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Most B2B organisations have AI activity. Someone’s trialling a tool. A team has a pilot running. There’s a vendor conversation happening somewhere. What most don’t have is accountability — a named person with a mandate, a budget, and a metric tied to what AI actually delivers for the business.
That’s the gap the first pillar of the AI Maturity Index measures. And it’s the gap that, left unfilled, ensures everything else — your data investments, your technology choices, your process redesign efforts — stays fragmented and never compounds.
The research baseline for Strategy & Executive Mandate across the g2m AI Maturity Index sits at 23%. Most organisations are closer to ‘exploring’ than ‘embedded.’ Most have someone interested in AI. Far fewer have someone accountable for what it delivers.
“C-suite leaders who are deeply engaged with AI are 12× more likely to be among the top 5% of companies winning with AI innovation.
BCG, The Widening AI Gap, Sep 2025
An executive AI mandate is not a working group. It’s not a committee, a task force, or a ‘centre of excellence’ that meets monthly and produces strategy documents. Those structures are where AI accountability is diffused.
A genuine mandate means three things are true simultaneously:
• A named executive owns AI ROI — not as a side project, but as a primary accountability
• That ownership comes with a defined budget tied to specific business outcomes — revenue, cost reduction, customer retention — not technology metrics like ‘tools deployed’ or ‘seats activated’
• AI is embedded in annual planning cycles and reviewed at the leadership level on the same cadence as commercial performance
The maturity question isn’t whether your CEO mentions AI in town halls. It’s whether your CEO can name your top three AI initiatives and their expected commercial impact. If not, AI is still being treated as an IT project.
Ownership determines outcomes. Not because of org chart politics, but because the owner determines the question being asked.
When AI ownership sits with IT, the question being answered is: ‘which technology should we adopt?’ That produces tool evaluations, procurement decisions, and infrastructure projects. Occasionally useful. Rarely transformative.
When AI ownership sits with a commercial or revenue leader, the question changes: ‘Where does this change how we generate, qualify, and grow revenue?’ That produces redesigned sales motions, smarter customer acquisition, faster time-to-close, and AI embedded in how the business actually operates.
Same technology. Completely different outcome. The difference is entirely a function of who’s asking the question.
The organisations winning on AI aren’t the ones with the biggest technology budgets. They’re the ones where a revenue leader took ownership of the question.
In the AI Maturity Index, the lowest maturity level on this pillar — score 1, ‘not started’ — is described as: AI is not formally on our strategic agenda.
In practice, this looks like:
• AI conversations happening at the team level with no executive visibility
• Tool subscriptions are being expensed through departmental budgets with no central tracking
• No board or leadership team discussion of AI as a strategic risk or opportunity
• No one who could answer the question: ‘What is our AI strategy and what’s it worth to us?’
This describes more organisations than most leaders would admit. The tell is usually that AI activity is present but invisible — happening in pockets, owned by individuals, and entirely dependent on those individuals staying around.
The highest maturity level on this pillar is: AI is a core plank of our business strategy with named ownership and measurable targets.
Concretely, this means:
• A named executive — typically the CEO, CCO, or CRO in a B2B context — owns AI ROI with a performance target attached to it
• AI features in the annual operating plan, not just the innovation roadmap
• Investment decisions are evaluated against business outcomes, not technology capability
• The board receives regular reporting on AI performance alongside financial and commercial metrics
Organisations at this level treat AI the same way they treat a new go-to-market motion: a leadership-owned initiative with a business case, a change plan, and a measure that matters. Not a technology deployment measured by adoption rates.
This is the pillar that determines whether the other pillars compound or fragment.
Without a clear executive mandate, data readiness investments lack commercial direction — you clean data, but nobody knows what problem it’s meant to solve. Technology decisions are made bottom-up by whoever has the budget, resulting in a stack of disconnected tools. Process redesign never happens because nobody has the authority to change how the business operates. And people enablement remains ad hoc because there’s no strategic rationale for investing in it systematically.
Strategy is the pillar that makes the others coherent. It’s not sufficient on its own — a clear mandate without data or process backing is just a slide deck. But without it, nothing else scales.
This is why the AI Maturity Index measures all five pillars together, not in isolation. The pattern of scores across pillars tells you more than any single number. And the Strategy pillar score is almost always the upstream cause of weakness everywhere else.
The g2m AI Maturity Index measures Strategy & Executive Mandate across five questions: how AI features in your strategic priorities, who owns accountability for outcomes, how AI investment is evaluated, how embedded AI is in your go-to-market model, and the depth of active leadership engagement.
Your score on this pillar — and how it interacts with your scores on data, technology, process, and people — is where the useful diagnostic starts.
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This is Part 1 of the Five Pillars of AI Maturity series.
Read the full 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
Or start with the overview:
→ We’re Building Australia’s First AI Maturity Benchmark — Here’s Why We Need You In It
A: The right owner is the leader who already owns revenue and customer outcomes. In most B2B organisations, that is the CEO, CCO, or CRO. IT, data, and operations leaders are critical partners, but when they own AI, the work tends to optimise technology rather than transform how you generate, qualify, and grow revenue.
A: Not necessarily. Many organisations are better served by making AI a core accountability of an existing commercial leader, with clear targets and budget. A standalone AI role can work, but only if it has direct commercial accountability and a clear line into the core go-to-market decision makers. -
A: No. Early-stage experimentation is exactly when you need clarity on ownership and outcomes. A mandate does not mean a fully formed strategy; it means someone is responsible for turning experiments into a coherent portfolio of initiatives with measurable business impact.
A The absolute dollar figure matters less than the link between spend and outcomes. Start by defining one or two priority use cases (for example, reducing sales cycle length, improving lead qualification, or increasing marketing-originated revenue), then allocate budget against those outcomes. The mandate should be to prove value in a defined area, then scale.
A: Useful leading indicators include: - AI initiatives appear in your annual operating plan, not just innovation slides - Leadership meetings review AI performance alongside core commercial metrics - Sales, marketing, and service teams can articulate how AI changes their day-to-day work - Pilots are being shut down or scaled based on business impact, not excitement about tools
A: The Index translates a vague idea of “we should be doing more with AI” into a structured picture of where you stand today: how AI features in your strategy, who owns it, how it is funded, and how leadership is engaged. That shared baseline makes it easier for executives to agree on where to focus first and how to connect AI decisions to commercial outcomes.
Chris Fell: 21/07/2026 11:53:49 AM
People, Culture and Sustained Enablement is the lowest-scoring pillar in the entire AI Maturity Index research baseline, at 13%. Lower than strategy....
Chris Fell: 21/07/2026 11:53:24 AM
Process Redesign is the second-lowest scoring pillar in the AI Maturity Index research baseline, at 17%. Only people and culture scores lower. And...
Chris Fell: 21/07/2026 11:53:03 AM
Technology Architecture is the highest-scoring pillar in the AI Maturity Index research baseline, at 27%. On the surface, that sounds like good news...
Chris Fell