Moving Past the AI Hype (Introducing MCP)
In this post, I’ll cut through the noise surrounding Generative AI and the Model Context Protocol (MCP), explain the practical changes on the horizon, and show you why understanding these topics matters if you want to stay relevant in the industry.
With Generative AI dominating headlines, it's easy to feel overwhelmed by bold claims of job replacement and rapid change. I understand this uncertainty because I share it. While you hear projections about programmers being replaced by AI in a matter of months, the real story is that our profession is changing, and what that means for you right now. In this post, I’ll share insights and actionable steps to leverage and adapt to the technologies shaping our future.
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We’re launching a new series on AI & MCPs, exploring how you can adapt to stay ahead of your competitors and unlock new experiences for your customers. In the coming weeks, we’ll introduce practical examples on how to create and use both AI and MCP in the AWS cloud.
The Expensive AI Experiment
Today's hype in AI investment mirrors the early days of the internet, where substantial spending often lacked focus on tangible outcomes. Senior leadership is increasingly demanding a direct connection between AI initiatives and strategic deliverables, without a clear and/or reasonable goal.
A study conducted at MIT found that only 5% of AI pilots significantly boost revenue, with most showing little to no effect on profits. Similarly, McKinsey reports that over 80% of companies see no clear impact on earnings from generative AI. It may be time to acknowledge the red flags.
From our experience, these failures often occur because teams and projects are siloed, in addition to a failing integration strategy. Running a few proof-of-concept projects in isolation will increase costs across the company. Isolated solutions can't share information or build the operational context an AI requires, so they end up providing little value to you or your customers.
Enterprise systems hold decades of refined business logic, data, and tested processes. Your ERP system covers supply chain complexity and your CRM knows your customers. Companies have invested a significant amount of effort into these systems and now rely on them to maintain their competitive edge. The problem is that these essential systems were not designed for natural conversation. They require strict commands instead of everyday language.
In effect, the systems with the most valuable business information are the most challenging for AI to access. Companies face a choice: either build costly middleware for each new integration, or limit AI to new, context-free applications. The second option often results in weak AI projects that people are reluctant to use.
The Overlooked Infrastructure Crisis
Despite the excitement and investment, most AI pilot programs fail to produce lasting results. Gartner says nearly a third of generative AI projects are quickly abandoned. The reason is not that models are too complex, but that they aren't integrated into the core business systems.
These challenges demonstrate that the real obstacle to successful AI is not the complexity of the models, but rather the lack of integration with the core business. The Model Context Protocol, or MCP, addresses this by embedding AI directly into existing business systems. It offers a standard way to connect everything, making integration easier and helping AI deliver real, lasting value.
Instead of treating AI as something extra, companies need to focus on integration as the main challenge. By building infrastructure that makes AI easy to use as a core business tool, not just in isolated pilot projects, organizations can unlock long-term value.
The Missing Infrastructure Layer Most Overlooks (Model Context Protocol )
MCP serves as the crucial link between AI and business software, resolving longstanding integration challenges. Standardization allows access across diverse systems while maintaining security. This makes conversational AI broadly usable and enables seamless integration with both legacy and modern core systems.
Many people focus on bigger AI models, but the real benefit comes from better integration. The Model Context Protocol offers three main advantages. Firstly, it securely connects AI to business systems at scale, thereby reducing the need for custom integration. Next, it enables AI to access essential business data using standardized methods. Finally, you can update or swap the AI model without modifying MCP or the API, ensuring your setup is future-ready.
Nobody Wants to Learn Your Software
Most people don't want to spend their time clicking through menus or filling out forms. Asana reports that employees spend a lot of time on repetitive administrative tasks. These hours could be used to solve real problems.
Most daily tasks are straightforward, such as finding a number, checking a status, or approving a request. Instead of working through complicated software, it would be easier to ask for what you need. With AI powered by MCP and in turn your core systems, routine work can be handled automatically, allowing people to focus on what matters most.
However, this doesn’t just apply to simple tasks. Imagine handling expense approvals, budget plans, or performance reviews through simple discussion. AI can orchestrate the details and keep everything on track, letting you focus on the big picture.
MCP enables the use of a single, simple conversational interface, such as chat or voice, for all your systems. This enhances customer experiences and facilitates the development of new business solutions. Integration becomes easier, and once you have MCP in place, any AI can connect. This 'build once, use everywhere' method reduces costs and complexity, making it affordable to grow your use of AI.
The Window for a Head Start is Closing
Currently, the adoption of AI shares many similarities with the dawn of the internet. At that time, companies that built strong online systems stood out and stayed ahead for years. Today, there is a similar chance to be among the first to create an AI infrastructure. Companies that move quickly will be ready for future advances in AI, while others may find it hard to keep up or fall short.
As more companies realize the value of easier integration and AI, MCP is becoming the standard. Leaders need to decide whether to continue spending on expensive, isolated AI projects or to invest in infrastructure that enables AI to deliver value at scale.
Choosing the proper infrastructure is almost always more cost-effective and impactful over time than funding scattered initiatives. Additionally, AI will only succeed if it is fully integrated into the core business infrastructure. Companies that make this integration a priority, rather than treating AI as an add-on, will move ahead of their competitors as conversation-based systems become the norm.
The era of AI pilot projects and struggling AI startups is coming to an end. Now is the time to invest in robust AI infrastructure and prepare your business for the next decade.
Ready to Transform Your AI Infrastructure?
At Elva, we help organizations move past AI PoC and MVP. Our team’s expertise with the Model Context Protocol and AWS allows companies to:
Implement a robust infrastructure that is suitable for integrating Generative AI.
Transform legacy systems into AI-accessible resources without costly replacements.
Build once, deploy AI everywhere across your entire ecosystem.
Make your business easy to talk to. Contact Elva now to implement MCP and gain a competitive edge.