We’ve grown used to asking LLMs for everything: “Show me sales for the last five years,” and instantly we get charts or slide decks. But this ease masks what traditional software still does best. Take a CRM: it stores opportunities as structured records linked to companies, contacts, lead sources and past contracts. That structure lets you run reliable queries—say, “which referrals closed above €50k in six months?”—in milliseconds. A free-form chat can’t enforce data integrity or guarantee the same answer next month. Beyond data storage, software enforces rules you’d quickly lose if you trusted only an LLM. You can’t record a deal without first creating the related company. You can’t delete a company while contracts remain open. These constraints guard against garbage data down the line. Visual tools—graphs, filters, dashboards—let you spot outliers and trends faster than any text response. And built-in workflows capture years of domain know-how: approvals, required attachments, sequence of steps. That’s not friction—it’s the guardrails that keep your operation running smoothly. The key isn’t to replace these systems with chatbots but to connect LLMs to them. Tools like the Model Context Protocol (MCP) act like USB-C for AI: a single interface letting models access real databases, enforce schemas and permissions, and trigger valid actions. Salesforce’s Agentforce and Atlassian’s remote MCP servers don’t dissolve CRMs or Jira into conversation; they let LLMs reach into those systems on their terms. Industry cases, like Air Canada’s chatbot misguiding a bereavement fare claim and costing the airline in tribunal fees, show what happens when you skip those guardrails. In the end, LLMs will sit behind deterministic software, not replace it.