MCP: The Infrastructure Layer Powering Agentic GTM
Agentic AI is changing how enterprises run GTM… reps move faster, customers get personalization, and revenue ties to outcomes.
But none of this works without the right foundation.
That foundation is MCP (Model Context Protocol)… a standard way for AI agents to securely tap into systems like CRM, ERP, data platforms, and internal tools.
Where Most AI Efforts Stall
Enterprises often get stuck in the pilot stage because integrations are fragile. Common failure patterns include:
Too many disconnected tools: decision paralysis
Missing context: agents don’t know when or how to use a tool
CRUD-only APIs: forced chaining of multiple “create, read, update, delete” calls to answer basic questions
Weak security: authentication and compliance bolted on late (or not at all)
These gaps make it difficult for AI to deliver outcomes at scale.
Why MCP Matters
MCP creates a standard way to expose enterprise capabilities to AI:
Composable: agents don’t juggle 10 calls; they access a single, workflow-based endpoint that answers complete questions
Context-rich: metadata and guidance help agents use tools correctly
Enterprise-grade security: OAuth 2.x and compliance are non-optional
Scalable: APIs can evolve without breaking agent workflows
The result… AI that’s not just a flashy demo, but usable, reliable, and production-ready.
Case Study: From CRUD Chaos to Workflow Clarity
Context: A global SaaS company wanted AI-assisted deal reviews and renewal risk flags in Salesforce and their product-usage warehouse.
Before (CRUD-heavy APIs):
High complexity: agents needed 5–7 calls to answer a single question
Frequent errors: authentication failures and when calls were sequenced incorrectly
Slow preparation: QBRs delayed by scattered data sources
Intervention:
Introduced a search/intent endpoint: “deal status + stakeholders + last activity for account”
Added a workflow endpoint: “renewal-risk summary” combining CRM, tickets, and usage logs
Standardized on OAuth 2.x flows with scoped access
After (MCP-enabled APIs):
60–70% fewer API calls per agent task (search/workflow replaced CRUD chains)
Faster time-to-answer for QBR prep
Fewer authentication errors and cleaner audit trails
Result: This shift validated what the MCP community has observed: agents prefer powerful search endpoints and curated workflows over brittle CRUD chaining.
Momentum in the Community
Across the ecosystem, we’re seeing rapid progress:
Open-source projects making it easier to design MCP servers with context and workflows
Tooling platforms auto-generating MCP servers from existing API specs
Community connectors emerging for systems like Salesforce, Notion, and Jira
This collective innovation is pushing MCP from concept into production reality.
How Enterprises Can Start
Audit APIs: do you expose search/intent and workflow endpoints, or just CRUD
Prioritize one system: start with CRM or ERP and introduce an MCP server
Align security early: bake in OAuth, RBAC, and compliance from the start
Measure outcomes: latency, error rates, number of calls per agent action, and iterate.
Closing Thought
The agentic era won’t be powered by bigger demos… it will be powered by infrastructure that makes AI usable at scale.
MCP is that infrastructure. Enterprises that embrace it early will leap ahead — not just in technology, but in delivering predictable, revenue-driving outcomes.