How to Prepare Your Business for AI. A Cheat Sheet

TLDR: Most teams aren't AI-ready. Not because they lack tools—but because their workflows, roles, and guardrails were built for a human-only workforce.
To prepare properly, you need to test AI in live work, redesign your processes, and make governance practical—not theoretical. This article shows how to start, what to watch out for, and where our Readiness Assessment can save you time.

What does “AI readiness” actually mean in 2025?

If you think it means:

Everyone knows what GPT stands for
You bought a licence to Microsoft Copilot
There’s a one-pager in HR about responsible AI use

You're not ready.

AI readiness means:

Your team knows which tasks AI supports—and which ones it shouldn’t touch
Your processes have been redesigned to make room for AI, not broken by it
Your policies cover real-world usage, not vague principles
If you don’t know where your gaps are, you can’t fix them. That’s why we built the AI Readiness Assessment in the first place.

How do you assess your team’s AI capability?

Most training fails because it’s generic, theoretical, or outsourced.
To build actual capability, start inside your own workflows.

Here’s how.

1. How do you redefine job roles for hybrid teams?
Sit with each function lead and break down their typical tasks
Categorise each task: predictable, judgment-based, data-heavy, client-facing
Mark what AI can help with vs. what it shouldn’t. Update the role definition with AI-supported tasks, manual review points, and new accountability flows This doesn’t just manage risk. It builds psychological safety (a big deal given the WHS rules in Australia).

 
2. How do you redesign a workflow with AI?
Pick a live process. One your team runs weekly or monthly. An example: customer onboarding, writing funding reports, handling internal requests.

• Map every step
  
• Label each one: AI-draftable / searchable / requires human discretion / must be reviewed
  
• Rebuild the process using actual tools (e.g. Claude, Microsoft Copilot, Notion)
  
• Run the new version manually. Don’t automate yet. Let your team learn through friction

We do this with clients in every AI Bootcamp—because until someone rewrites their work, none of it sticks.

3. What’s the best way to upskill non-technical teams?
Start with the work they already know. Run 1-hour drop-in sessions: bring a task, prompt a model, share results. Do working tests live on real documents or emails, 
share prompts, outcomes, and revision logic. This builds fluency, not just awareness. We cover it in our AI Fundamentals Masterclass using your own business context.

4. How do you promote responsible experimentation?
Most of the risk isn’t in bad actors. It’s in confused employees using AI in ways no one sees. Fix it by creating structured transparency:

• A shared doc or channel: “How AI helped this week”
  
• Post: task, prompt, result, what saved time, what needed review
  
• Normalise small wins. Treat failed attempts as case studies, not problems
  
• If you wait for permission to try, your team will do it anyway.

They probably already are. You just won’t know how.

What if your policies aren’t built for AI?

Most aren’t.

Check your current docs:

• Does your acceptable use policy mention LLMs?
  
• Are you logging what tools are used, and where outputs go?
  
• Who owns AI-generated content in your org?
  
• Do reviewers know how to spot hallucinations or bias?

If the answer is no, fix it before an incident forces the question.

We handle this directly inside your AI Strategy Roadmap, so you don’t end up retrofitting compliance later. What results should you expect from doing this properly?

• Fewer hidden uses of AI. More shared methods
  
• Faster, more consistent outputs—without replacing your people
  
• A visible culture of “safe to try” experimentation
  
• And better decision-making because governance is baked in
  

FAQs

Q: How do we know if we’re ready for AI?
A: Take the AI Readiness Assessment. It measures seven areas—strategy, tech, data, people, risk, ethics, and efficiency—and gives you a score with actions to improve.

Q: We already did AI training. Isn’t that enough?
A: Probably not. If staff haven’t applied AI to their own work, most of what they learned is sitting in a slide deck. Capability requires repetition and context.

Q: Is this just for digital or technical teams?
A: No. Some of the biggest gains come from legal, finance, operations, and frontline service teams—where GenAI reduces admin and speeds up decision support.

Q: What’s the first low-risk step we can take?
A: Run a live working session. Use a real task, try an AI tool together, and reflect on what saved time or improved clarity. Don’t over-engineer it. Just start.

 
Where to Start
AI Readiness Assessment: Benchmark your capability, risk, and opportunity areas
AI Fundamentals Masterclass: Learn how GenAI works by applying it to your own work
AI Bootcamp: Redesign one live workflow using real tools
AI Strategy Roadmap: Align your team, governance, and investment across functions

This work is easier than you think. But only if you stop waiting and start testing.
We’ll show you where to begin.