Gen AI worth $115bn to Australia’s economy but hurdles remain, says study

Generative artificial intelligence (Gen AI), predicted to add an annual value of $115 billion to the Australian economy by 2030, presents opportunities and obstacles for business leaders. New research from global leader for storage and information management services, Iron Mountains, reviewed these issues.

The study, conducted across six countries with critical data decision-makers, discovered that despite Gen AI usage by 97% of surveyed C-suite IT leaders in Australia, a series of common challenges persist. The hurdles include sourcing, protecting and preparing data for model training (41%), establishing and enforcing Gen AI policies (41%), creating and agreeing on a Gen AI strategy that aligns with organisational goals (37%) and determining ownership or copyright of Gen AI-produced content (33%).

Iron Mountain’s findings also highlighted a need for an individual in the form of a Chief AI Officer (CAIO), whose role would be to address these issues and pioneer Gen AI initiatives. Of the surveyed Australian C-suite leaders, 97% agreed that a CAIO could accelerate the adoption of gen AI within organisations. Despite this consensus, only 32% of global organisations have taken the step of appointing one. Still, the report revealed that 94% of organisations without a CAIO anticipate adding this role in the future.

The role of this specialist leader should involve the implementation of a strategy for optimal use of both physical and digital assets. The research indicated that 94% of Australian respondents saw a unified asset strategy as critical to the success of generative AI. Furthermore, 48% believed that a primary objective for a dedicated AI leader would be to ensure gen AI models are unbiased, consistent, and transparent. The elimination of silos between IT and data management teams was viewed as the most vital role of a CAIO by global respondents (38%).

Challenges in implementing a sound and responsible Gen AI strategy were also highlighted in the report. Organisations worldwide that currently have a CAIO are less prone to face planning for IT resources to train and implement gen AI models as an obstacle (32%, compared to 41% without a CAIO). Another significant challenge for global leaders is ensuring Gen AI models’ accuracy, lack of bias, and transparency (37%). Organisations without a CAIO are more likely to view this as a challenge (40%) than those with a CAIO (30%). Other barriers reported globally include creating and enforcing Gen AI policies (35%) and navigating Gen AI-related regulations and guidelines (35%).

Predicted to have an increasingly significant influence, Gen AI is simultaneously a profound opportunity and challenge for Australian businesses. The development of Gen AI strategies that align with organisational goals will be critical going forward, and the evolving role of the CAIO is likely to be integral in successfully navigating this terrain.