The landscape of product management is rapidly evolving, and artificial intelligence is at the forefront of this transformation. As I have been navigating through the AI tooling that's available, it's becoming increasingly clear that AI isn't just a tool—it's reshaping how we think and approach not only product strategy, user insights, and decision-making, but how Product Managers can harness these tools to adapt to this changing landscape.
Building Efficiency with the use of AI Agents
As a Product Manager at Microsoft, I've discovered that the key to leveraging AI effectively isn't just about using existing tools—it's about creating custom solutions that integrate seamlessly with our existing workflows. That's where the Model Context Protocol (MCP) has become a game-changer for my daily productivity.
MCP servers allow me to create specialized AI agents that can access and process data from various sources in real-time. Instead of manually switching between different tools and dashboards, I now have intelligent assistants that can pull information from practically any API, such as GitHub repositories, customer feedback systems, and internal documentation to provide contextual insights exactly when I need them.
Inspiration from Microsoft Build: GitHub Copilot Agents
The catalyst for my MCP server development came during Microsoft Build conference, where GitHub Copilot Agents were introduced. Seeing the potential for custom AI agents that could understand and interact with data sources I could present as an API sparked my imagination. I realized that the same principles could be applied to product management workflows—creating intelligent agents that understand our specific data sources, processes, and business context.
What excited me most about the GitHub Copilot Agents announcement was the emphasis on context-aware AI that could work within existing developer workflows. I had already been experimenting with Copilot Agent Mode in Visual Studio Code Insiders, which gave me hands-on experience with how AI agents could seamlessly integrate into development environments. This inspired me to think beyond generic AI tools and start building specialized MCP servers that could seamlessly integrate with my product management processes.
Real-World Implementation: Custom MCP Servers
I've built several MCP servers that have transformed how I approach product management tasks and I'm starting to dive into the following areas:
- Customer Insights Server: Aggregates user feedback, support tickets, and usage analytics to identify product improvement opportunities
- Feature Analysis Server: Combines telemetry data with user research to evaluate feature performance and adoption patterns
- Competitive Intelligence Server: Monitors market trends and competitor releases to inform strategic decisions
- Documentation Assistant Server: Keeps product documentation synchronized with the latest requirements and specifications
Case Study: AI-Powered Automated Documentation
A recent project I've implemented involved updating an extensive product architecture to reference a newer version of technical requirements. This task traditionally would have taken weeks of manual review and editing across many documents.
The Challenge
We needed to update all existing documentation to reflect changes to an updated version of requirements. The documentation was created by a different team over several years that informed us that they can no longer maintain the documentation. Knowing that we have customers that would need to depend on this guidance, it was critical to figure out a plan and strategy for updating the documentation for customer consumption.
The AI Solution
I developed an MCP server that could:
- Analyze Existing Documentation: Parse and understand the context of current documentation
- Access Vectorized Content: Query a vector database containing the new version requirements and specifications
- Generate Updated Content: Intelligently merge existing documentation with new requirements
- Add Missing Context: Identify gaps and automatically generate additional relevant content
"The AI agent didn't just find-and-replace content—it understood the context and purpose of each document, ensuring updates were meaningful and comprehensive."
The Results
The AI-powered documentation update process delivered remarkable results:
- 95% Time Reduction: What would have taken 3-4 weeks was completed in 2 days
- Enhanced Accuracy: The AI caught inconsistencies and outdated references that human reviewers had missed
- Improved Completeness: Added 40% more relevant content that wasn't present in the original documentation
- Consistent Quality: Standardized formatting and terminology across all updated documents
Key Learnings from AI Tool Implementation
Through building and deploying these AI tools, I've learned several crucial lessons about implementing AI in product management workflows:
1. Context is Everything
The most successful AI implementations are those that understand the specific context of your work. Generic AI tools can be helpful, but custom MCP servers that understand your product, users, and business context provide exponentially more value.
2. Be Proactive and Curious about AI Tooling
AI now offers an additional approach to consider. Traditional thoughts and processes can really throttle you. Allow AI tools to help you think outside the box.
3. Human Oversight Remains Critical
While the AI could generate and update content at scale, human review and validation ensured the outputs met our quality standards and accurately reflected our product strategy.
Building Your Own AI-Powered PM Toolkit
Based on my experience, here's how other product managers can start building their own AI-powered efficiency tools:
- Start Small: Begin with a single, well-defined use case like automated report generation or data aggregation
- Leverage MCP: Use the Model Context Protocol to create specialized agents that understand your specific tools and data sources
- Focus on Integration: The most valuable AI tools are those that connect disparate data sources you already use
- Iterate Based on Usage: Monitor how you actually use the tools and refine them based on real workflow patterns
- Share and Collaborate: Work with engineering teams to ensure your AI tools align with broader technical strategies
The Future of AI-Augmented Product Management
As AI tooling continues to evolve, I believe the most successful product managers will be those who actively build and customize AI solutions for their specific needs. The future isn't about replacing human judgment with AI—it's about creating intelligent systems that amplify our capabilities and free us to focus on strategic thinking, user empathy, and creative problem-solving.
The documentation automation project was just the beginning. I'm now exploring how similar approaches can be applied to competitive analysis, user research synthesis, and strategic planning. The key is to think of AI not as a black box tool, but as a programmable capability that can be tailored to solve your unique product management challenges.
Conclusion
Building custom AI tools using MCP and other frameworks has fundamentally changed how I approach product management. By creating intelligent systems that understand our specific context and data sources, I've been able to automate routine tasks, improve decision quality, and focus more time on strategic initiatives that drive real product value.
If you're interested in learning more about implementing MCP servers or AI-powered documentation automation, I'd love to connect and share experiences. The future of product management is being written by those who are willing to experiment with these emerging capabilities today.