Australian Businesses Still Stuck in the AI Hype Cycle
Data Governance Failures, ROI Gaps and Unclear Strategy Slowing Progress
Despite artificial intelligence (AI) dominating headlines and boardroom agendas, new data by Australia’s largest Systems Integrator, Brennan, reveals many organisations remain trapped in the AI hype cycle, struggling to convert its potential into proven business value.
A poll of IT leaders and decision makers from medium and large organisations - conducted during Brennan’s Cutting through the AI hype: From Concept to Reality series of presentations - found return on investment (ROI), data governance and the lack of a clear AI strategy were among the key challenges hindering AI adoption.
Key findings from the poll include:
* 41 per cent of respondents said identifying business use cases was their biggest concern for pushing for AI adoption
* 32 per cent cited building a strategy amidst the AI hype as their top challenge
* 20 per cent struggled most with defining ROI from implementing AI into their organisation
* Seven per cent said securing funding was their biggest barrier
* 60 per cent said the use of AI in their organisation was decentralised, with "everyone doing their own thing"
* Only eight per cent reported successful implementation of a centralised AI delivery model within their organisation 17 per cent said while there was a centralised delivery model, shadow AI was engaged within their organisation as the centralised approach was ineffective; and,
* Four per cent said they were completely blocked from utilising AI in their workplace
Brennan’s Head of Digital, Steve Anderton, said the poll results and feedback gathered throughout the series, which was attended by almost 200 IT decision makers a - showed AI maturity remained inconsistent, with data governance, security and infrastructure readiness falling short of what’s needed to support AI ambitions.
“While leadership is under pressure to act on AI, many organisations were trying to race ahead without clear guardrails, proven use cases or the right data foundations in place,” Mr Anderton said.
Brennan’s Head of Data and AI, Alex Shuttleworth, said the responses from the survey revealed concerning controls around the use of AI in organisations.
“While a decentralised or Shadow AI approach can accelerate value creation by AI and foster innovation, organisations must acknowledge the substantial data security, compliance, and governance risks and seek a balanced approach that fosters innovation, while controlling exposure,” Mr Shuttleworth said.
Shuttleworth and Anderton said there were several insights borne out of Cutting through the AI hype: From Concept to Reality that organisations need to consider as they progress in their AI journey. They are:
Don't try and eat the elephant all at once: Instead of trying to sell the whole framework, utilise a micro innovation approach, by proving the use case of some AI quickly and showing tangible value. Afterall, there is a fair level of scepticism from CFO’s, with a recent ADAPT research finding that 60 percent didn’t believe their organisation could build effective use cases for AI
Rapid AI Evolution Demands Strategic Focus: With the rapid evolution of AI, organisations need to be nimble with their AI strategy, including selecting the right investments, training their teams, and adapting infrastructure for long term value.
Measure Tangible Business Outcomes: Productivity gains are often touted as the main benefit of AI, but this rarely translates into a measurable ROI unless headcount reduction is involved. Instead, the value of AI should be evaluated across revenue growth, cost savings, risk mitigation, and enhanced customer experience.
Do not Overlook Data Governance: Governance and compliance are often given less priority when compared to metrics like speed of implementation and customer experience, yet these remain vital to sustaining AI initiatives. Poor governance could lead to data breaches, shadow IT proliferation, and flawed AI results.
Build an AI-Ready Data Platform: A modernised, adaptable data platform serves as the “gold” on which AI’s value is built. Organizations need a mature data infrastructure, including well-managed data pipelines and clear data stewardship, to ensure AI solutions are dependable and trustworthy so that AI ideas can progress to production.
Prioritise Proof over Hype: With five per cent of AI cases reaching deployment, identifying, and prioritising AI use cases is critical. Success depends on rapidly validating use cases tied to clear business value metrics. This process requires collaboration across IT, business units, and finance teams to align on objectives and unlock funding.
Managing Shadow AI: Shadow AI arises when business units adopt AI tools independently of IT, driven by unmet needs and pressure to innovate quickly. While this can accelerate value creation, it introduces substantial risks. Organisations must seek a balanced approach, including collaborative learning frameworks and selective centralization of AI governance.
Change Management and Collaborative Learning are Crucial: AI success requires more than deployment. It demands a culture of collaboration, continuous learning, and clear policies. Organizations must support staff in adopting AI safely and effectively, enabling experimentation without control.
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