"I don't get the hype around MCP. It just feels like a nice-to-have."
That was a CEO and founder of an AI-centric company speaking. Not a skeptic from outside the AI world—someone building AI products for a living.
Right now, the tech world is split into two camps: those who agree with him, and those convinced he's missing something fundamental. Both camps have valid points, but the real story is more nuanced than either side admits.
The Gap Between Promise and Reality
Here's what makes MCP compelling on paper: it's a standardized protocol that lets AI agents connect to your internal tools—GitHub, Jira, Slack, your databases, your APIs—in a structured, predictable way. Instead of building custom integrations for every AI tool and every data source, you implement the protocol once and everything connects.
The promise is real. When it works, teams see transformative results:
Development workflows: Read your current Jira sprint, break down tasks into implementation steps, and open pull requests for new tickets—all triggered from a Slack command. No context switching, no manual coordination.
Support operations: Automatically scan issues reported in support channels, correlate them with recent code commits, and alert the right engineering team with full context. The time from "customer reports bug" to "right engineer is investigating" drops from hours to minutes.
Operations and incident response: Monitor alerts from Grafana or Datadog, match them to recent deployments from your CI/CD system, and surface potential root causes based on historical patterns. Instead of manually correlating logs across five different systems, the AI agent does the detective work.
Sales enablement: Give sales reps a real-time, complete customer view—recent tickets, product usage patterns, billing history, technical health metrics—synthesized into a coherent context in seconds. No more "let me check five different systems and get back to you."
These aren't theoretical possibilities. They're real workflows that work when properly implemented.
So why does it still feel like a nice-to-have for most organizations?
Why MCP Feels Theoretical
The gap between MCP's promise and reality comes down to a simple question: How many enterprises actually allow—and actively encourage—all their employees to use MCP-powered AI agents end-to-end?
I personally know of just one: a leading Israeli tech company that's fully committed to MCP-based AI adoption across their entire engineering organization. They're seeing measurable productivity gains. I'll share their specific use case if there's interest (comment if you want the details).
For everyone else, MCP adoption is stalled by predictable enterprise constraints:
Security Review Overhead
Connecting AI agents to your internal systems means those agents can read from and write to production databases, create pull requests, modify tickets, access customer data. Your security team rightfully asks hard questions:
- Which agents have access to what data?
- How do we audit what actions were taken and by whom?
- What happens if an agent makes a mistake or is compromised?
- How do we enforce least-privilege access at the agent level?
Most organizations don't have good answers yet. So MCP stays in the "promising but blocked" category.
Governance Gaps
Beyond security, there are operational governance questions that don't have established patterns:
- Who approves new MCP server deployments?
- How do we manage versioning and breaking changes?
- What's the rollback plan if an integration goes wrong?
- Who owns the integration when it breaks—platform team or product team?
Without clear governance frameworks, even organizations that want to adopt MCP end up moving slowly.
Integration Complexity
The MCP protocol is well-designed, but implementing it properly requires real engineering work. You need to:
- Build or customize MCP servers for your specific tools and workflows
- Handle authentication and authorization correctly
- Implement proper error handling and retry logic
- Set up monitoring and observability
- Train agents on when and how to use each tool
This isn't a weekend project. It's infrastructure work that competes with product roadmaps for engineering resources.
The Result: Theoretical But Not Practical
For most enterprises, MCP remains in a proof-of-concept state. Small teams experiment with it. Pilot projects show promise. But organization-wide adoption—where every engineer, every support rep, every sales person has MCP-powered AI agents as part of their daily workflow—that's still rare.
When the CEO said "it feels like a nice-to-have," he wasn't wrong about the current state. For organizations that haven't solved the security, governance, and integration challenges, MCP is indeed nice-to-have but not must-have.
Why That Perspective Is Also Very Wrong
William Gibson famously said: "The future is already here — it's just not evenly distributed."
That's exactly where we are with MCP adoption.
The Early Movers Are Winning
The small number of organizations that have solved the implementation challenges—proper security models, clear governance, solid integration infrastructure—aren't seeing incremental improvements. They're seeing double-digit productivity gains.
When an engineer can query their entire codebase, check ticket status, review recent commits, and open a PR without leaving their AI chat interface, the time savings compound. What used to take 20 minutes of context gathering and tool switching now takes 2 minutes.
When a support team can automatically correlate customer issues with system health metrics and code changes, they resolve issues faster and escalate to engineering with better context. Customer satisfaction improves. Engineering firefighting decreases.
These aren't marginal gains. They're fundamental workflow improvements.
It's Still Early Days
We're at the very beginning of the MCP adoption curve. The protocol launched less than a year ago. Most enterprises are still figuring out their AI strategy in general, let alone their MCP implementation strategy.
The teams solving these problems now are building competitive advantages that will compound over time. They're not just deploying a tool—they're learning how to integrate AI agents into their actual work processes, which is much harder to copy than installing software.
The Competitive Advantage Is Massive
Here's what happens when your organization adopts MCP end-to-end while your competitors are still debating whether it's a nice-to-have:
Your engineers ship faster because they spend less time on coordination overhead and context gathering.
Your support team resolves issues faster because they have better tools for diagnosis and escalation.
Your sales team closes deals faster because they can provide immediate, accurate answers to customer questions.
Your operations team prevents incidents faster because they can spot patterns and correlations that would otherwise go unnoticed.
The organization that moves faster, resolves issues faster, and serves customers better doesn't win by a small margin. They win decisively.
The Question for Your Organization
Is MCP a nice-to-have or a must-have? The honest answer is: it depends where you are on the adoption curve.
If you haven't solved the implementation challenges yet, it's fair to say MCP is nice-to-have. You have more pressing priorities, and the theoretical benefits don't outweigh the real costs of implementation.
If you've solved security, governance, and integration, MCP becomes must-have. The productivity gains are too large to ignore, and your competitors who haven't figured this out yet are falling behind.
If you're somewhere in the middle—experimenting with MCP, running pilot projects, working through the governance questions—the real question is: how fast can you move from nice-to-have to must-have?
What It Actually Takes to Get There
Based on conversations with organizations at various stages of MCP adoption, here's what separates the teams seeing real value from those stuck in pilot purgatory:
Executive commitment: Someone in leadership needs to own AI adoption as a strategic priority, with budget and headcount to match. This isn't a side project for a few engineers to tackle in their spare time.
Security partnership: Your security team needs to be involved from day one, not brought in at the end to approve or block. The organizations succeeding with MCP have security leaders who see AI adoption as a strategic advantage worth solving for, not just a risk to mitigate.
Infrastructure investment: You need to build proper MCP infrastructure—gateway services, authentication layers, monitoring systems. This is platform engineering work that pays dividends across every AI use case.
Governance frameworks: Clear policies on who can deploy MCP servers, how to handle data access, what approvals are required. This sounds bureaucratic, but it's what allows you to move fast at scale.
Bottom-up adoption: The best implementations start with high-value use cases for specific teams, prove the value, then expand. Organization-wide rollouts from the top rarely work.
The Real Divide
The tech world isn't divided into people who think MCP is nice-to-have versus must-have. It's divided into organizations that have solved the implementation challenges versus those that haven't.
The CEO who called MCP "just a nice-to-have" isn't wrong about where most organizations are today. But the organizations that figure out implementation first will make his statement look very wrong very quickly.
So what's your take? Is your organization treating MCP as a nice-to-have experiment, or as a must-have competitive advantage? And more importantly: what would it take to move from one to the other?
