Accelerating Revenue Velocity: The Blueprint for Content-Aware Sales Agents

Editor’s Note: This blog is adapted from a talk by Kunal Kumar, Chief Revenue Officer at GeekyAnts, at thegeekconf 2025. As a CRO, Kunal explores why traditional chatbots have failed to impact the bottom line and introduces a blueprint for context-aware agents that accelerate revenue velocity. His session moves past the hype of basic chat interfaces to examine how the Model Context Protocol (MCP) can transform the enterprise sales funnel.

I am Kunal Kumar, Chief Revenue Officer at GeekyAnts. People often sit in these sessions wondering how to actually step up their revenue. I have a simple question for you: how many of you use chatbots but find they fail to impact your revenue? You are not alone. We feel this inside our own company. We often find ourselves chatting with colleagues rather than getting the right information that increases our revenue.

I want to focus specifically on the sales funnel aspect. In many enterprises, I see a situation that I call the swivel chair dilemma. Imagine your sales rep for one minute. Once a lead comes into the ecosystem, they pick up the call, check emails, and chat on WhatsApp. Then they enter lead data into the CRM by hand.

If they need a pricing quotation, they have to go into a pricing engine or an Excel sheet to fetch the data. They then relay that data to the client. We think this is inefficient once we have AI in our ecosystem. Everyone feels that AI can do this work autonomously, but this manual process is the real phase of the enterprise segment right now.

We saw a real example of this through a prototype by Apollo Tires and IBM. The Model Context Protocol (MCP) functions as a unified access adapter. I think of it like the universal adapters you use when you travel to different cities. Without a universal adapter, you have to swap between four or five different plugs.

The MCP protocol sits as a context layer on top of your data sources. It gets relevant data based on the request from the AI agent. Agents utilize this context layer to ensure secure data retrieval. This allows the agent to provide answers that are based on the actual context of your enterprise data.