AI Readiness Checklist for Australian Businesses

Who this is for

Business owners, IT leaders, and operations managers assessing whether their organisation is ready to adopt AI.

Question this answers

Is our organisation actually ready for an AI project, and what do we need to fix first?

What you'll leave with

Five dimensions of AI readiness to assess
Specific checklist items for each dimension
How to score your organisation's readiness
What to fix before starting an AI project

What AI readiness actually means

AI readiness isn't about having the latest technology or the biggest budget. It's about having the right foundations in place so an AI project can actually deliver value.

We assess readiness across five dimensions. Most organisations score well in some areas and poorly in others, and that's fine. Knowing where the gaps are lets you fix them before spending money on AI.

1. Data readiness

This is the most important dimension and the one where most organisations score lowest.

Check your data first

Data relevant to the AI use case exists and is accessible
The AI can't learn from data you don't have.
Data is reasonably clean (consistent formats, minimal duplicates)
Dirty data produces unreliable AI outputs.
Data is centralised or can be consolidated
Scattered across spreadsheets and email isn't ready.
Data volume is sufficient for the planned use case
RAG needs documents; ML needs training data.
Data sensitivity and privacy requirements are understood
Healthcare, financial, and personal data have specific constraints.
Data is regularly updated (not stale)
AI trained on outdated data gives outdated answers.

2. Process readiness

AI works best when it's augmenting a well-understood process, not replacing a chaotic one.

Know the process you want to hand over

The target process is documented and understood
If you can't explain the process to a person, you can't explain it to AI.
You can define what "good" looks like for this process
The process has measurable inputs and outputs
Current pain points are specific, not vague
"It takes too long" isn't specific. "Processing each invoice takes 12 minutes" is.
Exception handling is understood
What happens when the process doesn't follow the happy path?

3. People readiness

This is the dimension organisations underestimate most. Technology changes are easy compared to behavioural changes.

Get your people behind it

Leadership is committed (not just curious)
There's a clear project sponsor with authority and budget
End users are open to change (or change management is planned)
Someone internal can champion the project day-to-day
Expectations are realistic (AI augments work, doesn't replace people overnight)

4. Technical readiness

Make sure your systems can connect

Existing systems have APIs or can export data in usable formats
IT infrastructure can support the planned AI deployment (cloud/on-prem)
Security policies allow for the data flows the AI needs
There's internal or external technical capability to support the system

5. Budget readiness

Budget for the build and the running costs

Budget expectations are aligned with market reality
AI projects typically start at $20K-$50K for a meaningful proof of concept.
Ongoing costs are factored in (hosting, maintenance, model updates)
There's budget for data preparation if needed
ROI expectations have a realistic timeframe (typically 6-12 months to see returns)
Budget for a discovery phase is available ($5K-$15K)

Scoring your readiness

Count how many checklist items you can confidently tick across all five dimensions.

20+ of 26: You're ready. Start with a proof of concept on your highest-value use case.
14-19 of 26: Moderate readiness. Start with a discovery phase to fill the gaps, then proceed.
Under 14: Not ready yet. Focus on data quality and process documentation first. An AI project would likely stall.

Key takeaways

AI readiness is 80% about data and processes, 20% about technology
Most organisations score lowest on data readiness. Fix that first
You don't need to be 100% ready to start. A proof of concept can begin with moderate readiness
People readiness (organisational willingness) is often the hardest to fix
Budget readiness means having realistic expectations, not necessarily large budgets

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