Enterprise AI conversations have moved on from model comparisons. The more consequential question is whether your assistants can connect to the right systems, retrieve authoritative context, and operate inside clear governance boundaries.
That is where the Model Context Protocol (MCP) shows up in conversations now.
If you are a CIO, MCP will on the surface look like an integration standard, which it is. If you are a Chief Knowledge Officer, MCP will look like a distribution mechanism for knowledge, which it also is.
For both roles, the real point is that MCP makes it easier for AI systems to reach further. It also makes it easier to amplify whatever is already true about your knowledge estate, whether that is clarity and trust, or fragmentation and noise.
This blog is a high level technical introduction to MCP for CIOs and CKOs.
It is written to give you enough depth to hold a conversation with enterprise architects, without turning you into a protocol specialist. The central argument is simple: MCP is an enabler, but the differentiator is a well governed knowledge layer behind it.
MCP is an open standard for connecting AI assistants to the systems where data lives, and to the tools that can act on that data.
In practice, it gives you a consistent pattern for how an AI “host” application (for example, a desktop assistant, an IDE assistant, or an enterprise chat assistant) connects to external “servers” that expose capabilities. Those capabilities generally fall into a few types:
This is important because most enterprises are staring at an integration problem that scales badly. Every model and every assistant surface needs access to a growing catalogue of systems: Microsoft 365, CRM, finance platforms, matter systems, research platforms, data warehouses, knowledge bases, and external feeds. Historically, each connection has been built as a bespoke bridge. MCP aims to standardize the bridge.
It does not replace APIs. It sits above APIs and standardizes how an AI client discovers capabilities, invokes them, and incorporates results into its reasoning loop.
The diagram below (from What is the Model Context Protocol (MCP)? - Model Context Protocol) shows a simple illustration of the key MCP components in play:
There are two forces driving MCP into board level visibility.
The first is technical. As organizations move from “chat with a model” to “agents that complete work”, tool access stops being optional. Google’s guidance on function calling and grounding makes the same point in different language: models become more useful when they can call tools and ground outputs in verifiable sources, instead of relying on parametric memory alone.
The second is architectural. The industry is converging on modular patterns. McKinsey’s framing of an “agentic AI mesh” is a useful concept here in my view, not because you need to adopt their terminology, but because they highlight the real shift: from static, model centric stacks to composable, vendor agnostic systems, where governance is embedded rather than bolted on.
MIT’s long standing work on modularity and open interfaces explains why this sort of convergence happens. When complexity rises and uncertainty is high, stable interfaces let teams move in parallel, swap components, and evolve the system without rebuilding everything. In other words, protocols appear when the ecosystem needs a shared contract. Enter MCP.
CIOs have learned this lesson repeatedly with APIs, integration platforms, and data lakes. A standard connection does not guarantee a good outcome. It only makes the connection easier.
MCP makes it easier for an AI assistant to pull content from more places, faster. That can improve relevance and timeliness. It can also create three predictable failure modes if the underlying knowledge layer is weak.
If your content is duplicated across systems, inconsistently tagged, and poorly curated, then making it more accessible does not make it more reliable. It just makes confusion easier to surface, at machine speed.
In a single system, you can often rely on native permission trimming. In a multi system agent workflow, identity, delegated access, token lifecycles, and policy enforcement need to be consistent enough that the assistant does not leak what it should not see, and does not produce inconsistent answers for users who should be seeing the same view.
More connectors mean more points of failure, more audit requirements, and more opportunities for indirect prompt injection and unintended data exposure. This is not an anti MCP argument. It is the predictable price of interoperability.
One takeaway from this is: MCP is a capability multiplier, and governance determines whether it multiplies value or risk.
For CIOs and CKOs, the most productive way to think about “a governed knowledge layer” is not as a repository. It is a set of services and controls that make knowledge usable by humans and machines.
A robust knowledge layer typically includes:
If you already invest in these areas, MCP becomes an accelerant. If you do not, MCP becomes a mirror that reflects every weakness in your knowledge estate back to your users.
You do not need to implement MCP everywhere to benefit from it. Most enterprises will adopt it incrementally, just like most of your vendors' MCP Servers will evolve incrementally. The key is to place it correctly in the architecture.
A pragmatic reference pattern looks like this:
If you articulate it like that then you also separate “access” from “trust”. It is important to emphasize that MCP can standardize access. It is your governed knowledge layer which establishes trust.
You do not need to become a protocol specialist, but you do need to ask the questions that determine operational viability.
Here are some good starters to drive the right architectural conversations:
Typically, the questions are the difference between a pilot that demos well and a system that scales.
MCP changes the economics of knowledge, because it makes knowledge easier to invoke programmatically. This creates opportunity (for one, this is the opportunity to create a scalable profit center), so lean into that, but with clear guardrails.
Questions worth putting on the table early include:
It should be obvious that this is where knowledge management and the knowledge layer leveraged by MCP becomes critical operational infrastructure.
Most professional services and legal organizations are already deep in Microsoft 365. That is both an advantage and a trap.
It is an advantage because the systems of work are already there. It is a trap because “we have Microsoft 365” is often mistaken for “we have a knowledge platform which is governed”.
The gap usually shows up as soon as you deploy copilots at scale. Copilot and agents can only be as reliable as the content they retrieve, and retrieval quality depends heavily on structure, metadata, and governance.
This is where platforms like Atlas Intelligent Knowledge Platform are relevant, not as a pitch, but as a concrete example of what a governed knowledge layer looks like in practice when built natively on Microsoft 365.
A few factual examples that matter in an MCP shaped world:
Ignoring vendor preferences: MCP makes it easier to connect agents to knowledge services. The enterprise question is whether those knowledge services are governed enough to be safely invoked.
MCP is a meaningful step toward interoperability for AI agents. It reduces integration friction and encourages modular architectures, which is where the industry is heading.
At the same time, it raises the stakes on knowledge governance. As AI assistants gain reach, the governed knowledge layer becomes the controlling factor for accuracy, security, explainability, and adoption.
If you are planning for MCP, the most strategic investment is rarely “support the protocol”. It is building the knowledge layer and control plane that make protocol driven access safe, reliable, and repeatable.