The rise of MCP and the AI readiness divide

Why Model Context Protocol marks the next leap in AI

The AI ecosystem is evolving rapidly. Just as companies are getting comfortable integrating AI assistants like ChatGPT, Claude, and Gemini into their workflows, now everyone’s talking about Model Context Protocol (MCP), the latest hot topic in the world of AI product development. But why is it a big deal, what does it mean for businesses, and what can companies do to avoid falling further behind in their AI journey?

MCP is powerful, but only companies that are further along in their AI maturity journey will unlock the benefits of its full potential.

So, what is MCP in plain English?

MCP standardizes how AI applications (like chatbots, assistants, and agents) connect with your tools, systems, and data (like Slack, Google Drive, and databases). People often compare it to USB: In the ‘90s, industry leaders collaborated to create a single set of specifications (USB) for how various peripherals can connect to your PC, to avoid dealing with various ports and drivers. Similarly, MCP allows developers to build against one standard to make tools and applications interoperable with AI apps.

Why is that helpful? Because if you had 10 tools and 5 AI apps, you’d potentially have to build 50 (10x5) different integrations. That’s a lot of duplicative work, not to mention inconsistent implementations that hamper interoperability for a broader ecosystem of AI solutions.

But there are reasons beyond streamlining integrations that are getting companies excited for MCP.

Why businesses are excited about MCP

MCP (developed by Anthropic) is an open standard and has not been recognized as an official protocol by any international governing body. But many major AI providers, including OpenAI, Microsoft, and Google, have moved to support it, signaling its status as an industry winner (for now). 

So why is MCP quickly gaining adoption?

1. Faster innovation

By removing the bottleneck of integration work, product teams can reduce dev hours, prototype faster, and deploy tools more easily, instead of rebuilding integrations every time they adopt a new AI app or a new tool. 

2. Model-agnostic flexibility

Because MCP works with any AI model, product teams can avoid vendor lock-in and develop multi-LLM strategies, giving businesses the flexibility to mix models based on cost, performance, or reliability.

3. Tapping into an open ecosystem of solutions

Because MCP is an open standard, multiple developer communities have sprouted, resulting in companies and developers providing community MCP servers for integrations with more tools and data sources. Just like how adopting the USB standard enabled electronics manufacturers to market their solutions to a wider audience through a network effect, companies today have an opportunity to reach more users by adopting MCP rather than building their own custom integrations, which contributes to more incompatible AI frameworks.

4. Possibilities for agentic, composable AI

MCP was designed specifically to meet the needs of modern AI agents by providing a modular way to plug into tools, opening the door to a world where a model can act autonomously to select the appropriate tools and orchestrate tasks across your various systems and datasets at scale, simply by connecting to an MCP server.

Help your teams understand the business value of MCP

MCP is just another tool that can support your broader business strategy. And a good strategy starts with defining the business goals and user needs that you want to solve for. What AI tool might give you a competitive advantage? How might applying LLMs to your internal knowledge base help your customer support team? How might you help non-technical users ask data questions by connecting an AI assistant to your BI tools? MCP has made it much easier to make AI projects happen, but they should all ladder up to a coherent strategy.

Is your organization ready?

While MCP is lowering the barrier for companies to start creating business value with AI, many organizations are still early on in their AI maturity journey, where they’re training their employees to develop AI “habits,” or searching for meaningful AI use cases. In order to leverage MCP, there are other considerations for AI readiness, such as putting the right team in place, getting your data sources and APIs in order, and having a solid data governance policy in place. Companies already investing in strong data infrastructure, knowledge management, and responsible AI will use MCP to accelerate.

Just having access to AI isn’t enough—organizations need AI maturity. Knowing your organization’s AI readiness is just the first step.

Take the next step

Want to explore further? At Modus, our capabilities include assessing AI maturity at your organization, hosting workshops to improve education or jumpstart a strategic roadmap, and executing proof-of-concepts and pilot programs. Learn more about our AI services and get in touch today.