What can CIOs and CDAOs learn from ADAPT’s State of the Nation: Data and AI in Australia 2025?

Across Australia’s enterprise and midmarket, leaders are spending an average of $28 million annually on AI and data platforms.

But ADAPT’s latest research shows that Australia’s AI agenda is strong on investment but weak on readiness.

72% of CDAOs admit their initiatives are failing to deliver expected outcomes.

While boards increasingly see AI as strategic, only 24% say their organisations have AI-ready data architectures.

Weak data foundations, lagging governance, and insufficient workforce readiness are leaving even the best-funded projects unable to prove measurable impact.

To unpack these challenges, ADAPT’s Head of Programs & Value Engagement Byron Connolly sat down with Content Marketing Manager Justina Uy to explore what organisations must do differently to translate investment into value.

Fix the value gap by connecting AI to measurable business outcomes

Many organisations are chasing AI projects for the sake of adoption rather than impact. As Byron explained:

“Just because you can do AI doesn’t mean you should. Have a think about where AI is really going to benefit the business and just don’t throw it at any old thing.”

Healthcare providers and mid-market firms exemplify this problem. Healthcare reports an 83% ROI shortfall, and mid-market firms 71%.

Too often, AI is confined to low-impact administrative tasks rather than areas like pricing, customer experience, or strategic decision-making where returns are greater.

Byron stressed the need for stronger cross-functional ownership.

Scorecards shared between IT, data, and finance functions are critical to tie AI performance to tangible outcomes: operational efficiency, compliance, cost reduction, or customer engagement.

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Data is still the Achilles’ heel of AI

Less than one in four Australian organisations say their data is AI-ready.

Fragmentation, duplication, and low-quality inputs continue to undermine execution.

Byron highlighted that one solution is a shift toward Zero Copy Architecture and semantic layers.

These approaches can unify fragmented ecosystems and enable secure, real time workloads across multi cloud and hybrid environments.

Without these architectural fixes, even advanced models run on unstable ground.

“They should invest in semantic layers to unify fragmented ecosystems and enable secure, real-time AI workloads across their cloud and hybrid environments. Oftentimes they don’t necessarily know where their data is, so they need to work that out.”

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Governance blind spots are widening as adoption accelerates

Although AI is treated as a strategic priority, governance has not caught up. Only 26% of organisations have formal AI ethics structures in place.

Connolly emphasised the risks:

“They need to prioritise their risk controls, ownership structures, and also traceability in high-impact use cases because a lot of organisations have to meet regulatory and societal expectations when they roll out these AI solutions”.

Formal frameworks spanning sourcing, model development, deployment, and monitoring are essential. Without them, experimentation risks trust, compliance, and reputation.

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Talent and training deficits threaten long term readiness

AI adoption is outpacing workforce capability.

Only 6% of enterprises mandate organisation-wide AI training, and one in four have no preparation plan at all.

Byron argued that CDAOs and CIOs must orchestrate cross functional delivery units that bring together technology, finance, and line of business executives to ensure projects are supported by sustained funding and coordinated execution.

Embedding data literacy across the enterprise is also critical. Yet this remains one of the hardest execution gaps to close.

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Recommended actions for data and AI leaders

Australian organisations seeking to close execution gaps and realise ROI should take the following steps now:

• Tie AI initiatives to business value: Use shared scorecards across IT, data, and finance to measure efficiency, cost, compliance, and customer impact.
• Stabilise the data foundation: Invest in Zero Copy Architecture and semantic layers to eliminate duplication, unify ecosystems, and support secure real time workloads.
• Formalise governance end to end: Establish ethical and operational frameworks covering the full AI lifecycle: from data sourcing to monitoring.
• Upskilll at scale: Mandate enterprise wide AI training and embed data literacy to ensure employees can both use and govern new tooling.
• Be selective in AI deployment. Focus effort on use cases tied to commercial value.

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Australia’s AI leaders face a defining moment.

Investment is strong, ambition is clear, but execution gaps persist across architecture, governance, and capability.

As Byron concluded, the report shows that while most organisations are struggling, the next one to two years will separate those experimenting from those building durable advantage:

“I think in the next year or two we’re going to see some organisations kicking some real goals and making a difference.”

To benchmark your organisation against national trends and learn how other CIOs and CDAOs are closing data gaps, structuring governance, and turning AI ambition into measurable impact, download ADAPT’s State of the Nation 2025: Data and AI in Australia report.