AI Decision Guides 10 min read

AI Readiness Checklist for Australian Businesses

Assess your organisation's readiness for AI across data, processes, people, technology, and budget with this practical checklist.

Best for: Business owners, IT leaders Practical guide for business decision-makers

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    AI trained on outdated data gives outdated answers.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
AI StrategyRAGWorkflow AutomationData

Meet the person

Written by the person who does the work

This guide comes from real projects. If it raises a question about your own system, you can ask the founder directly.

HELLO PEOPLE designs, builds and looks after AI, software, app and data solutions for Australian businesses, with senior expertise on every project and a scope agreed before work starts. For AI, that means working on your own data, with a person able to check every answer.

Since 2007, HELLO PEOPLE has delivered more than 100 projects from Perth for small and medium businesses across Australia: custom software and apps, system integrations, data migrations, reporting and dashboards, and AI that works inside the systems a business already runs.

I lead every engagement myself. I trained in accounting before moving into IT, hold accounting and IT professional qualifications and an MBA, and bring more than 20 years of experience across sales, service delivery, inventory and compliance. I am also a PhD candidate in AI at Curtin University, researching retrieval-augmented generation (RAG), so the technology is always judged by what it does for the business.

  • An old-fashioned service

    Small and boutique. The person who scopes your AI project is the person who builds it, and the same person checks its answers before your team relies on them.

  • Quick responses

    No ticket queue and no account manager in between. You hear back within one business day, usually sooner.

  • A long-term partner

    The first AI project is the start, not the end. When you need the next system, integration or report, you call the same person, who already knows your business.

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Kasun Wijayamanna, Founder Kasun Wijayamanna
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