Imagine this: you work at a company, and you have a clear idea for a web app, something like a custom expense tracking tool that fits exactly the way your team works. You’ve tried off-the-shelf products, but they always miss the mark. The alternative, building your own application, is usually time-consuming, expensive, and requires getting developers involved early on. But what if you could prototype your idea, adjust the UI, change the data model, and publish it, all without writing a single line of code?

AI is no longer an abstract promise of the future. It’s here, embedded into enterprise workflows, products, and decision-making processes. From Microsoft Copilot to ChatGPT and domain-specific assistants, businesses are adopting AI at an unprecedented pace. But too often, “AI” is used as a catch-all term for a wide range of technologies. To lead the next transformation wave, organizations must move beyond generic AI adoption and toward agentic AI, a more autonomous, goal-driven form of AI that’s ready to take on real work.

In today’s AI era, you’ve likely interacted with Microsoft Copilot, ChatGPT, or Claude.ai, tools powered by advanced Large Language Models (LLMs). These models excel at understanding and generating human-like text based on vast amounts of training data. However, while LLMs are impressive at reasoning and answering general questions, they fall short when it comes to performing real-world tasks or retrieving live, domain-specific information.

This is where the Model Context Protocol (MCP) comes in.

The logistics industry constantly seeks efficiency in routing vehicles to deliver goods. This is where the Vehicle Routing Problem (VRP) and Pickup and Delivery Problems (PDP) come into the picture as both are sophisticated extensions of the classic Traveling Salesperson Problem (TSP). The TSP poses a simple yet challenging question: “What is the most efficient route that visits each destination once and returns to the starting point?” This problem is not just academic; it has practical applications in logistics, where optimizing routes can lead to substantial savings in time and costs.

We are thrilled to announce the unification of Bing Maps for Enterprise (BME) with Azure Maps, marking a significant milestone in our geospatial services at Microsoft. Azure Maps now boasts a robust stack of geospatial offerings, leveraging the powerful capabilities of Microsoft Maps, which also drives Bing Maps (our consumer maps experience). Over the past year, our team has dedicated significant time and effort to combine the strengths of Bing Maps for Enterprise into Azure Maps, enhancing our global quality and coverage.

Have you ever wondered what it’s like to be a Program Manager (PM) at Microsoft? As someone who often fields this question, I’d like to offer some insights into the fascinating world of product development and customer-centric decision-making that defines the role of a PM at one of the world’s leading tech companies.

At Microsoft, our mission revolves around creating products and services that address real-world needs and deliver tangible value to our users. As a Program Manager, my primary responsibility is to delve into the evolving landscape of customer demands and technological advancements to identify opportunities for enhancing our products.