Mohammad Bapu
Systems & Functional Lead - D365, Power Platform & Copilot | AI Enablement & Adoption | Speaker | Creator
Toronto, Canada
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Mohammad is a Functional Consultant at Hypertec Group, specializing in Dynamics 365, Power Platform and Microsoft 365 Copilot. He helps organizations drive digital transformation - AI adoption, streamline business processes, and implement governance frameworks that balance innovation with compliance. With hands-on experience building AI-powered solutions, automations, and copilots, Mohammad is passionate about making Microsoft AI tools accessible, practical, and impactful for diverse teams. A community mentor & technology advocate, he enjoys sharing his knowledge to empower users and organizations to innovate responsibly.
Area of Expertise
Topics
Shadow AI: Governing AI Without Slowing Innovation
Generative AI tools are being adopted rapidly across organizations, often without formal approval, oversight, or governance. While this “Shadow AI” is typically driven by a desire for productivity, it introduces significant risks, including unintended data exposure, compliance gaps, and loss of control over sensitive information.
With the rise of Microsoft 365 Copilot, these risks evolve. AI is no longer just external, it is embedded inside the digital workplace, operating on top of existing permissions, data structures, and collaboration patterns. This means organizations must rethink how they approach governance: not by blocking AI, but by understanding and guiding its use.
This session explores a practical approach to managing Shadow AI in both external tools and within Copilot. It introduces a lightweight governance model focused on detection, risk understanding, policy-driven control, and user guidance. Attendees will learn how to balance innovation with control & enabling employees to safely leverage AI while protecting organizational data.
Scaling Public Services with GenAI: Lessons from Montréal’s Copilot Studio Implementation
Using the City of Montréal’s Copilot Studio implementation as a real-world case study, this session explores what it takes to deploy conversational AI at scale in a public-sector environment.
We’ll examine how the city enabled natural-language access across 40,000+ webpages and connected APIs using Microsoft Copilot Studio and generative AI to improve citizen engagement, service accessibility, and operational efficiency.
Beyond the technology itself, attendees will learn practical lessons around AI-friendly content design, Bing indexing, enterprise search, API integrations, governance, analytics, and responsible AI considerations. The session will also highlight the collaboration model between business and IT teams that helped support scalable AI adoption.
Drawing from hands-on experience with Power Platform and Microsoft AI implementations, I’ll translate these lessons into actionable enterprise strategies for building compliant, impactful conversational AI experiences across organizations.
Attendees will leave with practical implementation patterns, governance insights, and real-world lessons for deploying enterprise-ready copilots that improve user experience while balancing compliance and operational needs.
Where Enterprise AI Actually Breaks - 5 Lessons from Real Implementations
Most enterprise AI initiatives don’t fail at the idea stage; they fail during implementation. In this session, I’ll share 5 key lessons from real-world AI adoption inside organizations, focusing on what happens after rollout.
We’ll explore common breakdown points, including low user adoption, unclear data ownership, and AI tools that don’t align with daily workflows. I’ll walk through real scenarios where initial approaches didn’t work, the constraints we faced, and the adjustments that delivered measurable results.
Attendees will leave with practical guidance on how to avoid common pitfalls, integrate AI effectively, and drive adoption across teams. This session is about decisions, trade-offs, and actionable strategies not theory/generic tool demos.
Why Enterprise AI Projects Fail and the Data Strategy Mistakes Behind It
Enterprise AI initiatives often fail not because of the technology, but due to misaligned data strategies, weak governance, and poor integration with business processes. Many organizations invest in AI tools expecting quick results, but without a strong data foundation and clear direction, these efforts struggle to deliver real value.
In this session, I will share practical insights from helping organizations adopt AI, improve workflows, and implement governance frameworks that balance innovation with compliance. The focus will be on common data strategy mistakes, including fragmented data, unclear ownership, and lack of alignment between business and technology teams.
The session will also explore how to integrate AI into everyday workflows to improve adoption and ensure it supports real business needs. In addition, attendees will learn simple ways to measure success through outcomes such as efficiency, productivity, and return on investment.
By the end of this session, participants will gain a clear understanding of why enterprise AI projects fail and how to design data strategies that enable successful, responsible, and scalable AI adoption across teams.
AI Adoption with Microsoft 365 Copilot
Microsoft 365 Copilot is transforming productivity, but successful adoption requires more than just enabling the feature, it requires strategy, enablement, and trust. In this session, we’ll walk through practical approaches to introducing Copilot into organizations, from running small pilots to scaling company-wide. Attendees will learn how to engage early adopters, create champions, and address concerns around security, compliance, and change management. Real-world examples will illustrate how IT teams and business leaders can measure Copilot’s impact on productivity while building confidence in AI-powered work.
AI Community Days (AICD) Québec 2026 Sessionize Event Upcoming
AI Community Days (AICD) Montréal 2026 Sessionize Event Upcoming
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