Where Does Your Business Sit on the AI Maturity Spectrum?
Most Australian businesses know AI could help — but aren't sure where to start or how far they could go. Our AI Maturity Framework gives you a clear picture of where you are today, and a practical path to the next level.
Take the Free Assessment (3 minutes) →Every business is on an AI journey, whether they realise it or not. The question isn't if you should use AI — it's how far along you are and what's the right next step.
We developed the PlusAI AI Maturity Framework after working with Australian businesses across professional services, operations-heavy industries, and technology. We've seen the same patterns: companies that try to leap from Level 1 to Level 4 waste time and money. Companies that take it one level at a time build lasting competitive advantages.
Here's how the four levels work.
The Four Levels of AI Maturity
Level 1: AI Explorer
Where you are: You're aware AI could help your business, but you haven't found the right entry point.
What this looks like in practice:
- Ad-hoc use of ChatGPT or Claude for drafting emails, brainstorming, or as a Google alternative
- No connection between AI tools and your actual business data
- AI outputs feel generic — they don't understand your industry, customers, or processes
- No measurement of whether AI is actually saving time or improving quality
Example:
A consulting firm asks ChatGPT to draft a client proposal, but the output is so generic it takes longer to edit than to write from scratch. The AI doesn't know the client's history, your service offerings, or your pricing model.
Your next move:
Document your team's top 3 most time-consuming repetitive tasks. Try using AI for one of them this week. Measure how long the task takes with and without AI.
Level 2: AI-Assisted
Where you are: Your team is actively using AI tools, and some people have built custom prompts or workflows for recurring tasks.
What this looks like in practice:
- Custom GPTs or saved prompts for specific tasks (report templates, email drafts, research workflows)
- AI assists with tasks but every output needs human review and editing
- Business data is manually copy-pasted into AI tools rather than connected
- Individual productivity gains of 2–5 hours per week, but not systematic
Example:
An accounting team has built a custom GPT for drafting client communications, but they manually copy in each client's details and financial summary every time. It helps, but doesn't scale.
Your next move:
Identify which of your data sources would be most valuable for AI to access directly. Pick one workflow where AI currently helps and measure the exact time savings.
Level 3: AI-Integrated
Where you are: AI systems are connected to your live business data and handling defined workflows with minimal oversight.
What this looks like in practice:
- AI systems access your databases, documents, and data feeds via RAG or API integrations
- Defined workflows run with AI handling the heavy lifting — humans review outputs rather than creating them
- Consistent, measurable time savings: 10–20+ hours per week across the team
- AI handles routine cases; humans focus on edge cases and strategic decisions
Example:
A property management company's AI system pulls from maintenance logs, tenant records, and contractor databases to auto-generate work orders, triage urgent requests, and draft tenant communications. Staff review and approve rather than writing everything from scratch — cutting admin time by 60%.
Your next move:
Audit which AI workflows still require manual review and evaluate if oversight can be reduced. Identify the next 3 processes to automate based on time savings and business impact.
Level 4: AI-Native
Where you are: AI is the primary interface for large parts of your operations.
What this looks like in practice:
- AI agents handle multi-step workflows autonomously (research → analysis → draft → review → delivery)
- Real-time monitoring and alerts powered by AI across operations
- Continuous improvement: AI systems learn from feedback and outcomes
- Your team's role has shifted from "doing the work" to "directing AI and making strategic decisions"
- AI capabilities become a competitive moat that competitors can't easily replicate
Example:
A recruitment firm's AI system scans new job briefs, searches and ranks candidates from multiple databases, generates personalised outreach sequences, schedules interviews, and compiles hiring reports — all with minimal human input. Recruiters focus on relationships and negotiations. The firm fills roles 3x faster than competitors still doing it manually.
Your next move:
Explore fine-tuning models on proprietary data. Build autonomous agents for complex multi-step processes. Consider how AI creates new revenue opportunities, not just cost savings.
How We Help You Level Up
No matter where you're starting, we have a clear path to the next level.
| Your Level | What We Do | Timeline | Expected Outcome |
|---|---|---|---|
| Level 1 → 2 | AI Discovery workshop. Identify your highest-value automation targets and build your first custom AI workflows. | 2–4 weeks | First measurable time savings. Clear roadmap for expansion. |
| Level 2 → 3 | Connect AI to your business data. Build RAG systems, API integrations, and automated workflows. | 4–8 weeks | 10–20+ hours saved per week. AI handling defined workflows autonomously. |
| Level 3 → 4 | Scale and optimise. Deploy AI agents, build continuous learning systems, and develop AI-native products. | 8–16 weeks | AI as competitive moat. New capabilities and revenue streams. |
Find Out Where You Stand — Free, in 3 Minutes
Take our AI Maturity Assessment to get your personalised score across 6 pillars, your current maturity level, and specific next steps tailored to your business.
What's Your AI
Maturity Level?
Answer 6 quick questions to discover where your business sits on the AI maturity spectrum—and what to do next.
Ready to Level Up Your AI Maturity?
Or skip the quiz and book a free discovery call — we'll walk through the framework together and identify your highest-value AI opportunity.
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