In this episode of the M365 Show, Mirko Peters speaks with Elliot Margot, Microsoft MVP for Microsoft 365 Copilot and Copilot Studio Team Lead at Witivio. Elliot shares a practical perspective on taking AI from an impressive demo to a secure, governed and genuinely useful enterprise solution. The conversation covers Microsoft Copilot Studio, multi-agent systems, enterprise RAG, MCP, Power Automate, governance, cost control and the human side of AI adoption.
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FROM CHATBOTS TO ENTERPRISE AI AGENTS
The gap between a chatbot demo and a production-ready agent is much larger than it first appears. An enterprise agent needs a clear purpose, reliable data, carefully scoped tools, sensible fallback paths and a way for people to understand what it is doing. Elliot explains that organisations should not wait for a perfect governance model before experimenting—but they also cannot deploy AI blindly. The strongest approach is to learn by building small, useful solutions while steadily improving guardrails, monitoring and operating models.
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GOVERNANCE IS A JOURNEY, NOT A BLOCKER
Governance, security and compliance are often the reasons enterprises hesitate to start with AI. Elliot makes the case for a balanced approach: establish the foundations, understand where data goes, apply Data Loss Prevention policies and sensitivity labels, but keep moving. Companies gain the most useful governance insights from real usage. AI evolves quickly, so governance cannot be treated as a one-time project; it requires ownership, continuous learning and administrators who know where to find the right controls across Microsoft 365, Power Platform, Purview and Copilot administration.
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SELLING AI THROUGH REAL BUSINESS VALUE
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Executive sponsorship is not only about promising headcount reduction. The better conversation is about improving service, reducing repetitive work and giving teams more time for work that requires judgement and human connection. Elliot uses the example of IT support: even a modest reduction in repetitive Level 1 tickets can create meaningful value. The most convincing AI projects combine a clear business case with a strong “wow” moment that helps people understand what is now possible.
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WHY MULTI-AGENT SYSTEMS MATTER
A single general-purpose agent can attempt many tasks, but specialised agents can deliver more reliable results. Elliot describes a multi-agent approach where different agents take on distinct roles, such as creating content, reviewing quality, checking user experience, validating requirements or orchestrating a workflow. Instead of expecting one model to get everything right on the first attempt, a multi-agent system can improve, audit and refine its work. This is how AI starts to resemble a coordinated digital team rather than a simple prompt-and-response experience.
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RAG, METADATA AND BETTER KNOWLEDGE RETRIEVAL
Enterprise AI is only as useful as the information it can retrieve. Elliot explains why metadata is essential for effective RAG implementations. Documents should have clear descriptions, languages, classifications and relevant tags so an agent can retrieve the right source quickly and avoid unnecessary token consumption. A large collection of poorly structured PDFs, duplicate files and outdated versions creates slow, expensive and unreliable answers. Good knowledge architecture means that the current approved information is available to the agent, while old versions are kept out of the production knowledge source.
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MCP AND CONNECTING AGENTS TO THE ENTERPRISE
Model Context Protocol, or MCP, is becoming an important way to connect AI agents with enterprise tools and APIs. Elliot explains MCP as a structured, discoverable bundle of capabilities that tells an agent what tools are available and how to use them. Instead of treating every API as an isolated endpoint, MCP can help package connections in a more consistent, secure and reusable way. For enterprise AI, this matters because agents need to work with real business systems—not just generate text in a chat window.
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COPILOT STUDIO, POWER PLATFORM AND PRODUCTION READINESS
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Copilot Studio opens AI development to more people, but building faster does not remove the need for responsibility. Elliot discusses the growing role of citizen development, prompt-driven building and AI-assisted creation across Power Platform. He also stresses that every production solution must be tested properly. Automated test prompts are valuable, but manual testing remains essential. Do not assume that an agent is ready for production simply because another AI says the workflow looks correct. Human review, scenario testing and clear ownership remain vital.
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DLP, PURVIEW AND KEEPING AGENT SCOPE SMALL
A strong security model starts with scope. If an agent is meant to summarise Teams meetings, Outlook messages and daily tasks, it should only have access to the relevant services. Elliot recommends separating use cases into appropriate Power Platform...