Where do Australian enterprises actually stand on AI maturity? Not where vendors say we are, not where we aspire to be – where are we really?

I’ve been tracking this question through client engagements and industry conversations. Here’s my assessment of the Australian enterprise AI landscape in late 2024.

The Maturity Framework

Before assessing where organisations stand, we need a common language. I use a five-level maturity model:

Level 1 - Awareness: Exploring AI possibilities, no production deployments.

Level 2 - Experimentation: Pilots underway, some proofs of concept completed.

Level 3 - Adoption: Production AI in specific use cases, measurable value.

Level 4 - Scaling: AI across multiple business areas, systematic governance.

Level 5 - Transformation: AI embedded in strategy, competitive differentiator.

The Australian Picture

Based on conversations with CIOs and transformation leaders across approximately 40 mid-to-large Australian enterprises, here’s the distribution:

Maturity Level	Percentage
Level 1 (Awareness)	15%
Level 2 (Experimentation)	40%
Level 3 (Adoption)	30%
Level 4 (Scaling)	12%
Level 5 (Transformation)	3%

The majority – 55% – are still in early stages (Levels 1-2). Only 15% have achieved scaling or transformation.

This isn’t failure. It reflects where global enterprise AI genuinely is, despite the hype. But it does suggest significant runway for improvement.

Patterns by Industry

Maturity varies significantly by sector:

Financial Services (Average: Level 3.2) The most mature sector. Regulatory pressure (fraud, compliance), rich data assets, and technology-forward culture drive adoption. Most banks have production AI for fraud detection, credit risk, and customer service.

Mining and Resources (Average: Level 2.8) Strong on operational AI (predictive maintenance, optimisation) but weaker on enterprise AI. Legacy technology and remote operations create challenges. Those who’ve invested are seeing real results.

Healthcare (Average: Level 2.4) Regulatory caution and data sensitivity slow adoption. Medical imaging AI is advancing, but administrative AI lags. Privacy concerns are legitimate but sometimes used as excuses for inaction.

Retail (Average: Level 2.6) Strong on demand forecasting and personalisation. Weaker on operational AI. Competition is driving investment, but talent constraints limit execution.

Government (Average: Level 1.9) Significant variation. Some agencies are genuinely innovative. Many are stuck in procurement processes not designed for agile AI development. Risk aversion dominates.

Professional Services (Average: Level 2.3) Knowledge work should be AI-friendly, but partnership structures and billable hour models create awkward incentives. Document analysis and research support are common; deeper integration is rare.

What Separates Leaders from Laggards

Organisations at Level 4+ share common characteristics:

Executive sponsorship: Not just lip service – active involvement and accountability.

Data maturity: Leaders invested in data infrastructure years before AI became hot.

Talent strategy: Mix of internal capability building and strategic external partnerships.

Realistic expectations: They pursue specific business value, not AI for its own sake.

Governance that enables: Risk management that guides rather than blocks.

Learning culture: Willingness to experiment, fail, and iterate.

Conversely, laggards share these patterns:

AI tourism: Lots of exploring, little doing. Endless strategy sessions without execution.

Data denial: Believing data is “good enough” without assessing reality.

Vendor dependence: Waiting for vendors to solve problems rather than building capability.

Fear-based governance: Treating AI as a threat to be contained rather than a capability to develop.

The Productivity Tool Question

Microsoft Copilot and similar tools have created an interesting dynamic. Many organisations count Copilot deployment as “doing AI” when it’s really just licensing software.

The question: does Copilot adoption indicate AI maturity?

My view: it’s necessary but not sufficient. Copilot deployment without change management, training, and measurement is just purchasing licenses. Real productivity AI maturity requires:

• Understanding what use cases deliver value
• Training users to prompt effectively
• Measuring actual productivity gains
• Iterating based on what works

Organisations that treat Copilot as another software rollout miss the point.

Where Investment Is Going

Based on conversations about 2025 budgets:

Increasing investment (60% of organisations):

• Productivity AI (Copilot, etc.)
• Customer service automation
• Internal knowledge management (AI consultants Sydney like Team400 are gaining traction)
• Process automation

Steady investment (30%):

• Existing AI initiatives
• Data infrastructure (often the wisest choice)
• Governance and security

Decreasing investment (10%):

• Custom AI development
• Experimental initiatives
• Unproven use cases

The shift toward proven applications and away from experimentation reflects post-hype pragmatism.

Barriers to Progress

What’s holding organisations back?

Talent (cited by 78%): Can’t hire data scientists, can’t retain them, can’t develop them fast enough.

Data quality (67%): Know the data isn’t ready, struggle to prioritise fixing it.

Unclear ROI (54%): Difficulty measuring and demonstrating AI value.

Legacy systems (51%): Technical debt blocking integration.

Risk/governance concerns (43%): Security, privacy, and regulatory uncertainty.

Budget constraints (38%): Competing priorities for limited funds.

Notably, “technology limitations” was cited by only 22%. The technology works. Everything around it is hard.

What Comes Next

Predictions for Australian enterprise AI in 2025:

Consolidation around platforms: Fewer custom builds, more platform adoption.

Governance maturation: From “block everything” to “enable with guardrails.”

Productivity AI focus: Less experimentation, more value from existing tools.

Talent crunch continues: Competition intensifies, internal development becomes critical.

Data investment catches up: Recognition that data quality limits AI value.

Regulation shapes decisions: EU AI Act implications, potential Australian regulation.

Practical Recommendations

Based on this assessment, recommendations for different maturity levels:

Level 1-2 organisations: Don’t try to leapfrog. Build foundations: data quality, governance basics, small pilots with clear value.

Level 3 organisations: Focus on extracting more value from existing AI before expanding scope. Measure and optimise current deployments.

Level 4+ organisations: Think strategically about AI as competitive advantage. Build unique capabilities, not just efficiency gains.

Everyone: Invest in people. Talent constraints are real and won’t resolve quickly. Internal development is as important as hiring.

Final Thought

Australian enterprises are progressing on AI, but the hype has run ahead of reality. Most organisations are still in early stages, which is fine – the opportunity remains.

The leaders aren’t necessarily the ones doing the most innovative AI. They’re the ones extracting real value from practical applications, building methodically on solid foundations.

That’s the model worth emulating.