As the new financial year begins, there’s no shortage of headlines proclaiming AI as the defining technology of our era. Boards are authorising investments. Executives are setting ambitious “AI-first” targets. Consultants (yes, even those like me) are drafting playbooks for digital transformation in an AI-powered world.
But as the buzz grows louder, so does a quieter risk: most organisations aren’t asking the right questions about how AI will reshape their business. In the race to “leverage AI,” the blind spot isn’t the technology itself—it’s how we make decisions about it.
The AI Hype Loop—and Why It’s Dangerous
It’s easy to get swept up in the AI hype cycle. Proof-of-concept pilots abound. Generative tools like ChatGPT are already shifting how we work (Gartner, 2023; McKinsey, 2023). Yet behind the scenes, too many projects proceed without clear governance, critical challenge, or a rigorous understanding of risk.
The uncomfortable truth: Most AI initiatives don’t fail because the tech isn’t ready. They fail because leaders underestimate the decision risk—the risk that assumptions, biases, or untested models will quietly shape outcomes in ways nobody intended (HBR, 2024).
Recent studies show this gap is only growing. According to Stanford’s 2024 AI Index, adoption of AI outpaces current risk and assurance practices in most sectors (Stanford, 2024). And while over 80% of enterprises will be using generative AI by 2026, only a fraction have robust assurance processes in place (Gartner, 2023).
From “How Do We Use AI?” to “How Do We Govern AI-Driven Change?”
The most important shift for FY25 isn’t about chasing the next algorithm. It’s about upgrading how we govern AI-related transformation:
- Who owns the risk? AI projects often live at the intersection of IT, strategy, risk, and operations. If everyone “sort of” owns it, then nobody truly does (AICD & CSIRO, 2023).
- Where’s the independent challenge? When excitement is high, dissenting voices get drowned out. How often do leaders encourage rigorous, evidence-based challenge to AI business cases (McKinsey, 2023)?
- Are assurance processes evolving? Traditional assurance—post-mortem reviews and checklist audits—can’t keep up with the speed and opacity of AI initiatives. We need dynamic, real-time assurance that evolves as projects unfold (Stanford, 2024; WEF, 2023).
The Hidden Risk: Cognitive Bias at Scale
Perhaps the biggest AI blind spot is this: AI can scale human bias just as quickly as it can scale insight. If the data, assumptions, or decision frameworks going in are flawed, AI will magnify—not mitigate—those problems (HBR, 2024; AHRC, 2022).
Left unchecked, AI can give leaders a false sense of confidence in decisions that are no more robust than they were before. We risk automating not just insight, but also our blind spots.
A New Imperative for Transformation Leaders
So what should transformation leaders do differently in FY25?
- Interrogate Assumptions Early. Treat every assumption in your AI business case as a risk until proven otherwise (AICD & CSIRO, 2023).
- Embed Assurance Up Front. Bring independent assurance into AI projects from day one—not after the fact (WEF, 2023).
- Prioritise Decision Literacy. Invest in helping all leaders—not just the tech team—understand AI risk, governance, and the limits of automation (AHRC, 2022).
Final Thought
AI is here. It’s powerful. But the biggest risk isn’t missing out on the latest tool. It’s missing the hidden risks in how we decide to use it.
In FY25, let’s challenge ourselves to go beyond the hype—to govern AI with the same rigour we demand of every other critical decision.
If you’re looking to strengthen your organisation’s decision-making and assurance for AI-powered transformation, Rainman Advisory can help. Reach out to start a conversation.
References:
- Gartner (2023). Top Strategic Technology Trends for 2024
- McKinsey (2023). The state of AI in 2023: Generative AI’s breakout year
- Harvard Business Review (2024). How AI is Changing Decision Making
- Stanford HAI (2024). 2024 AI Index Report
- World Economic Forum (2023). AI Governance: A Holistic Approach to Implementing AI
- Australian Human Rights Commission (2022). Human Rights and Technology Final Report
- AICD & CSIRO (2023). Artificial Intelligence: Governance and Leadership
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