MCP, explained: why wiring AI into your systems got cheaper
If you have asked "what is MCP" after seeing the Model Context Protocol in a vendor deck, here is the answer in one sentence: MCP is a standard way for AI systems to connect to your software, your CRM, your database, your ticketing tool, so that the connection is built once and works with any AI product that speaks the standard. The analogy that stuck across the industry is USB-C. Before a common port, every device needed its own cable. Before MCP, every AI-to-system connection was its own custom integration project.
The reason a CEO or COO should care about a protocol, which is normally the least executive of subjects, is money and negotiating position. Integration work, more than the AI itself, has been the quiet majority cost of putting AI into company workflows, and it used to be paid over and over. MCP collapses much of that repetition, and it loosens the grip any single AI vendor has on you. Both effects show up in your build budgets and your contracts.
What the Model Context Protocol is, without the jargon
Under MCP, each of your systems gets a small piece of software called an MCP server: a controlled doorway that describes what an AI is allowed to see and do there. Read these fields, search those records, create a ticket, never touch the rest. Any compliant AI application, an assistant, an agent, a coding tool, can then use that doorway. The protocol was introduced by Anthropic in November 2024 and then did something rare for a vendor standard: the competition adopted it, OpenAI and Google DeepMind among them, and governance moved to a vendor-neutral foundation under the Linux Foundation, as the protocol’s history records. That neutrality matters: a plug owned by one vendor is a leash, a plug owned by a foundation is infrastructure.
The integration tax it removes
Before a standard, connecting AI to company systems was an N times M problem: every AI application crossed with every internal system, each pairing a bespoke connector, each connector a small project with a maintenance tail. Change assistant, rebuild the connectors. Add a system, integrate it separately with each AI tool. In our own builds, wiring the AI into the client’s systems has regularly consumed more of the budget than the AI reasoning itself.
With MCP the arithmetic changes shape: one server per system, reused by every AI application you run, now and later. The server for your ERP written this year serves the assistant you buy next year. Integration becomes an asset you accumulate instead of a cost you repeat, and this holds for old systems too: a fifteen-year-old ERP with any kind of API can stand behind an MCP server, which is one more reason an old ERP is rarely the blocker it is assumed to be.
What it changes when you buy
The strategic effect is on switching costs. When integrations were bespoke to one vendor’s assistant, leaving that vendor stranded the integration investment, which vendors understood perfectly well at renewal time. When your systems expose MCP servers, the integration layer survives the switch, and the AI application on top becomes closer to a replaceable component. Two questions now belong in every AI procurement: does this product work with MCP servers we host, and does the vendor’s own product expose one. A vendor answering no to both is asking you to rebuild your plumbing inside their walls. The same logic runs through the build vs buy decision more broadly: the bought layer should never own your integration layer.
Adoption is far enough along that this is a fair demand rather than an early-adopter bet: by early 2026 the ecosystem counted thousands of public MCP servers and support across the major assistants and developer tools, a state of play summarized well in WorkOS’s overview of MCP in 2026. Your engineering team likely uses it already through their coding tools.
The caution that comes with the convenience
A standard plug for AI into your systems means it is now easy to give AI access to your systems, and easy is when access decisions get sloppy. An MCP server is a doorway, and doorways need the boring disciplines: least privilege, scoped credentials, an allowlist of actions, human approval on anything consequential, logs of everything. The server model helps here, because permissions are defined in one inspectable place per system instead of scattered through integration code. But the design work is yours: it decides whether the agent can read the order book or quietly refund it, a set of decisions we treat fully in scoping an agent’s permissions before production.
What this means for a board: MCP turned the most expensive, most repeated part of enterprise AI into standard infrastructure, and it moved bargaining power from AI vendors toward the companies that own the systems. The move to make now is unglamorous: have your team stand up MCP servers for the two or three systems where AI will touch your workflows first, with permissions designed properly. That wiring, servers, permissions and the agents on top, is the kind of work our custom AI team ships inside client infrastructure.