How to position the model context protocol for the business to shape safer agentic adoption in 2026

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The Model Context Protocol is a pivotal standard for 2026. It defines programmatically how machine intelligence connects with the enterprise. From greater security to better control, using the right MCP will make your agentic AI applications powerful, rather than pointless. Here’s why.

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To help you make the most out of this article, first, know that it is not a technical guide. If you are a product leader, you can forward this article to the business team that needs it the most. If you are in a business function–product marketing, sales, customer success, use it to position MCPs as a competitive advantage and use it to cast light on the complexities of agentic AI implementation, so you can build trust with business and technical partners.

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We are well past needing to define what an MCP is, because you probably already know the definition. Here’s a refresher, from anthropic, the company that defined the standard.

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The Model Context Protocol is a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. Its aim is to help frontier models produce better, more relevant responses. – Anthropic

Whilst ‘better, more relevant responses’ can generously include anything that comes to the imagination, it is worth making this aim a bit more specific, and mapping it onto benefits. 

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But before we do that, how about we humanize this definition? A Model Context Protocol standard is like the homunculus of your brain. It maps intelligence (your cortex and its functions) to the various tools (parts of the body) it needs to act intelligently in the world. It differs from an API because it does not retrieve or provide information; it seeks tools, data, and adequate spaces to take action with.

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Now, on to humanizing its benefits and making them more specific:

Benefit #1: More reliable outcomes to prompts. For example, agents know to reference specific reports more frequently and thus produce more reliable recommendations. Or, agents know to check records against the expected standard of data quality, thus more reliably automating data quality workflows.

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Reason to believe #1: The Model Context Protocol allows the business to control which tools, apps, features, and data people have access to when prompting common agents like Claude, Gemini, Joule, and Copilot:

Here, let us distinguish between model reliability and the fact that through an MCP the model can reliably access the right tool, control, environment.

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Benefit #2: De-risking applications of agentic AI. For example, only financial controllers should have content edit and view access to financial records, whereas other functional roles may have restricted access. Restricted access can mitigate immediate access to sensitive data to attackers, as a minimum data governance guardrail for experimental Agentic applications. 

Reason to believe #2: Through an MCP, businesses can define risk thresholds, such as who accesses what data, mitigating, at a minimum, who can physically  prompt-inject unwanted commands that leak financial data to the wrong players.

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Benefit #3: It guides intelligent agency from prompt to action so that it directly benefits the enterprise. 

Reason to believe #3: If the MCP standard helps define what tools are best to use, what data and in what context, then it is worth mentioning that this definition is more often than not unique to your business. Therefore, any action the agent takes through an MCP is a well-guided action that directly benefits the enterprise. 

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Imagine you need an agent to put paintings on your wall. You ask it to use a hammer and a nail. But there are many nails and several hammers. I am personally partial to simple nails and hammers, but you may not be. The wrong nail will cause a crack in your wall. You do not want that. With an MCP, you can tell the agent what nails and hammers you prefer using for your office walls.

A hammer is the perfect tool for driving nails, but I don’t recommend using it to fix a window. In the real world, there are many tools, each with its own best use case. – Craig Walls, Spring AI in Action

And there’s more to MCP than three solid benefits. Risk, reliability and control of agency may not always stand out as the strong positioning pillars for non-technical buyers making high-stakes decisions about agentic use cases. 

Not without a valid reason: it is hard to distinguish reasonable promises from hype. 

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Product Marketers, the ones who sit between product and marketing, are best equipped to provide the technical and business evidence needed in favor of such positioning. However, with new and innovative products, this may be a challenging task, but not impossible.

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So what evidence can you bring to make a strong ‌case for MCP adoption?

Lean on the safety and control benefits with a standard MCP and contrast them to performance benefits you do not get to control without an MCP. 

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When you consume a high-performing model off-the-shelf, you do not get to approve the model weights or the data it has seen. Therefore, the subtle model refinements you do not oversee or approve can cost your hard-won money if you blindly base decisions on that model’s recommendations. 

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However, with an MCP, you can at least control which data pipelines the model consumes, who uses the agent, and what tools it has access to–at the very least. 

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The MCP does not solve the problem of model mis-attunements nor vulnerabilities to prompt-injections. At a minimum, however, you can mitigate and offset some of this risk through a standard MCP by putting in guardrails for who can take action in your business environment from behind a prompt.

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This decision–to use a standard MCP server–might be a better decision than deciding to fine-tune your own model. It can be expensive, and by the time you finish training, you will have missed out on the more novel model architecture updates coming from big agentic and generative AI providers. By the way, over 90% of people who dare to bet on outcomes believe Gemini is and will be the best model… this month. Can you beat that?

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Why not achieve significant results with existing models, while enjoying greater control and safety through an MCP–which you can now expect from a reliable vendor rather than having to build your own?

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With agents, speed is less important than safety. Because if you would not give the keys to your office to your newest employee, why would you give it to AI?

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The early wave of GenAI deployments surfaced a pattern where speed sometimes outpaced safeguards. Under board and competitive pressure, CIOs deployed GenAI applications before implementing comprehensive processes to mitigate the potential for inaccurate or poor results. - From risk to reward: The dual reality of agentic AI in the enterprise, IDC

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The actions taken by AI agents will carry ‌greater consequences. Use the right breaks, use the right standard.

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As we enter 2026, problem-solving with LLMs gets an upgrade: from mere probabilistic answer generation to taking action. This upgrade makes agents not only highly capable but also conspicuous. Users would much rather skip thirty clicks on the screen in favor of thirty keystrokes that make up a prompt. 

AI agents are autonomous, multi-modal LLMs that don’t just talk, but do. As I have always deeply invested in the human side of technology, I see this not just as an upgrade, but as a force of transformation that comes with greater risks.  

I’ve spent the weekend diving deep into the architecture of AI Agents, studying Manning’s latest books on MCPs and LLMs, to stress-test my understanding, which by the way, can easily go out of date with the speed of progress we have been seeing. 

So, here is the non-technical truth: the 'AI Agent era’ is here, and it is powerful. But its power depends on integration. The Model Context Protocol is a standard way of plugging intelligence into enterprise apps, tools, and data. It’s like giving AI agents a set of keys to the office–one you can control.

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With a standard MCP, you reduce some of the risk, and decide which libraries and meeting rooms your agent can access, while guiding its office visits so that it directly benefits the business.

With better keys, you get better risk mitigation, control, and reliability. In 2026, the business value of using a standard MCP server is equal to the business value of successful agentic implementation. 

To that effect, I am proud to have taken to market the MCP server for SAP LeanIX. 

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Here’s to a powerful and safe start of 2026! 

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References: 

1. AI Governance: Secure, privacy-preserving, ethical systems, Engin Bozdag and Stefano Bennati
2. From risk to reward: The dual reality of agentic AI in the enterprise, IDC
3. Agentic MLLMs: Autonomous Multimodal AI, Emergent Mind 
4. Introducing the Model Context Protocol, Anthropic
5. MCP vs API: what is the difference, Freecodecamp
6.  Spring AI in Action, Craig Walls
7. AI Agents and Applications, Roberto Infante 

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