Model Context Protocol (MCP) Is Changing How AI Integrates and Why It Matters
Integrating AI into enterprise systems is still one of the most complex and time-consuming challenges in modern IT. For most organizations, enabling large language models to interact with existing tools, such as internal databases, collaboration platforms, or customer relationship systems, often means building custom APIs for each connection. This approach drains time, adds cost, and slows down innovation.
The concern is not limited to developers. In ‘The Impact of Technology in 2024 and Beyond: an IEEE Global Study’, 47 percent of global technology leaders listed difficulty integrating AI into existing workflows among their top three challenges when adopting generative AI[1]. That reflects an apparent demand for more scalable and sustainable integration methods.
This is where the model context protocol, or MCP, is gaining momentum. This article explores MCP, how it works under the hood, who’s building around it, and why it matters for enterprise AI strategy. We’ll also look at the broader impact of standardized AI integrations and what this means for service providers and global businesses.