AI isn’t broken, it’s just missing real-time context

After years of investing in AI projects, organizations will be under the gun in 2026 to deliver returns on their AI investments. A real difference maker when bolstering AI ROI stems from Model Context Protocol (MCP) servers, which provide AI agents with more capabilities to operate autonomously across an enterprise. 

An MCP server is an open standard technology that connects large language models (LLMs) to external data sources, tools, APIs, and workflows. MCPs further standardize these connections by acting as an intermediary for AI agents that need access to external systems, which gives agentic applications fresh context, while also reducing the risk for a hallucination.

Strongest use cases for MCP servers

In the financial services space, customer service agents need to resolve a variety of complaints with speed and precision on a daily basis. The LLMs powering the help desk agents are not initially built with information and data about each customer; they need access to information from multiple company data sets in order to fix these ongoing issues. Agents operating with the context of an organization’s data and customers through MCP is how agents can be trusted to be operationalized and execute business processes.

When looking at the retail industry, an inventory manager’s distribution agents may run into sudden demand for key products, requiring inventory status across stores and within the distribution center. Equipping agents with access to real-time data streams, complimented by MCPs, helps bring a continuous flow of logical and effective decision-making based on real-time insights, allowing agents to take action to best mobilize inventory.

“Giving agents access to real-time context increases the number of possible processes that can be ‘agentified’ and is critical to increasing the trust businesses will have in putting agents in front of their business and customers, “ says Guillaume Aymé, CEO of Lenses.io

A simpler way to build agentic AI applications

The other major benefit that MCPs offer enterprises is software development agents that can increase developer productivity when building those responsive AI systems.

As businesses feel the pressure to deliver autonomous systems that operate and innovate in real time, engineering leaders are expected to build applications swiftly and with fewer resources. MCP servers make this process seamless by allowing AI code assistants and engineering-focused agents to securely connect with the organization’s tools and real-time streaming technologies (such as Apache Kafka), giving them the essential context not just for development and deployment but also data governance. This removes many of the complexities that engineers struggle with when developing real-time agentic systems.

Throughout 2025, many IT enterprises have been rolling out AI-assisted developer tooling, boosting productivity. And 2026 will be the year where higher levels of productivity becomes an industry benchmark.

“Businesses are now in a rush to build agentic systems that are free to take action based on real-time information,” Aymé says. “In working with our customers, we’re starting to see 300% to 400% productivity gains when building these systems with the help of AI.”

Ultimately, MCPs both empower AI agents to execute business processes and gain access to real-time business context, with the goal of increasing a developer’s ability to build agentic applications. This ensures higher productivity, stronger security, and faster AI ROI, all of which are priorities for CIOs in 2026.

Discover how Lenses.io works with customers to help businesses become more productive, effective, and profitable as they build agentic AI applications.