By Gertrude G. Van Horn, SVP & CIO, Cien.ai

Introduction

At Cien.ai, many of our Business Development and Growth executives are based in Dallas, Texas, where you’ll often hear the phrase, “All hat and no cattle.” It’s shorthand for someone who talks a big game but doesn’t deliver.

Until recently, much of the conversation around AI — especially Agentic AI — felt that way: flashy demos, bold claims, and very little real-world execution. Lots of promise, not much proof.

At Cien.ai, we’ve been AI-native since Day One.  Over the past nine years, we’ve earned bigger hats — and acquired more cattle.  Even our trusted data foundation—built years before data quality became urgent—set the stage for early Agentic AI.  Now, with the arrival of Model Context Protocol (MCP), we’re not just growing the herd — we’re building the whole ranch.

Cien is a cutting-edge analytics company delivering trusted, AI-ready GTM data and sophisticated RevOps insights to enable significant data-driven transformations for our clients, powering growth and revenue improvements.  We’re not just creating the most actionable revenue insights in the business. We’re now building Agentic GTM Assistants that guide operators through complex reports, spotlight critical signals, and trigger coordinated action.

Imagine receiving not just a dashboard — but a guided tour through a sequence of next steps to align leadership, prioritize accounts, and close the revenue gap. All backed up by the access and transparency required of today’s compliance and privacy protocols. MCP makes that possible. 

What is MCP and Why It Matters

Introduced by Anthropic in late 2024, Model Context Protocol (MCP) is an open protocol that standardizes how AI systems securely access external tools and data sources. It replaces the old M × N integration headache — where every tool needed a custom connector — with  MCP’s clean, universal client-host-server model for seamless, real-time access to any tool.  It’s like a “super API” but better with protocols designed to enable AI Agents and Large Language Models (LLM) to interact with other external tools and services efficiently and intelligently. 

 Examples of what MCP allows agents to do:

• Query real-time GTM systems like Salesforce, Power BI, or HubSpot
• Chain actions across productivity apps like Slack, Outlook, or Google Calendar
• Operate securely within permissioned, auditable access boundaries

In short: MCP is the foundation for moving AI from talk to coordinated, completed tasks.“The key value of MCP is bringing together multiple tools, LLMs, and data sources, allowing autonomous agents to provide answers and solutions to real-world problems. MCP is at the heart of this agentic AI transformation.”
— Martin De Saulles, CIO.com, April 2025

How MCP Enables Agentic AI

Here’s how MCP transforms AI from a passive chat conversation into an active operator:

1. Standardized Tool Access: Agents connect to systems with no custom coding.
2. Real-Time Context: Live updates from CRM, calendars, and data sources.
3. Secure Autonomy: Permission-based access and audit trails.
4. Multi-Step Workflows: Read a note → send an email → create tasks — automatically.
5. Plug-and-Play Agents: Operate seamlessly across ecosystems.
   

Agentic frameworks also enable adaptive-like behavior at the agent level — not by retraining models, but by giving them real-time access to up-to-date context (static content and dynamic data)  and tools, and allowing them to adapt.

Cien.ai + MCP: Agentic AI That Delivers

We’ve been AI-Native since day one, adopting and leveraging emerging technologies and advanced constructs as they evolve. At Cien, we recognized the transformative potential of MCP early on. As soon as its capabilities became clear, we moved quickly to incorporate it into our product vision and strategy. It aligns perfectly with our mission to make game-changing recommendations that close revenue gaps through explainable, transparent data-driven action. By embedding MCP into the GTM Suite, we’re accelerating our ability to build intelligent, autonomous agents that don’t just analyze — they act.

Here are some examples:

1. Instead of digging through dashboards to find potential friction points and opportunities, the Agent does it for you – searches and prioritizes information and provides a simple summary. 
2. Need a growth plan? Instead of tedious, manual effort, the Agent builds one for you –  tailored to your business and incorporating relevant data and best practices. 
3. Wondering if reps are following through on their plans? The Agent tracks that and suggests ways to coach or nudge for the right behaviors and outcomes.

MCP became not just a feature, but a foundational enabler of the next generation of Cien’s AI-powered go-to-market automation. Over the past 9 years, we’ve acquired a lot of hat — and now, with MCP, even more cattle.

From Concept to Cattle

With 5,000+ MCP servers  –  providing access to tool endpoints and connection gateways – that are already live, bolstered by support from OpenAI, Microsoft, and Replit, MCP is quickly becoming part of AI’s infrastructure backbone. And at Cien, we’re already putting it to work. We’re actively engaged with 5 of the Top 10 Management Consulting firms, renowned PE firms, and B2Bs – assisting them with proven results in growth and revenue. We’ve identified over $1.6 billion in revenue growth opportunities, including a top-tier SaaS firm that generated $20 million in new revenue within 90 days of onboarding Cien, and a total of $180 million over 18 months. Cien works!

Final Word

Cien.ai isn’t just wearing the hat. We’re herding the cattle — and driving real revenue for our clients. Fast. Trusted. Proven. Game-changing.

Curious how MCP + Cien can transform your GTM motion? Let’s talk — and we’ll show you the ranch.

About the Cien.ai Growth Essentials Series

This article is part of the Cien.ai Growth Essentials Series, inspired by our work with B2B
business leaders, growth consultants, and PE operating partners. These articles focus on the
non-technical aspects of improving GTM performance. For the analytical and technical
methods used to quantify these concepts, explore our Practical RevOps Analytics Series.