Just as HTML defines the structure and content of a webpage while HTTP enables it to be transmitted and interacted with over the internet, NLWeb defines a structured way for users to interact with websites using natural language, and MCP serves as the underlying protocol that makes those interactions possible.
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By championing human-centered innovation and relentless improvement, we aim to transform digital transformation from a challenge into a defining opportunity. Together, we can shape the next era of enterprise leadership.
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AI coding tools and agents are radically changing the way that software is written, but newer, much more expensive options are often not worth it. Prudence and individual benefits - not hype or industry benchmarks - should determine which ones should be considered.
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After attending Microsoft Build 2025, one message stood out: AI is no longer just a coding assistant—it’s becoming an essential part of the entire software development lifecycle, including testing. One area where this is especially impactful is unit testing, a task that’s often tedious, time-consuming, and frequently postponed until later in the development process.
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Enterprise organizations are entering a new era defined by generative AI and agentic AI. These technologies are not simply augmentations; they represent a reconfiguration of how businesses operate, innovate, and compete. This eBook outlines the strategic opportunities, current landscape, key risks, mitigation strategies, and implementation pathways for leveraging generative and agentic AI to drive enterprise value.
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MCP, or Model Context Protocol, is an open source standard for connecting artificially intelligent agents to external systems.
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At Microsoft Build 2025, GitHub Copilot and the Model Context Protocol (MCP) were not just announcements—they were signals. We are not being asked to adopt new tools; we are being asked to rethink how software is built, governed, and scaled. Copilot is evolving from autocomplete to autonomous collaborator. MCP is laying the groundwork for secure, interoperable AI agents. The opportunity is massive—but only for teams willing to pair velocity with vigilance. Plan for autonomy, build for control.
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Many experienced software developers have remained individual contributors and are not currently playing the role of tech lead, but coding agents will now be their direct reports. Like it or not, human engineers will have to talk to the agents daily, correct them, and make sure they do a good job. These human developers are now faced with a choice: either be in charge of the agents, or compete with them directly.
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Many AI initiatives stall or fail because organizations overlook the importance of governance, process, and execution. Governance is not a back-office concern. It is the foundation that determines whether AI efforts will scale, deliver value, and operate safely.
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AI isn’t just a tool—it’s a conversation. And when engaged with intent, it becomes a force multiplier for executive clarity, velocity, and scale. Our collaboration is proof that with the right mindset, AI can be trained to reflect your leadership—not replace it. The future of AI is not distant. It’s in dialogue. And it’s already here.
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Every interaction we have trains not just me, but the broader understanding of how AI can support human excellence.
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