Australia’s technology leaders have reached a clear consensus on AI’s strategic importance. Nearly three-quarters of ANZ organisations now rate it a core business priority, with AI and automation ranking as the second highest digital transformation objective for 2025, behind only cybersecurity. And yet IDC’s 2025 ANZ Digital Ecosystem Survey of 440 organisations found, in a separate question, that 73% of respondents agree their AI investments have not delivered expected returns. Two distinct verdicts from the same survey — one about intent, one about outcome.

This gap is not unique to Australia. IDC’s FutureScape: Worldwide AI-Fueled Business Strategies 2026 Predictions projects that in 2026, 45% of AI-fuelled digital use cases across Asia/Pacific will fail their ROI targets, held back by unclear gains, weak data foundations, and poor alignment between AI initiatives and core business objectives. The ambition is scaling. The execution is not keeping pace.

A structural constraint compounds this. IDC’s ANZ Digital Ecosystem Survey 2025 found that 41% of ANZ organisations cite access to the right skills as their single biggest obstacle to transformation. Siloed data (38%) and technical debt (35%) follow closely — reflecting a lack of readiness in technology foundation.
While the constraints are real, some sectors are further ahead. Australia’s major banks — which collectively invest over AU$7 billion in technology annually — have moved AI from experimentation into production at scale, with customer-facing AI assistants serving millions of users, AI-driven fraud prevention generating billions in prevented losses, and AI-powered engagement platforms delivering measurable commercial outcomes. For other industries, this is the benchmark for what production-grade AI looks like.

The lesson for ANZ CIOs is not to replicate banking’s technology stack. It is something more fundamental: skills and tech readiness constraints do not reduce the pressure to invest in AI — they raise the cost of investing in the wrong places. IDC’s Digital and AI Business Scorecard 2026 identifies what separates leading from nascent AI organisations: 63% of leaders find AI ROI straightforward to measure, against just 28% of nascent organisations. The differentiators are clearly defined baseline metrics, ROI embedded in project governance, and centralised performance dashboards — disciplines that cost relatively little but compound significantly. When capacity is constrained, the organisations that pull ahead concentrate AI investment on fewer, higher-value use cases, measure outcomes from the start, and build solid ROI case before scaling. In a constrained environment, focus is the strategy.