Why Using Copilot Isn’t an AI Strategy

“You should all be using AI now.”

Does this sound familiar? For many organisations, AI adoption amounts to a trickle-down instruction to use a free, company-provided Copilot account, sit through a hastily curated training video, and somehow “incorporate AI into your workflow”. In practice, this usually means pasting the odd email into Copilot and asking for a rewrite.

It feels modern and efficient. Technically, it’s actually pretty amazing. But once the novelty fades, does it look like a step towards a four-day working week? Is anything in the business truly changing?

What Copilot is genuinely good at

Copilot and its cousins ChatGPT, Claude and Gemini, are excellent thinking companions.

They’re good for: • Drafting rough first passes of emails and presentations. • Talking through ideas and sharpening pitches. • Summarising unfamiliar territory into executive summaries • Handling ad hoc, one-off tasks like booking a meeting room

In other words, they’re a very smart notepad. A surprisingly articulate rubber duck. That’s useful. Notepads have had a long and honourable career.

But they live outside your business. They don’t know you, your goals or have access to your data until you give it to them, each time.

Where the cracks appear

The trouble starts when people expect chat tools to behave like software systems. Every answer still needs a human to copy it somewhere else, check it, tweak it, and re-enter it into the system that actually matters.

At that point you haven’t automated anything. You’ve just created a new job. Human middleware. The computer’s doing the creative stuff, you’re doing the boring stuff. Spoiler: I think this should be the other way round.

Enter AI agents

AI agents are like Copilot, except they run in the background when you’re not there. Without oversight and effective prompting, we risk letting an enthusiastic intern with the keys to the filing cabinet accidentally shredding your most valuable documents on their first day.

The step from Copilot to agent is a step towards a traditional software engineering project.

An agent needs permissions. Memory. Guardrails. Escalation paths. Observability. Testing. A sense of when to stop.

A dull but telling example

Take incoming emails.

The Copilot version looks like this. Someone reads the email, pastes it into a chat, asks what to do, copies the answer, then decides whether they trust it. Repeated over and over until the draft is good enough to send.

The system version looks different. Emails arrive. They’re classified. Context is pulled from other systems. Routine cases are handled. Edge cases are flagged. Decisions are logged. Humans only see the ones that genuinely need judgement.

Same AI models. Radically different machinery behind the curtain.

The part most people don’t see

Modern AI systems don’t just respond. They act.

They call other tools. They fetch data. They chain steps together. They remember what they’ve already done.

Once you cross that line, you’re no longer “trying AI”. You’re operating it. That brings power. It also brings complexity. Things can fail quietly. Or quickly. Or expensively. This is why agentic systems need proper engineering, rather than just using Copilot on its own.

Get building

If you think dishing out Copilot is ticking the AI box, you’re missing out.

Your competitors are already building agentic workflows, small, targeted software projects that leverage AI to do the things computers are good at: sorting, checking, categorising, and pushing clear summaries, dashboards and documents to humans.

Copilot is impressive. It’s earned trust and found its way into everyday work. However that’s not the same as automation.

Reliable automation requires engineering. The good news is that AI has driven the cost of building these systems down sharply. Many dull, day-to-day processes that were never worth automating before are suddenly viable.

That’s an opportunity. Some organisations are already taking it.