3 things leaders need to know from Microsoft Build 2026

Having witnessed significant advancements in technology like the internet, cloud computing, and now agentic AI, I’ve had a unique perspective. Prior to my time at Microsoft, I founded a systems integration firm, facing the complexities of discerning which technological trends would genuinely benefit organisations and whether they were adapting quickly enough.

These insights influence my perspective during transformative moments, such as now.

Each year, Microsoft Build showcases numerous updates that developers eagerly anticipate. Typically, the focus is on fresh capabilities for tech teams. However, this year’s announcements strike a profound chord: they are less about exploration and more about fulfilling the pressing need to transform how businesses function, compete, and achieve results.

If you’re not a developer, the Build conference can come across as overwhelmingly technical. It may not be immediately clear how these updates can lead to business growth or cost savings. Therefore, I’d like to share key takeaways for business leaders eager to grasp the essentials swiftly.

1. Your AI is only as effective as its understanding of your business

While models are crucial, the real advantage lies in how well AI comprehends your business—its unique data, processes, and operational nuances.

Whenever a team rolls out a new AI project, they encounter a familiar challenge: the AI lacks context. It doesn’t grasp your customers as your sales team does, nor does it understand your specific definitions of success, risk, or revenue. Hence, each new initiative starts from scratch.

This scarcity of shared context is a significant issue. If every AI project is reinventing the wheel, businesses lose precious time, consistency, and momentum. We focused on addressing this gap at Build.

Here’s how this translates into action: A unified intelligence foundation across your organisation.

Microsoft IQ introduces an enterprise intelligence layer where your data, processes, and knowledge are interconnected across all AI platforms. This way, new agents can come in with a clear understanding of your business and evolve with increased usage.

Thanks to Build, this shared intelligence foundation is now a reality. Work IQ helps AI comprehend how your team functions and how the business operates. Fabric IQ facilitates data integration across various systems, and Power BI fosters analytical capabilities. Furthermore, Foundry IQ ensures that deployed applications in Azure and custom sources operate with the same understanding of your business. Together, these tools create a cohesive context for AI to thrive.

We also unveiled Web IQ in limited preview, adding real-world context from outside your organisation.

These integrated layers enable agents to work with a unified understanding of the business across all systems used by your organisation. Once this context is in place, the next step is ensuring that models resonate with your specific business needs.

With features like Frontier Tuning, firms can optimise models using their own datasets and workflows, potentially trimming costs by as much as 10x while speeding up responsiveness.

This shift is particularly vital as we transition from AI that holds general knowledge to AI that is attuned to your unique circumstances. For business leaders, this represents a move from using generic tools to implementing systems personalised to your organisation’s operations—leveraging your data and expertise for a competitive edge.

Most firms have a patchwork of AI solutions—perhaps a pilot project here or an assistant there—but what they often lack is a comprehensive system designed for widespread deployment.

This distinction is crucial. Individual tools yield isolated results, whereas a system that nurtures context, maintains governance, and improves with time can yield far better outcomes.

This focus was paramount at Build this year, reinforcing our approach to Azure.

Here’s how this looks in practice: An integrated framework for creating, managing, and scaling agents effectively.

Rooted in Azure, the Microsoft Agent Platform consolidates everything organisations need to develop, manage, and scale agents across the enterprise. This foundation enables the transition from pilot projects to full production, targeting three common obstacles that often hinder progress.

First, there’s the challenge of speed. Transforming a promising prototype into a viable business operation can be slow. Rayfin addresses this by streamlining the path from concept to enterprise-ready deployment with built-in security, data management, and governance from the outset.

Second, there’s modernization. As AI starts interacting with core business systems, those systems must continuously evolve rather than undergo large, disruptive overhauls. New agentic features in Azure allow teams to update and improve applications constantly, ensuring systems keep up with business needs without hindering operations.

The third concern is trust at scale. As more agents enter production, robust governance and security become paramount. Azure integrates Microsoft Foundry, Agent 365, Azure Container Apps, and the broader Microsoft Security ecosystem to ensure that organisations run agents with built-in controls from the onset.

The leaders of this new era will be those not with the most AI tools, but with the best systems surrounding them.

3. Expectations have risen: AI must deliver tangible business results.

It’s easy to observe the announcements from Build and think of them as distant insights. However, your board or executive team may have a different viewpoint. There’s a scenario where business leaders observe the updates and think, that’s interesting, I’ll monitor the situation. Meanwhile, your board might already be planning several steps ahead.

Why is this? Because the pressing question of the previous year—does AI truly work?—has been effectively answered. The new question is: why isn’t it already playing a vital role in our operations?

In other words, there’s now an expectation for AI to produce measurable outcomes, such as quicker cycle times, reduced costs, and enhanced customer experiences—not just insights or trial runs.

Here’s how this looks in action: Enterprise-ready options, control, and flexibility.

Foundry now boasts the widest range of leading models in the market, from OpenAI’s GPT-5 series to the latest offerings from Anthropic and Fireworks AI’s open-weight collection—all fortified with security and governance. Additionally, we’ve entered the frontier model arena with a new range of enterprise-ready MAI models, allowing organisations greater control over costs, performance, and the specific applications of AI. The crux of this is not merely having model options but the capacity to adapt AI to suit your unique data, workflows, and requirements for better outcomes at reduced expenses.

This control becomes particularly critical when AI starts evolving beyond mere assistance into complex scientific and engineering tasks. Our Microsoft Discovery platform, designed for scientific research and multifaceted problem-solving, is now generally accessible. It employs specialized AI agents to sift through research, formulate hypotheses, run simulations, and refine outcomes in continuous cycles—drastically shortening timelines that traditionally spanned years to just months. This is a significant transformation that business leaders should be attentive to: AI is starting to significantly cut down the time needed for research, analysis, and iterations.

To facilitate this shift, our infrastructure is also evolving. The GPU-accelerated Fabric Data Warehouse offers up to 7x better query performance for AI-scale workloads versus three competing vendors for reporting and application functions at 64-user. Moreover, Azure Cobalt 200 VMs provide a purpose-built cloud system for AI-based tasks.

Furthermore, the Azure Infrastructure Resiliency Manager equips organisations with planning tools for resilience when AI begins managing actual operations.

The bottom line is readiness for production: equipping organisations with the control, agility, computing power, and resilience to run AI in the critical areas of their operations.

Your next steps for creating an AI-driven business

For me, the underlying theme here is that we’ve moved from experimentation to expectation.

AI is now woven into workflows, interlinked across systems, and anticipated to deliver substantial results.

This shift has immediate implications for business leaders. The conversation has shifted from whether AI is effective to where and how it should be integrated into your business now. This means using your next planning cycle to ask more operational questions:

• Where are we still treating AI as a one-off project instead of integrating it into core workflows?
• What areas require shared data and context to be effective before we introduce another tool or model?
• Which prototypes are ready for deployment, where genuine value can be realized?
• How are our AI initiatives directly linked to business goals like cost reduction, speed, and customer experience?
• In what areas should AI be making a significant impact on the business today, not just next year?

Your competitive advantage won’t stem from merely experimenting with AI. It will arise from how swiftly and effectively you implement it with a robust system founded on your own intelligence.