoft Digital. “There’s a lot of excitement, but there’s also a risk if it’s not sustainable. A big part of our focus is figuring out how to take churn out of the system and make this work long‑term—for the business and for our people.”

Working into focus areas

When we started to scale our initial AI efforts, our first challenge was simple: Everyone is building, but not always toward the same destination. That’s why we split the work into two primary focus areas that match how an IT organization operates. These areas include:

• AI for corporate functions. Our AI work supports teams like finance, legal, and HR. We focus on removing friction from core processes and helping people make faster, better decisions.
• AI for IT. We support AI initiatives across our IT operations in several areas:
  • Network and devices. We’re using AI for faster network device lifecycle management, more efficient incident management and remediations, and lower costs
  • Employee experience. We want to enable Microsoft employees to contribute real business value and enjoy how they do it.
  • Support. We’re reducing tickets, resolving issues faster, and helping support teams stay ahead instead of reacting.
  • Tenant management and security. Our AI investments strengthen how we run and protect our Microsoft 365 tenant.

From there, we map AI initiatives into those focus areas so we can see what’s happening across the landscape and spot gaps, overlaps, and opportunities to reuse what already exists.

“We operate a council which helps set direction, but product management oversees execution of the solutions. Without product management’s ownership, our council would degrade into just a low-level approval step, which quickly makes us a roadblock instead of an enabler.”

Bill O’Brien, principal group product manager, Microsoft Digital

This step sounds basic, but it changes the conversation. It moves us away from a list of disconnected projects and toward a portfolio view, where we can figure out which scenarios matter most, where we have duplication, and where we need to invest more.

Keeping execution with product owners

While our AI strategy council sets direction, execution lies strictly with our product owners. A strategy council can’t run delivery. If it tries, it slows everything down. We avoid that trap by separating direction from doing.

“We operate a council which helps set direction, but product management oversees execution of the solutions,” says Bill O’Brien, a principal group product manager in Microsoft Digital. “Without product management’s ownership, our council would degrade into just an approval step, which quickly makes us a roadblock instead of an enabler.”

This clarity on roles and responsibilities helps teams work fast and ensures the council remains strategic. Product owners can prioritize week by week, learning from usage, adjusting product features, and shipping value. The council can stay focused on the portfolio and which bets rise to the top, what tradeoffs to make, and how we communicate progress and business outcomes to leadership.

“The first part of our strategy was all about getting people to a point where they could identify what they were trying to accomplish and report on how they’re getting there. We created a value measurement framework in partnership across multiple key players to give teams an idea of what’s valuable to the organization.”

Keith Bunge, principal software engineer, Microsoft

Using a common value framework

Once we can see the portfolio and have identified clear ownership, we still need one more thing: A shared language for determining value. Early in our journey, we were tempted to declare success simply based on activity—how many pilots we launched, how many tools we built, or how many demos we could show.

That activity is critical for innovation, but it doesn’t help us understand and drive business value. We needed teams to define the value they expect to deliver, explain why, and show how they’ll measure it.

“The first part of our strategy was all about getting people to a point where they could identify what they were trying to accomplish and report on how they’re getting there,” says Keith Bunge, a principal software engineer at Microsoft. “We created a value measurement framework in partnership across multiple key players to give teams an idea of what’s valuable to the organization.”

That framework helps in two ways:

1. It forces upfront discipline: Teams clarify what value they’re chasing and how they’ll prove they’ve achieved it.
2. It allows for fair comparison across very different initiatives: Everyone is describing impact in consistent categories, rather than inventing a new scorecard each time.

As our approach matures, we’re also pushing past raw savings metrics to the harder question: What did we do with the time or money we saved, and how did this create increased agency or capabilities?

Combining strategy and execution: A practical example

Here’s how that looks when we apply this approach to a real-world scenario.

Say one of our teams is proposing an AI solution to automate energy management in buildings. On day one, the idea sounds great: use signals from internal temperature and movement sensors to automatically adjust HVAC usage across large buildings. But the role of the strategy council isn’t just to approve great ideas. We ask for a clear value claim and a measurement plan.

Bunge provides a solid value claim for the example above.

“I’m going to come up with an automation that allows me to automatically turn off air conditioning in a building based on signals that we have from our internal sensors,” he says. “I think I’m going to be able to save $100,000 a quarter with this project because of my usage projections overlaid on the HVAC costs over the past five years.”

That kind of statement is useful, because it’s specific. It also forces the next question: How do you prove it? We’re asking teams to explain what data they’ll use as a baseline, what counts as savings, and how they’ll report progress over time.

We’re also raising the bar as the program matures.

Early on, teams may be able to prove that they saved time or reduced effort. As we get more rigorous, we’re pushing the “so what” conversation: What happens with the time saved, and what changes in the business as a result? It’s all part of moving from value measures to business outcomes, including what gets reinvested and where impact actually accrues.

Connecting AI strategy to the rest of our councils

Our AI strategy council is not the final measure or a standalone solution. We use it as the front door to a broader ecosystem that helps us move AI from ideas to enterprise outcomes.

“Business strategy needs to lead the AI strategy. Business strategy defines the ‘what and why.’ AI defines the ‘how’ to get the business strategy implemented with real value. We need to use AI to help us achieve the business strategy, not the other way around.”

Qingsu Wu, principal group product manager, Microsoft Digital

Here’s how it fits together in practice. We use the strategy council to set our direction, and we keep a short list of top scenarios visible. Then we rely on complementary councils and capability groups to make those scenarios real: teams are building skills and patterns through enablement, strengthening foundations through data readiness, and applying Responsible AI practices so solutions scale safely.

We use process improvement and change management to drive adoption, because a strong model doesn’t matter if people don’t change how they work. And we use metrics and value tracking to keep the entire system accountable.

We’re also keeping a clear principle at the center: Business strategy leads, AI follows.

“Business strategy needs to lead the AI strategy,” says Qingsu Wu, a principal group product manager in Microsoft Digital. “Business strategy defines the “what and why.” AI defines the ‘how” to get the business strategy implemented with real value. We need to use AI to help us achieve the business strategy, not the other way around.”

That distinction matters as AI capabilities keep expanding and as teams continue to move faster.

Moving forward

As this work matures, one thing is clear: Strategy isn’t something we finish and move on from. It’s something we’re actively maintaining as AI adoption accelerates.

What we’ll do next is consistent with that mindset.

We plan to keep scaling what works while tightening and improving the system around it. We’re strengthening alignment across teams, pushing for more consistent measurement of impact, and sharpening how we choose the right approach for the right problem. We’re also treating strategy as a living motion, not an annual document, because business and technology are constantly changing.

We know that what got us here isn’t going to get us where we need to go next. We’re excited about the continued evolution of AI strategy here at Microsoft Digital as we focus on scale, alignment to real business problems, and making sure the pace is sustainable for our business.