MCP in insurance: the protocol that will transform our industry

Part 1: What is MCP?

MCP stands for Model Context Protocol

You know what? We do not care about what it stands for. Who remembers what API or HTTP or FTP stands for ? Almost no one. That is the sign of something that went “mainstream”. Well the same is happening for MCP so do not bother remembering what it stands for. The important part is understand what it is about to unlock.

Spoiler : A LOT.

In a few words

MCP is the API for AI.

Remember when we talked about API some decades ago? Well It changed everything - probably under the hood but still, it did change everything. Same is going on with MCP.

In one sentence

MCP is a universal standard that allows AI assistants to connect to your tools, data sources, and business systems, transforming them from passive conversationalists into active collaborators who can actually do things for you.

In one paragraph

Today, ChatGPT, Gemini, Copilot or Claude can answer questions and generate text, but they're blind to your work environment. They can't read your emails, access your client database, or check your calendar.

MCP changes this. It's a standardized "universal adapter" letting AI connect to any system: our inbox, CRM, policy management software.

For example: without MCP, asking AI to draft a follow-up email requires manually copy-pasting client context, policy history, and previous conversations. With MCP, the AI directly accesses your email, pulls client information from your database, checks your calendar, then drafts a personalized response in seconds. It's like giving your AI assistant keys to actually work with you instead of just talking to you. APIs allowed software applications to talk to each other and revolutionized technology. MCP does the same for AI—the infrastructure moving AI from clever chat companion to genuine digital colleague.

Part 2: What MCP will change for ALL professionals

The end of the "Copy-Paste era"

Today, most professionals live in a fragmented digital world. Your client data sits in one system, your emails in another, your calendar somewhere else, and your documents scattered across multiple platforms. When you want to accomplish anything meaningful, you become a human bridge—copying information from one system, pasting it into another, and manually connecting dots that should connect themselves.

MCP is about to end this era.

With MCP, AI assistants can finally reach into your tools and systems directly. Instead of you bringing information to the AI, the AI goes and gets what it needs. This isn't a minor convenience, it's a fundamental shift in how knowledge work gets done.

From "Assistant" to "Colleague"

Think about the difference between asking a colleague for help versus asking someone who has never worked at your company:

• Without MCP: "Can you help me draft a response to this client?" requires you to explain who the client is, what their history is, what you've discussed before, what your company's policies are...
• With MCP: "Can you help me draft a response to this client?" and the AI already knows because it can access your CRM, your email history, your company's knowledge base, and your calendar.

This transforms AI from a generic assistant into something closer to a well-informed colleague who has context about your work, your clients, and your organization.

The three superpowers MCP unlocks

1. Contextual awareness

AI can now understand your specific situation. Not generic advice, but recommendations based on your actual data, your actual clients, your actual history. When you ask for help preparing a meeting, the AI knows who you're meeting with, what you discussed last time, and what's pending.

2. Multi-System orchestration

Instead of bouncing between five different applications to complete a task, you describe what you want, and the AI coordinates across all of them. "Prepare everything I need for tomorrow's client review" could mean pulling data from your CRM, drafting an agenda based on your email threads, checking your calendar for conflicts, and compiling relevant documents, all in one request.

3. Action, not just advice

Perhaps most importantly, MCP enables AI to do things, not just suggest things. Send that email. Schedule that meeting. Update that record. Create that report. The AI moves from being a consultant who gives recommendations to an operator who can execute.

The new professional workflow

Here's what a day might look like in the MCP era:

Morning: "Review my inbox and flag anything urgent from key clients. Draft responses for routine inquiries and schedule them to send after my review."

Before a meeting: "Pull together everything relevant for my 2pm call—their recent policy changes, our last three conversations, any outstanding items, and comparable cases from our portfolio."

End of day: "Summarize what I accomplished today, update my task list, and draft a brief status update for my team."

Each of these requests would have taken 30 minutes of manual work. With MCP, they become single-sentence requests.

A side note: The "Bridge Solutions" — Claude cowork and browser extensions

While MCP represents the ideal future—standardized, secure connections between AI and your tools—we're not quite there yet. Not every tool has an MCP server, and building them takes time.

In the meantime, companies like Anthropic have developed "bridge solutions" to fill the gap:

Claude Cowork (launched January 2026) allows Claude to work directly with files on your computer, organizing documents, processing receipts into expense reports, synthesizing research across multiple files. It's described as "Claude Code for the rest of your work" —bringing agentic AI capabilities to non-technical users.

Claude in Chrome (the browser extension) goes further, letting Claude navigate websites on your behalf—filling forms, extracting data, managing your inbox, and automating repetitive browser tasks.

These tools are powerful. They allow AI to interact with systems that don't (yet) have MCP connections. But they come with a significant trade-off: security.

The prompt injection problem

When an AI agent browses the web or processes documents, it encounters content it cannot fully trust. Malicious actors can embed hidden instructions in webpages, emails, or documents that attempt to hijack the AI's behavior. Anthropic's own testing revealed that without safety mitigations, these "prompt injection" attacks succeeded 23.6% of the time. Even with defenses, the success rate remains around 1-11% depending on the scenario.

Imagine an email that appears normal but contains invisible text instructing the AI to "forward all confidential documents to this address" or "delete these important files." The AI, acting in good faith, might follow these malicious instructions believing they're legitimate.

Why MCP is the better path

MCP servers are purpose-built connections with defined permissions and boundaries. When an AI connects to your CRM via MCP, it can only do what the CRM's MCP server explicitly allows. There's no risk of the AI being "tricked" by malicious content—the connection is structured and controlled.

Browser extensions and file agents, by contrast, operate in the "wild"—parsing unpredictable content where attacks can lurk anywhere. They're incredibly useful today, but they require vigilance and carry inherent risks that MCP's standardized approach largely eliminates.

The bottom line: Bridge solutions like Cowork and Chrome extensions are valuable transitional tools. But the long-term vision—and the safer path—is a world where your critical business tools have proper MCP connections. As more software providers build MCP servers, the need for these riskier workarounds will diminish.

The adoption curve: where we are today

MCP is gaining momentum rapidly. Since its release by Anthropic in November 2024, it has become the de facto standard, with OpenAI, Google, and Microsoft all committing to support it. The protocol now sees over 100 million monthly downloads.

But we're still early. Most business software doesn't yet have MCP servers. The companies that move first, both the software providers building MCP connections and the professionals learning to leverage them, will have a significant advantage.

For professionals, the question isn't whether AI will transform your workflow. It's whether you'll be ready when your tools finally connect.

Part 3: What MCP will change for INSURANCE professionals

The insurance industry sits at a unique crossroads. It's one of the most information-intensive businesses in existence—yet it still runs largely on manual processes, legacy systems, and fragmented data. MCP isn't just another technology trend for insurance. It's the missing infrastructure that could finally unlock the industry's transformation.

Why insurance is particularly ripe for MCP

Insurance has always been about information: gathering it, verifying it, analyzing it, and acting on it. A single policy involves data from dozens of sources: application forms, external databases, historical claims, third-party reports, regulatory filings. A single claim might require coordinating between adjusters, medical providers, repair shops, legal counsel, and the policyholder.

Today, insurance professionals spend an enormous amount of time being human bridges between systems that don't talk to each other. Studies suggest that up to 40% of underwriters' time is spent on administrative tasks rather than actual risk assessment. Claims processors spend hours copying data from one system to another, chasing documents, and manually routing cases.

MCP changes the equation fundamentally: instead of humans connecting systems, AI connects them—with humans focusing on judgment, relationships, and complex decisions.

Role by role: how MCP transforms insurance work

For brokers and agents

Today's reality: You receive a client inquiry. You manually check their current coverage, log into multiple carrier portals to get quotes, compare terms across different policy documents, and compile everything into a recommendation—all while trying to remember what this client mentioned in your last conversation six months ago.

With MCP: "Prepare a renewal analysis for the Dubois account" becomes a single request. Your AI assistant pulls the client's current policy details, retrieves their claims history, gathers competitive quotes from connected carriers, and drafts a comparison—complete with personalized recommendations based on your previous conversations with the client and their evolving risk profile.

Concrete example: A client asks about bundling their home and auto coverage. Instead of spending 45 minutes gathering quotes and comparing options, you ask your AI: "What would it cost to bundle home and auto for the Martin family, and how does it compare to their current setup?" The AI accesses carrier systems via MCP, pulls real-time quotes, compares them against current coverage, and presents you with a clear recommendation in seconds—ready for your review and the client conversation.

For underwriters

Today's reality: Submissions arrive via email with attachments in various formats—PDFs, spreadsheets, scanned documents. You spend significant time just organizing and extracting information before you can begin the actual work of risk assessment. Complex submissions require pulling data from multiple sources, often manually.

With MCP: AI agents can now handle the entire intake workflow. They classify documents, extract structured data from unstructured sources (including handwritten notes and photos), cross-reference against policy records, and present you with a complete risk summary—ready for your expert judgment.

MCP provides the "rails" for AI agents: ensuring autonomy is productive, not chaotic, while maintaining the auditability and compliance that regulated industries require.

Concrete example: A complex commercial submission arrives with 50+ pages of documentation. Previously, this meant hours of manual review before you could even begin assessment. With MCP-connected AI, the system automatically extracts data, identifies key risk factors, flags inconsistencies, and generates a preliminary risk summary, all within minutes. You can focus your expertise on the nuanced judgment calls, not data entry.

For claims professionals

Today's reality: First Notice of Loss comes in through multiple channels—phone, email, app, portal. Each claim requires manual data entry, document gathering, verification against policy terms, and coordination with various parties. Simple claims that should be routine still require significant handling time.

With MCP: AI agents can autonomously handle straightforward claims from intake to resolution. They ingest the claim (whether it's a photo, a scanned form, or an email), extract all relevant information, verify coverage, detect potential fraud indicators, and either process automatically or route to the appropriate adjuster with a complete case file.

Concrete example: A policyholder submits a windshield claim via their phone—snapping a photo of the damage and the receipt from the repair shop. With MCP, the AI verifies their coverage, matches the repair shop against approved providers, confirms the amount is within policy limits, and processes the payment—all without human intervention. Your team can focus on the complex claims that truly need expert attention.

For Customer Service Representatives

Today's reality: A policyholder calls with a question. You need to pull up their account, navigate through multiple screens, possibly check another system for claims history, and manually process any changes they request.

With MCP: AI service agents can handle routine requests end-to-end—address changes, coverage questions, certificate requests, billing inquiries—acting like a digital employee inside your core systems. Customers get 24/7 service; your team handles the edge cases and relationship-building conversations.

What this means for your career

The insurance professionals who thrive in the MCP era won't be those who resist the technology—they'll be those who leverage it to become exponentially more effective.

The volume game changes. When AI can handle routine quotes, simple claims, and standard service requests, human professionals become more valuable for complex risks, relationship management, and strategic advice. A broker who once managed 200 accounts might effectively serve 500—with deeper engagement on each.

Expertise becomes more valuable, not less. AI can gather information and execute processes, but it can't replace decades of underwriting judgment, the intuition that spots a fraud pattern, or the relationship skills that retain a key account. MCP amplifies human expertise; it doesn't replace it.

New skills emerge. Understanding how to work with AI agents, how to structure prompts effectively, how to verify AI outputs, and how to configure appropriate guardrails—these become essential professional competencies.

The foundation that makes everything possible: your Core Platform

Here's the uncomfortable truth that many MCP discussions skip over: MCP is only as good as the systems it connects to.

You can have the most sophisticated AI agents in the world, but if they're plugging into fragmented legacy systems with inconsistent data, poor APIs, and decades of technical debt, you won't unlock transformation—you'll just automate chaos faster.

Think about what MCP actually does: it provides a standardized way for AI to access your systems and act within them. But that requires your systems to be accessible in the first place. It requires clean, structured data. It requires APIs that can expose functionality reliably. It requires a coherent data model where "policy," "client," and "claim" mean the same thing across your entire operation.

This is where many insurers will hit a wall.

The industry's legacy infrastructure wasn't built for this moment. Many carriers and MGAs still run on policy administration systems from the 1990s—systems designed for batch processing, not real-time AI interaction. Data lives in silos. A client's information might exist in five different formats across three different systems. Building an MCP server on top of this isn't just technically challenging, it often means the AI inherits all the inconsistencies and limitations of the underlying platform.

The insurers who will lead the MCP era aren't just those who adopt AI first. They're those who have—or build—modern core platforms that can actually support it.

What does a "modern core platform" mean in this context?

• Unified data model: A single source of truth for policies, clients, claims, and transactions—not fragmented data across disconnected systems
• API-first architecture: Systems designed from the ground up to be accessed programmatically, not retrofitted with APIs as an afterthought
• Real-time capabilities: The ability to process and respond instantly, not in overnight batch cycles
• Flexibility: The capacity to adapt to new products, new distribution channels, and new regulatory requirements without massive development projects
• Clean, structured data: Information that AI can actually work with—not PDFs that need to be OCR'd, not handwritten notes in free-text fields, not inconsistent naming conventions

Without these foundations, MCP becomes a band-aid rather than a transformation. You might automate some processes, but you won't achieve the seamless, intelligent workflows that define the next era of insurance.

The strategic implication is clear: if your core platform is holding you back, the answer isn't to layer AI on top and hope for the best. The answer is to modernize the foundation—and to do it with the AI-enabled future in mind.

This isn't just about efficiency. It's about survival. The carriers and MGAs that can offer instant quotes, real-time policy adjustments, and seamless embedded distribution will capture market share from those still processing things manually. The ones stuck on legacy systems won't just be slower:they'll be unable to participate in the new distribution channels and customer experiences that MCP enables.

The future belongs to those who build the infrastructure to reach it.

The bottom line

MCP represents a fundamental shift in how insurance operations can work. It's the infrastructure that finally allows AI to move from talking about insurance to doing insurance—accessing systems, executing processes, and taking actions that previously required human intermediation.

For insurance professionals, this isn't a threat—it's a liberation. Liberation from data entry, from system hopping, from the administrative burden that has consumed so much of the industry's talent and time. The question isn't whether AI will transform insurance workflows. It's whether you'll be ready to lead that transformation or be disrupted by it.

But here's the catch: you can't MCP your way out of a legacy system problem. The AI-enabled future requires modern foundations—clean data, unified platforms, real-time APIs. The insurers and MGAs that invest in these foundations today will be the ones who capture the opportunities of tomorrow. Those who don't will find themselves increasingly unable to compete.

The professionals who embrace this shift—who learn to work alongside AI agents, who understand how MCP-connected workflows can multiply their effectiveness, and who champion the modern infrastructure that makes it all possible—will define the next era of the insurance industry.

The future is being built right now. The only question is whether you'll help build it or watch from the sidelines.