Plugging AI Into Your Systems — Integration Made Simple

AI knows everything under the sun. Yet it doesn't know your inventory.

Smart, but It Doesn't Know Us

Ask AI "How were our sales this month?" and it can't answer. Of course — it has never seen your company's books.

For AI to do real work, it must be connected to your systems. Inventory, CRM, accounting, scheduling — only when it can reach this data does it become "our company's AI."

The "AI that works" foreshadowed in AI Is Changing the Way We Work is, in fact, only possible on top of this integration.

Integration = Handing AI the Tools

Integration sounds hard, but the idea is simple. You tell AI "this is the window to look up inventory, this is the window to place an order," and grant it permission to use them.

Then AI doesn't just answer — it looks things up and acts directly. Tasks like "check inventory and propose an order if we're low" become possible.

MCP: Turning Ad-Hoc Connections Into a Standard

There was a hurdle here. Every system attached in its own way, so each integration was a separate construction project.

That's why open standards like MCP emerged. Think of it as a common spec for connecting AI models to external tools and data.

The analogy is the standardization of charging ports. Devices once needed different chargers, but once the spec converged, any charger fit. MCP plays that role in the AI world — attach it once to the standard, and many AIs and many systems speak to each other.

What Changes for SMBs

Not long ago, connecting ERP and AI was the domain of large enterprises. Now, with standards and tools maturing, it is coming down quickly to small and mid-sized companies.

Examples of what becomes possible:

• Inventory — "Tell me items near stock-out and draft a purchase order"
• Customer service — "Check where this order's shipment is and write a reply"
• Accounting — "Summarize this week's receivables and send them by owner"

Work where a person used to receive an answer and re-type it now runs from lookup to execution in one go.

Connection Needs Brakes

Reaching into systems also means an accident happens if it reaches wrong. It's a problem if AI places a mistaken order or sees data it shouldn't.

So integration starts with the brakes.

1. Read first, write later — allow only lookups at first. Grant power to actually change things after trust is built. 2. Least privilege — open only the windows that task truly needs. Don't open everything. 3. A human checkpoint — for anything that spends money or is hard to undo, let AI propose and have a person approve.

Attach Narrowly, Then Widen

Integration isn't one giant project wiring up every system at once. Like moving from pilot to production, it starts by attaching your single most painful task, read-only, and trying it.

The moment AI begins to know you, the "tool you question" turns into "a colleague who works." That doorway of transformation is integration.