Inside Microsoft’s marketing team: Scaling expertise with AI

Business leaders today are grappling with a well-known challenge, but it’s more daunting than ever before.

Every organisation is under pressure to adapt quickly as markets evolve, customer expectations soar, and technology develops at an almost dizzying speed. Teams are expected to produce even better results, often with the same resources as before.

Artificial Intelligence (AI) is stepping in to help meet these challenges. Some of the best examples I’ve come across focus on amplifying the judgment, strategy, and success metrics that top performers utilise for themselves and their teams. AI agents apply this expertise consistently across a growing workload, enabling teams to accomplish more without compromising on quality.

We’ve experienced this ourselves within our team. As the pace of innovation has quickened, we’ve moved from quarterly product launches to staging them weekly—and sometimes even daily. This has led our teams to support a whopping 150% increase in launch activity year-on-year.

To ease the burden, we’ve sought ways AI can assist Microsoft teams in quickly finding the right information, cutting down on repetitive coordination, and bringing more consistency to the work reliant on shared knowledge. For this, we leveraged Microsoft Foundry—our platform for developing and managing enterprise AI applications. This allows us to create AI agents that are rooted in business understanding and integrate seamlessly into our workflows, enabling our teams to scale effectively while staying focused on tasks that truly require their expertise.

Understanding the Importance of Context

One crucial lesson became apparent quite early on: AI’s effectiveness hinges on the data it can access. General-purpose AI can churn out content, but enterprise-level decisions rely heavily on information scattered across documents, workflows, business systems, communications, and institutional knowledge.

For us, Microsoft IQ was instrumental in linking our business context to our AI capabilities. Instead of having employees sift through multiple sources for information, our AI agents could draw from the same reliable knowledge people rely on every day. This helps surface the relevant details and facilitates more informed decision-making.

But Microsoft IQ does more than just ground AI in the right data; it also connects the knowledge and workflows that guide daily business operations.

This transformative shift changed how AI could support us. Instead of merely helping individuals find information, AI can now assist teams in working from a common understanding of what’s happening throughout the organisation.

However, context by itself wasn’t enough. The real breakthrough lay in fostering a collaborative environment where teams could enhance what was already proving successful.

As people started sharing effective agents and AI skills, it became much easier to reuse and scale expertise. Ideas that originated with one team were quickly adapted to benefit many others.

Microsoft Foundry played a key role in this, allowing us to base our agents on internal knowledge, link them to existing workflows, and extend their usage beyond individual teams.

In many respects, this illustrates a broader lesson in AI adoption. As Jay Parikh recently noted, “AI by itself won’t transform a business; it’s the system surrounding it that makes the difference.” The following examples will illustrate what this looked like for our marketing organisation:

Elevating Quality on a Larger Scale

With the expansion of our Microsoft Foundry business, the volume of content we produced surged. Our team now reviews and publishes over 200 blog posts each year, adhering to a consistent quality standard increasingly reliant on a select group of subject matter experts. Much of their efforts were spent applying the same review criteria repeatedly.

To improve efficiency, one of our content leaders drafted a detailed rubric for assessing strong blogs and refined it until it encapsulated our team’s expected standards.

Using Microsoft Foundry, we translated that expert-backed rubric into a repeatable process that could identify gaps and opportunities prior to reaching human review. This capability was integrated directly into the content creation workflow, delivering instant feedback on every draft and making expert-defined standards accessible to every content creator.

Review cycles that used to require significant manual effort can now be completed in minutes, resulting in greater satisfaction and an estimated annual savings of over 2,000 hours across our team. More importantly, this method showcases a broader strategy organisations can adopt in various areas: employ AI to implement established criteria at scale, allowing experts to dedicate their time to where their judgment, mentorship, and experience are most valuable.

What we’ve automated is consistency, not the exercise of judgment. Our team established the benchmark based on our expertise; the AI agent evaluates each post against that benchmark.

Testing Messaging Prior to Customer Interaction

As innovation speeds up, one recurring question arises: will our messaging resonate with the intended customers?

At Microsoft, we strive to keep customers at the heart of everything we do. This focus led us to explore methods for assessing messaging prior to its launch, relying on more than just internal opinions.

We implemented this strategy through the AI Messaging Assistant (AMA), which helps evaluate messaging and positioning from various audience perspectives before it goes to market. Instead of solely relying on internal feedback, teams have the opportunity to test whether a message is clear, relevant, and actionable for the stakeholders they aim to reach.

Utilising Microsoft Foundry, we established AMA with a virtual congress of personas based on actual customer conversations, complemented with the expertise, product details, messaging guidelines, and business context our teams depend on daily. This facilitated a transition from a sporadic AI trial to a repeatable workflow, enabling teams to evaluate messaging against a comprehensive understanding of audience needs instead of rebuilding this knowledge for every review.

The key insight here is leveraging AI to scrutinise significant decisions before they reach customers, partners, or employees.

Ensuring Team Alignment Amid Rapid Change

With the surge in launch activity across our business, maintaining team alignment became more challenging than the work itself. New announcements emerged daily, priorities shifted swiftly, and information became scattered across planning backlogs, documentation, meetings, and operational systems. Our marketers were spending excessive time piecing together context rather than acting on it.

To tackle this, we began by documenting how our marketing tasks are actually performed, turning vague processes into clear specifications. Armed with this information, we could categorise the work: identifying what required a marketer’s judgment, what could be automated, and what could be delegated to AI.

Utilising agents developed with Microsoft Foundry, we integrated the systems our teams already use, including planning backlogs, documentation, meeting alerts, and other operational data sources. Instead of manually collating updates from numerous locations, teams now operate from a real-time overview of critical developments, upcoming launches, and changes impacting go-to-market strategies.

This evolution transformed team alignment from a labor-intensive effort into a repeatable workflow. Rather than spending time gathering information, our teams focus on understanding what has changed, why it matters, and what actions to undertake next.

The struggle was never about a lack of expertise; it was about coordinating that expertise in a swiftly changing landscape.

The result isn’t just quicker communication—it’s improved organisational coherence. When teams operate from a shared foundation, decisions are made more swiftly, transitions are smoother, and organisations can respond promptly to changes.

Enhancing Expertise and Minimising Friction

In each of these scenarios, the aim wasn’t to automate for automation’s sake. The objective was to ensure expertise was accessible wherever it could add value. Reflecting on this, it wasn’t merely one AI capability that altered our work; rather, it was constructing the right system around these capabilities, which allowed expertise, context, and judgment to be scaled across the entire team.

As technology continues to evolve and the pace of business quickens, one truth remains unchanged: people provide the judgment. People dictate the strategy. People determine what success looks like. AI simply helps to amplify that.

Frequently Asked Questions

Q1: How can AI improve team productivity?
AI can streamline processes by automating repetitive tasks and providing instant feedback, allowing teams to focus on higher-value activities that require their expertise.

Q2: What role does context play in AI effectiveness?
Context is vital as it allows AI to pull relevant information from various sources, ensuring that the outputs are accurate and aligned with business needs.

Q3: Can AI really enhance decision-making?
Yes, AI can support and enhance decision-making by supplying data-driven insights, helping teams make informed choices that align with their objectives.