Example scenario
A company plans to expand its use of AI-powered productivity tools. Rather than allowing individual departments to adopt AI independently, the IT team establishes policies for deployment and management first. Using Microsoft Intune, the team centrally manages configurations, application settings, and access policies for approved AI experiences on Windows 11 devices. The organization also uses Microsoft Agent 365 to help observe, govern, and secure AI agents as they're introduced across the business. New agents are onboarded through IT-controlled workflows, and security policy templates are applied to help ensure agents are secure, governed, and compliant from the start.
The organization determines which experiences and agents will be available, how they'll be managed, and how employees will access them. By establishing consistent policies and oversight, IT can support AI adoption while maintaining visibility into how AI capabilities are used.
As a result, employees receive a consistent set of approved AI experiences, while IT maintains visibility and control over how those capabilities are used across the organization. This creates a foundation for broader AI adoption without introducing unnecessary complexity.
Reflect
Think about your own organization.
If new AI tools were introduced tomorrow, what governance policies or management controls would need to be in place to ensure those tools are used consistently, responsibly, and in alignment with organizational requirements?
Key takeaway
Organizations that successfully scale AI don't focus solely on deploying new capabilities. They also establish governance processes that help ensure AI experiences are consistent, manageable, and trusted. By creating this foundation first, organizations can support innovation while maintaining control over how AI is introduced and used.