Featuring Panelists:
Shane Sloan, Product Manager | Mobile Mentor
Tammara Edgin, Customer Dev. Mgr. | Mobile Mentor
Leaza Silver, Sr. AI Workforce Specialist | Microsoft
AI adoption doesn’t fail because the technology falls short, it stalls when organizations try to scale without the right structure in place. This session breaks down the practical path to deploying Microsoft Copilot in a way that’s secure, purposeful, and measurable.
The conversation focuses on real-world lessons from supporting organizations through AI rollouts, with particular relevance for education environments where data sensitivity, compliance, and varied user needs raise the stakes.
Where Copilot Adoption Gets Stuck
Excitement about AI is easy. Turning that excitement into consistent value across an institution is the hard part. The session highlights five common friction points organizations encounter:
Finding the right use cases
Successful adoption starts with business analysis, not experimentation alone. Teams must identify where AI meaningfully supports goals, improves outcomes, or removes friction from daily work.
Preparing and protecting data
When AI is introduced, overshared or poorly governed data can quickly become a risk. Sensitive information (including financial data and personal records) must be identified, classified, and remediated before broad AI access is enabled.
Empowering users, not just enabling tools
Adoption rises when training connects directly to job roles and real tasks. Generic AI overviews rarely stick; practical, workflow-based enablement does.
Building and managing AI agents
AI agents can extend Copilot’s value, but they require thoughtful design, technical skills, and scalable architecture. Without a plan, agents become difficult to maintain.
Proving ROI while governing responsibly
AI initiatives need more than enthusiasm. Telemetry, ROI models, steering committees, acceptable-use guidelines, and autonomy policies help ensure AI is both valuable and controlled.
Together, these areas form a structured, multi-workstream approach that turns AI from a pilot project into an operational capability.
Understanding Copilot Experiences and Capabilities
Not every Copilot experience operates the same way, and knowing the difference shapes deployment strategy.
One version functions primarily through the web and works with files users choose to provide. Another connects directly with organizational data across Microsoft 365 — including collaboration spaces and documents — unlocking deeper capabilities such as integrated research, analytics, and broader agent functionality. Low-code tools also make it possible to build organization-wide agents, supported by flexible consumption models.
Clarifying these options helps institutions align licensing with their goals, security posture, and pace of adoption.
Seeing AI in Everyday Work
Live demonstrations illustrate how Copilot can support both instructional and operational scenarios. Examples include generating classroom materials such as assessments and answer keys, as well as automating scheduling tasks and producing structured research briefs.
These scenarios show how AI can act as a productivity partner, handling synthesis, drafting, and analysis at a scale that would otherwise consume hours of staff time.
From Curiosity to Capability
The key theme throughout the session is that AI success is intentional. Institutions that treat Copilot as part of a broader strategy — one that includes data protection, role-based enablement, agent planning, and governance — are better positioned to see sustainable results.
AI is becoming a core layer of how work gets done. The opportunity now is to deploy it thoughtfully, ensuring innovation and responsibility move forward together.
