Gain insights from Altman Plants on how aligning processes, data, and AI at massive scale can reduce waste, increase sell‑through, and turn operational complexity into a competitive advantage.
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Learn how readiness gaps, strong governance, and organizational trust determine successful Copilot and AI adoption in this session on responsible AI.
This session shows how the Model Context Protocol streamlines AI adoption while strengthening data‑driven decisions, security, and compliance.
Gain insights from this demonstration on enterprise integration patterns, architectural design principles, and production strategies for data agent systems.
Through a customer-partner discussion, this session provides an honest look at an early-stage modernization and implementation journey.
Want to understand how to use Microsoft Fabric as the foundation for AI, analytics, and automation? Dive into the practical, hands-on guide in this session.
Vertex explores how AI and intelligent tax engines are transforming indirect tax compliance and how generative AI can help unify tax data across systems.
sa.global defines why combining Ai with deep industry expertise is the trigger for rewiring operations, decisions, and outcomes for businesses.
Celigo explains how it helps organizations close the Operational AI Gap through embedding AI into integrations and business processes.
AI is evolving from a feature to a living, breathing ecosystem of workflows. That’s prompting customers to shift from experimenting to re-platforming.
The Microsoft ecosystem is designed to scale in layers, so users should start with productivity, move into automation, and then expand into enterprise agents and intelligence. Understanding this progression will yield better, faster outcomes.
The Data and Fabric masterclass will deliver a practical roadmap for building a secure, AI-ready data foundation.
Because Microsoft controls identity, productivity, business apps, and cloud infrastructure, intelligence runs across the entire stack, creating a decision layer embedded throughout the enterprise.
Recent events in the US and Canada highlight the privacy-public safety dichotomy, as well as questions on the responsibility of AI firms in sharing signals of future harm.
CIOs need secure foundations, enterprise agreements, and clear data classification policies when it comes to AI tools. The solution: deliberate paths for specialization.
AI models can classify and analyze content to assess whether it is likely to be machine-generated, aiding in detection at scale. The Microsoft stack support a fraud-resistant-by-design security posture.
Industries such as healthcare can benefits from OneDrive agents grounded in internal policies, procurement, and workflows, thereby supporting entire teams at scale.
In the absence of a dedicated data expert, AI compressed what could have been a multi-day investigation into about an hour, then humans resolved the Fabric overload.
Winning with AI is about bigger models and resilient infrastructure. OpenAI is addressing both requirements by supporting millions of queries per second while managing user data at global scale.
Enterprise Microsoft licensing practices need to evolve into tiers that reflect the type of work being done, which will result in greater clarity, predictability, and accountability.