Why Chat Alone Isn't Enough: 5 Use Cases That Need MCP

Five ordinary enterprise workflows a chat assistant cannot finish, what each looks like once the assistant can reach the systems involved, and where a person stays in the loop.

Patrick Eden
Updated
8 min read
enterprise-aimcpuse-casesworkflow-automationgovernance
Why Chat Alone Isn't Enough: 5 Use Cases That Need MCP

TL;DR

A chat assistant can draft, summarise and explain. It cannot look up an order, check a budget line or open a ticket, because it cannot reach the systems those live in. Below are five ordinary workflows that stop at exactly that wall, what each looks like once the assistant can reach the systems over MCP, and the step in each where a person should still decide. No invented percentages this time; the 2025 version had ten of them.

The wall

Every one of these workflows has the same shape. Someone asks the assistant for help. The assistant produces good text. Then the person opens three or four systems to find out whether the text is true, pastes the findings back, and does the actual step (the refund, the reschedule, the ticket) by hand. The assistant was in the conversation and not in the work.

The Model Context Protocol is what puts it in the work. A system is exposed once as an MCP server; any assistant that speaks the protocol can discover and call its tools. What follows is the same five workflows with that in place.

1. Customer support

With chat alone: the agent asks for a reply to "where is my order", gets a polite draft, then opens the order system, the carrier portal and the CRM to fill in the facts.

With reach: "Sort out ticket 4471." The assistant reads the order, checks the tracking, sees the parcel held at a depot, reads the customer's history, drafts the reply with the real date and proposes a goodwill credit under the contract.

Where a person decides: the credit. The assistant proposes; the agent approves; the approval is logged.

2. Sales proposals

With chat alone: the rep pastes in last year's proposal and asks for a new one. The assistant produces something plausible with last year's prices and none of this year's context.

With reach: "Draft the renewal for Nordkap." The assistant pulls the account from the CRM, current list prices from the pricing system, the open support tickets, and the usage report, and writes a proposal that reflects all four.

Where a person decides: the discount. A rep can propose within their band; anything beyond it waits for the sales lead.

3. Finance close

With chat alone: the analyst exports the GL to a spreadsheet, pastes chunks into the assistant, and asks what looks odd.

With reach: "Which cost centres are more than ten percent over budget this month, and why?" The assistant queries the ERP, lists the variances, reads the supporting POs, and writes the commentary the controller would otherwise write on Friday night.

Where a person decides: the journal. The assistant may read everything the analyst may read; it may propose an accrual; it posts nothing.

4. IT operations

With chat alone: the on-call engineer pastes a log excerpt and asks what it means. Useful, and then they go and look at the dashboard themselves.

With reach: "Why is checkout slow?" The assistant reads the monitoring system, correlates the latency with a deploy an hour earlier, reads the change ticket, and drafts the incident note with a proposed rollback.

Where a person decides: the rollback. The 2025 version of this post celebrated incidents "resolved automatically without human intervention". That is the wrong goal. Restarting a service in production is exactly the call that should wait for a human, and be logged with their name on it.

5. Project and portfolio management

With chat alone: the PMO lead pastes status emails from six project managers and asks for a summary. The summary is only as honest as the emails.

With reach: "Which projects are behind against their baseline?" The assistant reads the work-tracking system, the resourcing tool and the budget lines, and reports which projects say green and look amber.

Where a person decides: the escalation. The assistant flags; the lead decides what goes to the steering committee.

The common thread

In all five, the assistant is not doing anything a person on that team could not do. It is doing the looking-up, so the person can do the deciding. That only works if two things are true.

First, the assistant has to be able to reach the systems. That is MCP, and it is solved.

Second, the assistant has to be allowed to. An assistant that can read the CRM, query the ERP and propose a rollback has to be governed like a person who can. The protocol does not do that: every client that reaches a server sees everything it exposes, and a read on one record and a read on all of them are the same tool. So the security review asks who may use which tool, on which records, with what approval and with what proof, and a pilot built straight on the assistant cannot answer.

That is where Palma sits. Each person gets one governed connector, assigned through the groups they already hold in the identity provider, carrying every tool and Skill they are entitled to into whichever assistant they use. Policy is evaluated on the actual arguments of the call. The steps marked "where a person decides" above are held for approval. Every call is logged with who, what, when and why, and cost is attributed per team. The assistant does not change; what changes is that IT can say yes.

Which one to start with

The one with a queue and an owner. Support and finance close are usually the fastest, because the systems involved already have MCP servers and the approval step is obvious. Connect the identity provider, expose the two or three systems that team uses, set reads open and writes behind approval, and hand the security team the audit trail after the first week.

If you would like help choosing, book thirty minutes.

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What is Palma.ai in one sentence?

Palma.ai is the enterprise governance layer for MCP — it gives every person and agent a single governed connector carrying the tools and Skills they're entitled to, enforces policies on the actual arguments of a call, pauses high-risk actions for approval, and records everything in a tamper-evident audit trail.

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Deciding which person may use which tool, with which arguments, with whose approval — and being able to prove it afterwards. MCP itself covers how a client authenticates to a server and how a tool is described and called; it does not decide which person may use which tool, hold a risky call for a human, or keep the record an auditor asks for. Palma adds that layer: one governed connector per person, assigned by identity-provider group, carrying the tools and Skills they are entitled to into whichever assistant they already use.

Does my team have to set up MCP servers themselves?

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What's a Skill, and why does it matter?

A tool is a verb — "send an email". A Skill is the playbook that tells an agent when and how to use the verbs it already has: how your team actually closes the books, runs an incident review, or qualifies a lead. Skills are versioned, scanned before they're served, and scoped like any other piece of enterprise software — so your best operator's process reaches everyone else's agent.

Does it work with the AI clients we already use?

Yes — everything is served over MCP, so the same connector, Skills and policies follow the person into whichever assistant they open, whether that's Claude, ChatGPT, Copilot, Cursor or something else. Switching tools doesn't mean re-approving, re-installing or re-auditing anything.

How do we prove what an agent actually did?

Every tool call is attributed to the person it was done for, the agent that did it, and the application it ran in — with the arguments, result, duration and cost. The audit trail is tamper-evident and verifiable offline with your own key, so your auditor doesn't have to take our word for it, and it streams to the SIEM you already run.

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