What MCP changes about enterprise software

As AI agents increasingly complete tasks across multiple enterprise systems through chat interfaces, software value may shift from visible user experiences toward underlying capabilities that agents can access. General-purpose AI models can reason across varied tasks but may incur higher, less predictable costs for specialized work, while specialist systems offer predefined workflows, domain expertise, and more reliable outputs. The article argues that predictions of a “SaaSpocalypse” overlooked the continuing enterprise costs of security, compliance, stability, and long-term maintenance, even as AI makes software easier to prototype. Model Context Protocol (MCP) is presented as a standardized connection layer that lets general-purpose assistants orchestrate work while invoking specialized tools when needed, reducing custom integrations and expanding where a company’s capabilities can be used. DeepL cites its MCP Server, which makes its language AI available through assistants including Microsoft Copilot, ChatGPT, and Claude, as an example of this emerging division of labor.

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