AI adoption // EP16

Ep16: Why your business feels behind on AI adoption

And why it's not the tools

If your business has grown fast and there's a nagging feeling that everyone else has AI worked out, Kim's take is that you're not behind. You've been handed a stack of examples that were never about a business like yours, and you've spent months comparing yourself to them. The real problem isn't the tools, it's the foundations, and once you see that it gets a lot more manageable.

Based on AI Your Business Podcast2026-07-29

The short version

Kim hears the same few sentences in nearly every conversation. We're behind because we've stalled on rolling out AI. Because I'm not sure how my people are using it. Because there are too many options. Because I can't translate what's coming at me into my business. Each of those is a symptom, not the problem.

The examples making you feel behind describe a starting point your business has already passed. Stop comparing against them.
Results are all over the place because they're being worked out one person at a time on top of half-embedded processes. That's a structure problem, not a people problem.
The foundation work isn't a detour from scaling. It's what future scaling is built on, and the gains compound once it's in.

The examples in your feed don't fit you

The usual story is a solopreneur or one team member who picks up an AI tool, builds a set of prompts and gets a remarkable result. Sit with the shape of it for a second.

  • One tool, one platform, one person
  • A small business with no legacy systems
  • No team of people working the same role in different ways
  • No clients with confidentiality clauses
  • Nothing that had to be unpicked before any of it rolled out

A scaling business is the opposite of that. Decisions still run through the owner, processes are half embedded, some of them work because one person's knowledge is holding them together, and the systems are still catching up to how the business actually operates today.

The Excel problem

The most common rollout is to hand the tool to the team and let everyone work out how it helps them. On the surface that looks like trust and enablement, and there is a real case for building parts of it bottom-up. In practice it's like handing everybody Excel and asking them to teach themselves.

Kim learned Excel that way and never became a power user. If the team is figuring out AI one person at a time, self-directed and individual, of course the results are varied. That's the mechanism. It isn't that your people aren't capable. Nobody has given them a clean, clear track to run on.

Lay AI over processes that are half embedded and it doesn't smooth the gaps over. It makes them louder and faster.

The reframe

The foundation work most businesses skip is not a detour from scaling. It is the scaling. Designing workflows with AI built into them, deciding how AI is governed, writing the acceptable use policy: that's the structural work that takes a business to the next level.

It means changing some processes, changing some ways of working, rolling out the right tools rather than any tools, and then actually teaching the team to get value from them. It's a real piece of work rather than a short list. There's also a deadline attached: from 10 December 2026, Australian privacy policies need to disclose automated decisions about people, and that has to be auditable.

Where you actually sit

At one end is enterprise, which sorted governance with the board a year or two ago and has a transformation office and a budget pointed at AI. At the other end is the experimenter on Substack and Reddit running ten agents with no guardrails, no security and no team or client data to protect. Loud, fascinating, and not relevant to your business.

In between is the established, scaling business with real clients, real complexity, a growing team and systems catching up. That's the gap this podcast is aimed at, and it's the one our AI for Teams Toolkit and governance work are built for. Ep17 picks up what happens once the team is enabled.

Frequently asked questions

Is my business actually behind on AI adoption?
Probably not. The examples filling your feed are one person, one tool, one workflow, in a business with no legacy systems, no team working the same role differently and no client confidentiality to unpick. They describe a starting point an established business has already passed. You're not behind, you're mid-build.
Why are our AI results so inconsistent across the team?
Because everyone is working it out individually. Handing the team a tool and asking them to figure out how it helps looks like trust, but in practice it's like handing everyone Excel and asking them to teach themselves. Varied results are the mechanism working as designed. It's a structure problem, not a people problem.
What does fixing the foundations actually involve?
Designing workflows with AI built into them, deciding how AI is governed including an acceptable use policy, rolling out the right tools rather than any tools, and teaching the team to get value from them. It's a real piece of work, and it isn't a detour from scaling. It is the scaling.

Want to turn this into a practical AI plan?

AI Collab helps established Australian businesses move from interest to implementation: strategy, team capability, governance and custom builds that fit the way the business actually works.

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