I’ve witnessed several major tech shifts firsthand—the rise of the internet, the cloud era, and now agentic AI. Before coming to Microsoft, I started a systems integration company, so I’ve been in the seat where you’re trying to determine which trend is genuine, what it means for your company, and whether you’re keeping up.
That background influences how I view moments like these.
Each year, Microsoft Build brings a flood of announcements that developers track closely. Most years, the focus is on new features for technical teams to experiment with. What sets this year apart is that these features feel less like experimentation and more like a mandate to transform how organizations function, compete, and achieve results.
If you’re not a developer, Build can seem highly technical, and it’s not always clear how the news translates into business value or cost savings. So I’d like to share some key insights for business leaders looking for a quick overview of what’s most important.
1. Your AI is only as good as what it knows about your business
Models are important, but sustainable competitive edge increasingly depends on how deeply AI grasps your business—your proprietary data, your workflows, and how your company actually runs.
Every time a team launches a new AI initiative, they hit the same wall—the AI begins with zero context. It doesn’t know your customers the way your sales team does. It doesn’t grasp your definitions of revenue, risk, or success. And so, every initiative starts from zero.
That’s why context has become a scaling bottleneck. If every AI project has to reconstruct the same groundwork, organizations sacrifice time, consistency, and momentum. That’s the problem we aimed to solve at Build.
What this looks like in practice: A shared intelligence foundation for your entire organization.
Microsoft IQ introduces an enterprise intelligence layer where your data, processes, and organizational knowledge maintain live connections across every AI system, so new agents can begin with an understanding of your business and get better as usage increases.
That shared intelligence layer went from concept to reality with general availability. Work IQ helps AI understand how people work and how the business functions. Fabric IQ connects business data across systems and Power BI. Foundry IQ extends that grounding into deployed applications in Azure, unstructured data, and custom sources. Together, they enable agents to operate from the same business context across the systems your organization depends on.
We also introduced Web IQ in limited preview as the newest addition to the layer, bringing real-world context from outside the organization.
Together, these layers enable agents to operate from the same business context across the systems your organization depends on. With that shared context established, the next step is making the models themselves reflect your business.
And, with capabilities like Frontier Tuning, organizations can fine-tune models using their own data and workflows, cutting costs by up to 10x while boosting response speed.
This is especially meaningful because we’re shifting from AI that knows a lot about the world to AI that knows a lot about your world. For business leaders, that’s the difference between a generic tool and a system that mirrors how your organization actually operates—leveraging your own data and expertise with AI systems for competitive advantage.
Most organizations have assembled a patchwork of AI tools. A pilot here, an assistant there, a proof of concept that performed well enough to grow. What they haven’t created yet is an industrialized system built for end-to-end production at scale.
The difference matters. Individual tools deliver individual outcomes. A system that shares context, enforces governance, and grows smarter over time.
This was a central theme at Build this year, and it’s core to how we’ve built Azure.
What this looks like in practice: An integrated platform for building, running, and governing agents at scale.
Built on Azure, the Microsoft Agent Platform brings together what organizations need to build, run, govern, and scale agents across the business. It’s the foundation for moving agents out of pilots and into production—and it’s designed to solve three challenges that consistently slow that transition.
The first challenge is speed: moving from a promising prototype to something the business can actually operate. Rayfin helps close that gap by making it easier to go from concept to enterprise-grade deployment, with security, data management, and governance built in from the start.
The second challenge is modernization. Once AI starts touching core business systems, those systems need to evolve continuously, not through large, disruptive transformation cycles. New agentic capabilities in Azure help teams update, integrate, and improve applications in parallel and on an ongoing basis, so systems can keep pace with the business without slowing operations down.
And the third challenge is trust at scale. As more agents move into production, governance and security need to be part of the system from the beginning. That’s why Azure brings together Microsoft Foundry, Agent 365, Azure Container Apps, and the broader Microsoft Security stack to help organizations run agents with controls built in from the moment they start operating.
The winners of this era won’t be the organizations with the most AI tools. They’ll be the ones that build the best system around them.
3. The bar has moved. AI is expected to deliver real business outcomes.
It would be easy to view the Build announcements as something to observe from a distance. But your board or C-Suite might have a different perspective. There’s a version of this moment where business leaders read the Build announcements and think, interesting, I’ll keep watching. Your board or C-suite
They might already be multiple steps ahead.
Why? Because the question organizations were posing a year ago—does AI actually work?—has already been settled. Today’s question has shifted: why isn’t it powering major parts of our business yet?
Put simply, AI is now expected to produce tangible results—such as shorter cycle times, reduced costs, and better customer experiences—rather than just providing insights or serving as an experiment.
What this means in practice: Enterprise-grade choice, control, and resilience.
Foundry now provides the widest range of frontier models available in the industry—from OpenAI’s GPT-5 series to the newest offerings from Anthropic and Fireworks AI’s open-weight portfolio—all with security and governance built in. We also stepped into the frontier model arena at Build with a new family of enterprise-ready MAI models, giving organizations greater control over cost, performance, and how AI is tailored to specific business use cases. The business value isn’t just about having model options. It’s about the ability to mold AI around your own data, workflows, and requirements so it can generate better results at a lower cost.
That level of control becomes most critical when AI moves past simple assistance and into deep scientific and engineering work. Microsoft Discovery, our agentic AI platform for scientific research and complex problem-solving, is now generally available. It leverages specialized AI agents to surface through research, formulate hypotheses, execute simulations, and refine outcomes in ongoing cycles—turning timelines that once took years into months. This is the transformation business leaders need to focus on: AI is starting to dramatically shorten the timeline for work that previously required lengthy cycles of research, analysis, and iteration.
To enable that transformation, the underlying infrastructure is evolving as well. GPU-accelerated Fabric Data Warehouse delivers up to 7x faster query performance for AI-scale workloads, compared to three comparable external vendors for reporting and application workloads at 64-user concurrency. Azure Cobalt 200 VMs deliver purpose-built cloud infrastructure designed for AI-native workloads.
And Azure Infrastructure Resiliency Manager helps organizations build resilience planning into their operations when AI is running real, production workloads.
The bottom line is production readiness: equipping organizations with the control, speed, compute power, and resilience they need to run AI in the parts of the business where performance truly matters.
Your next step to build an AI-powered business
For me, the common thread is how expectation has taken the place of experimentation.
AI is now woven into workflows, connected across systems, and expected to deliver meaningful business outcomes.
For business leaders, the implication is both strategic and urgent. The question is no longer whether AI works, but where and how it should be operating in your business right now. That means using the next planning cycle to ask a more operational set of questions:
- Where are we still treating AI as a standalone pilot instead of integrating it into core workflows?
- Where do we need shared data and context before adding another tool or model will truly make an impact?
- Which prototypes are ready to transition into production, where real value can be captured?
- Which AI initiatives are directly linked to business outcomes like cost reduction, speed, and customer impact?
- Where should AI be powering meaningful parts of the business today, rather than next year?
Your competitive edge won’t come from experimenting with AI. It will come from how quickly you put it to work with a robust system grounded in your own intelligence and running on a foundation you can trust.
