Most B2B organisations have AI activity. A pilot here, a tool subscription there, a team experimenting in the background. What most don’t have is a clear read on where they actually sit — how their AI maturity compares to the market, where the real gaps are, and what to fix first.
That’s the problem the AI Maturity Index was built to solve. And to solve it properly for Australian organisations — not just import a global benchmark that doesn’t reflect local market conditions — we need Australian businesses in the data.
Here’s the exchange. You give us ten minutes and an honest set of answers across five pillars. We give you back a real score, a benchmark comparison against the world’s leading AI research and the emerging Australian dataset, and a report from g2m’s AI go-to-market specialists with the three things to fix first. Your answers are secure and anonymised. No strings.
TL;DR
• AI maturity measures how effectively an organisation has embedded AI into strategy, data, technology, process, and people — not just which tools it has bought.
• The global research baseline sits at around 20% on a normalised 0–100% scale. Most organisations are closer to ‘exploring’ than ‘embedded.’
• The gap between AI leaders and everyone else is widening, not narrowing — BCG, McKinsey, RAND and others all point the same way.
• AI maturity is a leadership and process problem, not a technology problem. The organisations winning treat it as a go-to-market strategy, not an IT project.
• The g2m AI Maturity Index benchmarks your organisation across five pillars and tells you where to focus first. Free. 10 minutes. Instant results.
AI maturity is the degree to which an organisation has moved from experimenting with AI to embedding it as a core part of how the business operates, generates revenue, and serves customers. It is not a measure of which AI tools a business has purchased. It measures whether AI is genuinely integrated into strategy, data infrastructure, technology architecture, business processes, and the people capability to use it well.
A mature AI organisation doesn’t just use AI — it has accountable leadership, clean and connected data, deliberate technology decisions, redesigned processes, and a team with the literacy and confidence to operate in an AI-augmented environment. Most organisations have one or two of these. Very few have all five.
AI maturity matters because the commercial gap between AI leaders and everyone else is widening. BCG’s The Widening AI Gap (September 2025) found that leaders are pulling further ahead, not closer to the pack — generating 2× the revenue growth of laggards, plus 40% more cost savings. McKinsey puts the proportion of organisations with genuinely mature AI strategies at around 1%. RAND Corporation found 80% of AI projects fail to deliver their intended business value across 2,400+ enterprise initiatives.
The implication for B2B organisations is direct: the longer AI maturity is treated as a technology project rather than a commercial priority, the wider the competitive gap becomes. This is not a future risk. It is a current one.
“The difference between AI leaders and everyone else isn’t narrowing as the technology matures — it’s widening.”
— BCG, The Widening AI Gap, September 2025
The research baseline across the g2m AI Maturity Index — drawn from Accenture, BCG, RAND Corporation, IDC, Gartner, ServiceNow and McKinsey, all published in 2025 or early 2026 — puts the average organisation at around 20% on a normalised 0–100% scale. That places most organisations in the ‘exploring’ band: aware of AI, taking initial steps, but far from embedding it systematically.
To be transparent: these are research-derived estimates, not yet scores from real respondents to the Index. They are conservative composites of the published findings above, normalised to our five-pillar framework. As Australian organisations complete the Index, the benchmark shifts from research-derived to data-derived — and we’ll say so clearly when that happens.
The pattern across all sources is consistent regardless of who’s measuring: the overwhelming majority of organisations are still in early exploration, with only a small fraction operating at an optimised level. The gap is real, and it is measured the same way no matter which research house you read.
The g2m AI Maturity Index measures AI maturity across five interdependent pillars. Each one represents a distinct way organisations get stuck — and together they reveal where the real constraint sits.
Whether AI has an accountable owner with a business case tied to revenue or efficiency outcomes — or whether it’s a collection of individual experiments nobody is responsible for.
Research baseline: 23% — most organisations have someone interested in AI. Far fewer have someone accountable for what it delivers.
Whether customer and commercial data is clean, accessible and connected enough for AI to use — across CRM, support, finance and product data, not siloed across disconnected systems.
Research baseline: 20% — the pillar that quietly kills more AI initiatives than any other. No model is better than the data feeding it.
Whether AI tool decisions are deliberate — a considered build/buy/orchestrate framework — or a pile of disconnected point solutions adopted by whoever found them first.
Research baseline: 27% — the highest-scoring pillar. Buying tools is easy. Orchestrating them is not.
Whether AI is being used to redesign processes around what it makes possible — or simply to automate existing broken workflows.
Research baseline: 17% — the second-lowest pillar. Most AI investment is bolted onto old processes rather than used to rethink them.
Whether teams have the AI literacy and psychological safety to experiment productively, and whether that capability is sustained rather than a single training event.
Research baseline: 13% — the lowest-scoring pillar across all research. And the one that most often makes everything else fail.
The five pillars fail in combination, not independently. An organisation can score relatively well on technology architecture (27%) and still get almost nothing from its AI investment — because the people pillar (13%) and process pillar (17%) are the actual constraints. A capable tool handed to a team that’s never been given time or permission to learn it, bolted onto a process nobody has redesigned, will produce expensive underperformance regardless of how sophisticated the technology is.
The interdependencies run in every direction. Strategy without process redesign produces pilots that never scale. Technology without data readiness produces tools that hallucinate with confidence. Process redesign without people enablement produces a workflow nobody uses. This is why the Index measures all five, and why the most useful output is the pattern of scores across pillars — not any single number.
AI maturity correlates far more strongly with commercial leadership behaviour than with technical sophistication. The organisations scoring well on strategy and process pillars in the Index are not the ones with the largest data science teams. They are the ones where a revenue or commercial leader took ownership of the question: where does AI change how we generate, qualify, and grow revenue? That question produces different decisions, different investments, and different outcomes than the equivalent IT question: which technology should we adopt?
When AI sits with IT, organisations get a technology rollout with patchy commercial adoption. When it sits with revenue leadership — sales, marketing, customer success, RevOps — organisations redesign how they sell and serve customers, with technology as the enabler rather than the headline. The businesses getting real value from AI right now are treating it the same way they would treat a new go-to-market motion: leadership-owned, process-redesigned, measured against pipeline velocity, deal size, retention, and cost to serve.
We don’t know yet — and that is the honest answer. Every existing AI maturity benchmark is global. None of it reflects the specific constraints, market dynamics, and pace of adoption of Australian B2B organisations. The Index is seeded from international research, and the Australian-specific dataset is being built now, as organisations complete the assessment.
What we do know is that the global research baseline applies as a starting point: most organisations, in any market, sit around 20% on a normalised scale, in the ‘exploring’ band. The distribution in Australian organisations may differ — and finding out is precisely why we’re building the benchmark. Every organisation that completes the Index contributes a secure and anonymised data point to what will become the first real Australian AI maturity dataset.
The AI Maturity Index walks through all five pillars and benchmarks your position against the research base, transparently labelled as research-derived until real Australian respondent data builds the picture further. You get your scores immediately, free, with no commitment. If you want the deeper read — a personalised report from g2m’s AI go-to-market specialists with your specific gaps and the three things to fix first — that’s included too.
It takes ten minutes. Your answers are anonymised and go straight into building a benchmark Australian businesses haven’t had before. For most leadership teams, it’s the first time anyone has put a number on the question rather than a "vibe."
Free · 10 minutes · Instant score · Personalised report from g2m’s AI go-to-market specialists
Frequently Asked Questions
What is the AI Maturity Index?
The AI Maturity Index is a free online assessment built by g2m Solutions that measures an organisation’s AI maturity across five pillars: Strategy & Executive Mandate, Data Readiness, Technology Architecture, Process Redesign, and People, Culture & Sustained Enablement. It benchmarks results against a global research baseline and, as the dataset grows, against Australian-specific respondent data. Results are instant. A personalised report with the top three priority recommendations is also available.
How long does the AI Maturity Index take?
The AI Maturity Index takes approximately ten minutes to complete.
Is the AI Maturity Index free?
Yes. The assessment and instant score results are completely free. The personalised report from g2m’s AI go-to-market specialists is also included at no cost.
What is a good AI maturity score?
On the g2m AI Maturity Index, scores are expressed as a normalised percentage from 0–100%. The research baseline puts the average organisation at around 20%, in the ‘exploring’ band. Scores above 50% indicate the ‘proficient’ band, meaning AI is actively embedded across most pillars. Scores above 75% indicate ‘advanced’ maturity. Most organisations score lower than they expect, particularly on process redesign (17% baseline) and people enablement (13% baseline).
Why is AI maturity important?
AI maturity is important because the commercial gap between AI leaders and organisations still in early exploration is widening. BCG research found that AI leaders generate 2× the revenue growth of laggards and 40% more cost savings. RAND Corporation found 80% of AI projects fail to deliver intended value. Measuring AI maturity identifies where the specific gaps are — and which gap is constraining the others — so organisations can invest in the right things rather than adding more tools to an already fragmented system.
What is the difference between AI maturity and AI readiness?
AI readiness typically refers to the technical and infrastructure prerequisites for deploying AI. AI maturity is a broader concept that includes strategy, leadership accountability, data quality, process redesign, and people capability alongside technology. An organisation can be technically ready to deploy AI tools but still have very low AI maturity if it lacks executive ownership, clean data, or processes redesigned around AI capabilities.
How is the AI Maturity Index benchmark calculated?
The current benchmark is research-derived — a composite of findings from Accenture, BCG, RAND Corporation, IDC, Gartner, ServiceNow and McKinsey, all published in 2025 or early 2026, normalised to the g2m five-pillar framework. It is not yet drawn from real respondents. As Australian organisations complete the Index, the benchmark will shift from research-derived to data-derived. g2m will communicate clearly when that transition occurs.
Want to go deeper on each pillar? Read the Five Pillars of AI Maturity series:
• Part 1: Who Should Own AI in Your Organisation? The Case for an Executive Mandate
• Part 2: Why Your Data Is Probably Your Biggest AI Constraint
• Part 3: Why Buying AI Tools Isn’t the Same as Having a Strategy
• Part 4: The Difference Between Automating and Redesigning
• Part 5: Why Training Events Don’t Build AI Capability