The Tech That Could Decide Your Place in the AI-First Future
If you’re an insights provider, now is the time to lean in. MCP isn’t a trend—it’s the infrastructure your data needs to stay relevant, usable, and monetized in an AI-first world.
There was a time when the idea of companies openly exposing their data or functionality via APIs felt like heresy. Fast-forward a decade, and APIs are not just commonplace—they’re foundational to the modern digital economy. Stripe built a payments empire on APIs. Twilio reshaped communications. Plaid redefined financial connectivity. The real story isn’t just about technology—it’s about ecosystem creation. APIs didn’t just unlock growth; they unlocked participation. They turned isolated capabilities into platforms, invited third-party innovation, and rewired how services interact. In short, they became indispensable.
Yet, curiously, the measurement and insights world remains stubbornly behind. For every data platform promising "on-demand access," what they often deliver is a dashboard with limited interactivity, a static PPT, or—if you’re lucky—a downloadable CSV. It’s integration theater, not true accessibility. And in a world increasingly driven by automation, it’s not just a missed opportunity—it’s a liability.
Now comes the next evolution: MCP, or Model Context Protocol. MCP is a framework for embedding rich, model-readable context into data streams—turning raw information into actionable insight.
Much like the early days of APIs, where competing protocols like REST, SOAP, and GraphQL coexisted and evolved, MCP is likely just the first salvo in a broader movement. Initiated by Anthropic, it signals a new class of interoperability standards for AI agents. And let’s not kid ourselves—OpenAI is almost certainly working on its own version of a model-readable context layer. If you’re in the data space, the emergence of multiple protocols is inevitable. Which means now is the time to understand what MCP is, how it works, and how to prepare your data for a multi-protocol future. The ones who invest early will have the upper hand—not just in access, but in influence over how these protocols evolve.
At first glance, MCP might sound like just another spec in the sea of acronyms. But don’t be fooled—this isn’t just incremental change. MCP represents a foundational shift in how data will be structured, understood, and consumed by intelligent systems. It is the protocol that will make or break your data’s relevance in the emerging era of agentic AI. And let’s be honest, insights data to the uninitiated is confusing and non-standard.
Think of it this way: if APIs enabled a world where humans could pull data into applications, MCP enables a world where autonomous agents can pull insight from that data—and act on it. The difference? Context.
MCP embeds critical metadata—who collected the data, when and where it was gathered, the assumptions behind its collection, the methodologies used, and how it should be interpreted. It transforms flat data into contextualized information. And that transformation is precisely what agentic AI needs.
If you’re not yet familiar, Agentic AI is the name for autonomous agents capable of reasoning, decision-making, and action. They are poised to become the front line of business decisioning. Think of these agents as systems that don’t just analyze data, but decide and act on it, often independently. These systems don’t just retrieve data. They interrogate it. They synthesize it with other signals. They act on it, often without human prompting. In this world, data without context is not just incomplete—it’s dangerous. It leads to flawed recommendations and erroneous conclusions. MCP is the answer to this challenge.
To draw a historical parallel: APIs didn’t just enable services; they forced industries to rethink value chains. MCP will do the same. It will compel data providers to reconsider not just what they deliver, but how they structure, contextualize, and expose their data to the machines that increasingly drive enterprise workflows.
And let’s be clear: the measurement industry doesn’t have the luxury of lagging again. The next generation of data consumers isn’t made up of human analysts combing through dashboards. It’s made up of AI agents running 24/7, scanning environments, generating briefings, optimizing campaigns, identifying risks, and flagging anomalies. If your data isn’t model-readable and context-rich, it won’t be consulted. It will be skipped.
MCP ensures your data isn’t just seen, but understood.
So why the hesitation? Partly it’s inertia. Many data suppliers still operate under a legacy mindset—one where access is tightly controlled, delivery is static, and differentiation is defined by opacity rather than utility. There’s fear, too. Fear that exposing context gives away competitive advantage. But that fear is misguided. In a world driven by AI, your moat isn’t secrecy—it’s usability.
The good news? MCP doesn’t mean giving away your IP. It means giving machines the information they need to properly interpret your data. It’s about moving from data that informs to data that enables action. And for providers, that means becoming indispensable to the systems shaping the future of work.
In the same way APIs unlocked entirely new business models, from fintech to communications, MCP will usher in a new era of relevance and inclusion. Data providers that embrace MCP will be the first to plug into AI workflows, be featured in autonomous analyses, and shape model-led decision-making.
MCP is not a future-state ideal. It’s an immediate available technology. The AI wave has already arrived, and it’s building momentum. Every insights provider has a choice: stay static and be sidelined, or get context-ready and become essential.
To the forward-thinkers, the innovators, the platform builders: now is the time. Explore MCP. Enrich your data. Make it legible to AI tools. Because the agents of tomorrow are already choosing their sources today—and they’re not waiting for your PPT to load.