AI adoption is advancing across Australian organisations, but the research reveals a significant gap between experimentation and the ability to translate AI investment into measurable business value.
Our 2026 report provides an evidence-based view of how Australian organisations are progressing from AI experimentation towards adoption, scale and measurable business value.
say managers aren't equipped to lead AI-enabled teams.
report consistent team-level or embedded AI use.
don't measure how many AI experiments make it into production.
cannot demonstrate broad measurable ROI from AI.
report that they have an AI usage policy.
report a mature AI operating model.
As Australian organisations move past experimentation and try to scale, leaders are asking about strategy and governance, workforce readiness, business value, and what separates the organisations pulling ahead from the rest. Here are some of the most common questions leaders are asking about AI adoption and ROI.
AI maturity remains concentrated in the earlier stages. Almost two thirds of organisations are still Exploring or Experimenting, while relatively few have progressed to Scaling or AI Embedded maturity.
24% are still Exploring, 41% Experimenting, compared with just 8% Scaling and 3% Embedded.
For every organisation scaling or embedding AI, roughly six are still exploring or experimenting.
For many organisations, AI adoption is no longer the primary challenge. The more important question is whether AI is delivering measurable business value.
43% report no measurable ROI from their current AI initiatives, and only 1% report organisation-wide ROI measured and reported.
89% cannot demonstrate broad measurable ROI from AI.
Clear ownership is one of the foundations organisations need to move beyond isolated experimentation.
Responsibility for AI most commonly sits with CIO/Technology, accounting for 48% of respondents, while 13% of respondents are unsure who owns AI at all.
Managers play a critical role in enabling AI adoption, but capability has not kept pace.
Only 12% report that most managers have been trained, and 44% identify a lack of management capability as a barrier to greater business value, the most frequently cited barrier in the survey.
Nearly 9 in 10 organisations have not trained most of their managers to lead AI-enabled teams
The findings point to a gap between AI adoption and organisational integration, individual use is becoming established, but workflow redesign and embedded practice are not.
66% have an AI usage policy, yet just 3% report a mature AI operating model.
Policies are 33X more common than embedded AI operating practices.
Organisations further along the AI maturity journey are distinguished not by a single capability, but by a combination of stronger governance, workforce readiness, adoption, delivery maturity and measurable business value.
Five lessons from AI leaders: governance enables scale, capability drives adoption, leaders move pilots into production, workflow redesign creates value, successful organisations measure outcomes.
The benchmark scorecard highlights a clear gap between establishing the foundations for AI and translating those foundations into measurable business value.
Strategy & Governance leads at 57/100, while Business Value trails at just 27/100.
60% of respondents identify increasing AI skills as their top priority for the next 12 months, the most frequently cited priority in the benchmark.
