Business leaders are facing a familiar challenge at an unfamiliar scale. Every organization is being asked to move faster as markets change quickly, customer expectations continue to rise, and technology advances at a pace that can feel overwhelming. Teams are expected to deliver greater results, often with the same resources they had before.
AI is helping organizations meet those expectations. Some of the strongest examples I’ve seen revolve around scaling the judgment, strategy, and success measures that strong performers already set for themselves and their teams. AI agents apply that expertise consistently across a growing volume of work, helping them deliver more without sacrificing quality.
We’ve seen it firsthand on my team. As innovation cycles have accelerated, product launches have increased from a quarterly cadence to weekly—and sometimes even daily—events. Our teams are now supporting a growing volume of launches, up to 150% year over year.
To relieve the pressure, we’ve looked for places where AI can help teams at Microsoft find the right information faster, reduce repetitive coordination, and bring more consistency to work that depends on shared context. To do that, we used Microsoft Foundry , Microsoft’s platform for building and managing enterprise AI applications, to create agents grounded in business knowledge and embedded in the flow of work, helping our teams operate at greater scale while staying focused on the work where their expertise matters most.
Start building with Microsoft Foundry Why context matters One lesson became clear very quickly: AI is only as good as the data it has access to. General-purpose AI can generate content, but enterprise decisions depend on information spread across documents, workflows, business systems, communications, and institutional knowledge. For us, Microsoft IQ helped connect that business context to our AI capabilities.
Rather than asking employees to assemble information from multiple sources, agents could draw from the same knowledge people rely on every day to surface relevant information and support better decisions. But IQ does more than ground AI in the right data. It helps connect the knowledge and workflows that shape how the business actually operates.
That shift changed the role AI could play. Instead of simply helping people find information, it could help teams work from a shared understanding of what’s happening across the business. Learn how Microsoft IQ helps bring together people, data, knowledge, and workflows Context alone wasn’t enough.
The breakthrough wasn’t a single agent. It was creating a way for teams to build on what was already working. As people shared successful agents and AI skills, expertise started becoming easier to reuse and scale.
Ideas that began with one team could quickly create value for many others. Microsoft Foundry became important because it allowed us to ground agents in organizational knowledge, connect them to existing workflows, and operationalize them beyond a single team. In many ways, this reflects a broader lesson across AI adoption.
As Jay Parikh recently wrote, “ AI alone doesn’t transform a business. The system around it does .” The following examples show what that looked like inside our marketing organization: Raising the quality bar at scale As our Microsoft Foundry business grew, so did the volume of content we needed to create.
Our team now reviews and publishes more than 200 blog posts each year, maintaining a consistent quality bar increasingly dependent on a small number of subject matter experts. Much of their time was spent applying the same review criteria over and over again. Rather than reviewing every draft from scratch, one of our content leaders documented the rubric she uses to evaluate a strong blog and refined it until it reflected the standards our team expected.
Using Microsoft Foundry , we translated that expert-defined rubric into a repeatable workflow that could identify gaps and opportunities before content reached a human reviewer. The capability was integrated directly into the content creation process, bringing instant feedback to every drafted post and making expert-defined standards available to every content creator.
Review cycles that once required substantial manual effort can now be completed in minutes, resulting in higher satisfaction and over 2,000 estimated hours saved annually across the team. More importantly, the approach demonstrates a broader pattern organizations can apply in many domains: use AI to apply established criteria at scale so experts can focus their time where judgment, coaching, and experience create the most value.
What we automated is consistency, not judgment. Our team set the bar based on our expertise; the AI agent reviews every post against that bar. Validating messaging before it reaches customers As the pace of innovation accelerated, one question kept coming up: would our messaging resonate with the customers we were trying to reach?
At Microsoft, we aim to keep the customer at the center of everything we do. That led us to look for ways to evaluate messaging before it reached customers, using more than internal opinions alone. We applied that approach through AI Messaging Assistant (AMA), which helps evaluate messaging and positioning against different audience perspectives before going to market.
Instead of relying only on internal opinions, teams can pressure-test whether a message is clear, relevant, and actionable for the stakeholders they are trying to reach. Using Microsoft Foundry , we grounded AMA with a virtual congress of personas based on real customer conversations and extended it with the expertise, product knowledge, messaging guidance, and business context our teams rely on every day.
Originally published at azure.microsoft.com
