What AI Breaks First Inside Real Operations

Teams don’t resist AI out of fear. They resist confusion.

Last week’s column made one thing clear: AI is not a cure. It’s a diagnostic. On paper, most leaders nodded along.  Of course systems matter. Of course tools amplify gaps. But this week, let’s leave ideas aside and step into the shopfloor, the back office, the ops WhatsApp groups, the Excel sheets that are “almost correct”, and the people who are quietly trying to make AI work inside real businesses.

Because when AI enters day-to-day operations, it doesn’t break everything at once. It breaks very specific things first. And those breakpoints tell you exactly where your operating system is weak.

Where AI actually fails 

In theory, AI adoption looks clean. In practice, it lands inside a business that already has workarounds, shortcuts, and informal rules.

Most AI initiatives don’t fail because the tool is bad. They fail because the business asks AI to operate inside undefined work.

Here’s what that looks like on the ground.

Where SOPs don’t exist

In many SMEs, SOPs are assumed, not written. People say: “Everyone knows how this works.” “It’s obvious.” “We’ve always done it this way.”

Until AI asks a simple question: “What exactly is the process?” Take something basic like order processing. One person checks stock before confirming.

Another confirms first and “manages” stock later. A third relies on experience and intuition.

When AI is introduced … whether for drafting confirmations, updating customers, or tracking orders… it needs a single version of the process.

Without it, AI outputs start contradicting reality. The result?

Sales thinks AI is wrong. Ops thinks AI is unreliable. Founders step back in.

The real issue wasn’t AI accuracy. It was that there was never one agreed way of doing the work. AI doesn’t tolerate fuzzy processes. Humans quietly adapt. That’s the difference.

Humans are remarkably good at working with incomplete information. If a form is half-filled, someone calls. If data doesn’t match, someone checks WhatsApp.

If details are missing, someone guesses and fixes it later. AI doesn’t do that. It takes inputs literally. So when AI is used for: drafting proposals, responding to customers, creating reports, prioritising tasks it surfaces a brutal truth: your inputs were never clean to begin with.

Customer names vary. Prices are updated “sometimes”. Delivery timelines live in people’s heads. AI doesn’t fix this. It exposes it.

Teams then label AI as “not practical”, when the real problem is that the business has survived for years on informal correction loops that AI cannot see.

Broken handoffs 

Every business has handoffs: Sales → Ops; Ops → Accounts; Accounts → Dispatch; Support → Resolution

On paper, these handoffs exist. In reality, they’re fragile. Information leaks. Ownership blurs. Assumptions creep in. Humans compensate with reminders and follow-ups. AI cannot.

When AI is used to automate updates or coordination, these handoff gaps become painfully visible.

Customers receive confident updates that Ops can’t fulfil. Invoices don’t match what was promised. Support replies don’t align with actual resolution status.

Teams then say, “AI created the problem”.

It didn’t. AI just removed the human glue that was holding a broken handoff together.

Why Teams Resist AI

Founders often assume resistance comes from fear: fear of replacement, fear of technology, fear of change.

That’s rarely true in SMEs. What teams actually feel is confusion.

They don’t know: what the “correct” process is, which input matters most, who will be held accountable if AI output is wrong, whether following AI will get them into trouble later

So they hedge. They double-check. They bypass. They keep doing things “the old way” on the side. Not because they’re anti-AI. But because the system doesn’t protect them yet.

Until roles, inputs, and handoffs are clarified, AI feels risky to the people closest to execution.

A Quiet Pattern 

In businesses where AI does stick, something very unglamorous happens first.

Before AI: one workflow is written down, inputs are defined, ownership is clarified, review points are fixed

Only then is AI introduced… not everywhere, but in one controlled slice of work.

The team isn’t asked to “trust AI”. They’re shown how AI fits into a system that already makes sense. That’s when resistance fades.

Not because AI is impressive. But because confusion is removed.

What Leaders Should Fix 

If AI feels messy inside your operations, don’t start by asking: “Is this the right tool?”

Start by asking: Do we have one clear SOP for this work? Are inputs defined, or assumed? Is ownership explicit at handoffs? Does the team know what happens when AI is wrong?

These are operational questions, not technology ones. And they’re solvable without buying anything new.

The Uncomfortable Truth

AI is not breaking your operations. It’s showing you where operations were already breaking quietly, informally, and expensively.

Humans patched the gaps with effort. AI removes the patch and shows the crack.

That’s not a failure. That’s a signal.

Next week, we’ll talk about what leaders must redesign before scaling AI across teams so that intelligence actually creates momentum instead of confusion. Because in real operations, clarity always comes before speed.

(Rashmi Kulkarni is the CEO at PPS Consulting. She can be reached at rashmi@ppsconsulting.biz. Views personal.)