Featuring Panelists:
Shane Sloan, Product Manager | Mobile Mentor
Tammara Edgin, Customer Dev. Mgr. | Mobile Mentor
Leaza Silver, Sr. AI Workforce Specialist | Microsoft
Adopting Microsoft Copilot isn’t just about turning on a new AI tool, it’s about preparing your data, your people, and your governance model so AI can actually deliver value. This session walks through the practical steps organizations can take to move from curiosity to confident, secure deployment.
Drawing on real-world experience supporting Copilot rollouts, the discussion focuses on what it really takes to make AI work inside an organization, especially in education environments where data sensitivity, compliance, and diverse user needs add complexity.
The Reality of Copilot Adoption
Many organizations start with excitement, then quickly run into the same roadblocks. This session highlights the most common challenges teams face when implementing Copilot, including:
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Defining meaningful use cases that align with business or institutional goals
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Addressing data security concerns, including sensitive information exposure and internal sharing controls
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Ensuring users are trained to use Copilot effectively, not just occasionally
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Understanding AI agents, how they operate, and how much autonomy they should have
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Establishing governance structures, policies, and ways to measure ROI
The message is clear: success with AI is as much about structure and strategy as it is about technology.
Education-Focused Use Cases That Matter
For schools, colleges, and universities, Copilot opens the door to both instructional and operational improvements. The session explores practical examples tailored to education, along with tools that help educators and staff get started faster, such as curated prompt libraries and real-world case studies.
Participants are encouraged to think beyond classroom use and consider how AI can also streamline internal processes (reporting, planning, communications, and research) where time savings can be just as impactful.
Securing Your Data Before Scaling AI
AI readiness starts with data readiness. A major focus of the session is protecting sensitive information such as student and staff records. Best practices discussed include:
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Strengthening content controls in collaboration platforms
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Using data labeling to classify sensitive information
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Limiting unnecessary internal sharing
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Building security into the foundation before broad AI access is enabled
Rather than slowing innovation, strong data governance actually enables safer, wider adoption.
Understanding Copilot Capabilities and Licensing
Not all Copilot experiences are the same. The session clarifies how different licensing approaches affect what Copilot can access and do.
One version operates primarily through the web and only works with files users explicitly provide. Another connects directly with organizational data inside Microsoft 365, unlocking deeper capabilities such as meeting intelligence, document collaboration, and integration with business systems. Flexible licensing models can support phased adoption strategies.
Understanding these differences helps organizations choose the right starting point and avoid surprises later.
Moving From Experimentation to Impact
The session closes by reinforcing that successful Copilot adoption is not a one-time IT project. It’s an ongoing journey that blends technology, people, and policy. Organizations that take a thoughtful, structured approach – starting with data protection, clear use cases, and user enablement – are the ones that see measurable gains.
AI is already reshaping how work gets done. The opportunity now is to deploy it with intention, confidence, and a clear path from data to real-world outcomes.
