If 2023 was the year of AI discovery and 2024 was that of AI experimentation, then 2025 will be the year that organisations seek to maximise AI-driven efficiencies and leverage AI for competitive advantage.
Despite the ambitions many leaders harbour, they face a series of challenges that must be overcome to realise the true value of AI investments. Primary among these is the need to ensure the data that will power their AI strategies is fit for purpose. In fact, a data framework is critical first step for AI success.
The struggle many organisations face is reflected in the relatively slow uptake of meaningful AI projects in Australia, which sometimes is at odds with the wants of their workforces.
Many workers are not waiting for guidance and permission when it comes to adopting AI tools, leading to the emergence of shadow AI. In late 2023, a report from ISACA suggested that up to two-thirds of workers are using unsanctioned AI tools, despite only 11% organisations having a formal policy permitting its use.
According to Richard Kulkarni, Country Manager for Quest, a lack of clarity concerning governance and policy around AI means that employees and teams are finding workarounds to access the technology. "Some senior technology leaders fear a Pandora's Box type situation with AI becoming impossible to control once unleashed. Yet research shows Australians are already using AI without formal policies. Lack of oversight establishes a different kind of risk, with shadow IT posing significant security threats to organisations."
Compared to other parts of the world, the uptake of AI within Australian businesses is lagging. Research from HubSpot found only 17% of Australian firms had integrated AI or AI-enhanced tools within their operations.
Another impediment to AI adoption is the ongoing need to ensure that appropriate governance and protections are in place. This is to ensure any potential negative consequence is avoided.
This challenge has been recognised by the Australian Federal Government, with Industry and Science Minister Ed Husic announcing in September the creation of a set of voluntary AI guidelines, with consultation on whether these should be mandated in high-risk areas.
Getting these foundational aspects of AI governance in place will be critical to successful adoption, and for unlocking an opportunity that the Tech Council of Australia estimates could contribute $45 billion to $115 billion per year to the Australian economy by 2030.
The question now for every Australian business leader is how to adopt AI in ways that are both fast and safe, such that they can get on with using it to accelerate decision-making and automate core and non-core processes to better serve their customers.
There is, however, another barrier standing in the way of their ambitions: data readiness.
"Strong data strategies de-risk AI adoption, removing barriers to performance. If you're not keeping up the fundamentals of data and data management, your ability to adopt AI -- at whatever stage you are at in your AI journey -- will be impacted," Kulkarni points out.
Despite decades of investment in data management solutions, many continue to struggle with data quality issues, either through their failure to modernise legacy investments or through the outcomes of acquisitions and business decisions, which in either instance have led to data existing in multiple silos across their organisations.
This need to improve data governance is therefore at the forefront of many AI strategies, as highlighted by the findings of The State of Data Intelligence report published in October 2024 by Quest, which found the top drivers of data governance were improving data quality (42%), security (40%), and analytics (40%).
The 2024 report also found that ensuring data readiness and quality for AI was the fourth most cited driver of data governance programs, as reported by 34% of respondents, with the focus on AI governance efforts and AI data readiness needs permeating the report's findings.
With many AI strategies highly reliant on having access to large volumes of high-quality data, the need to resolve the issues cited above is driving interest in new solutions to data governance.
"AI thrives on clean, contextualised, and accessible data. Without it, businesses risk perpetuating the very inefficiencies they aim to eliminate," adds Kulkarni.
One field that is gaining attention is data intelligence, which uses metadata to provide visibility and a deeper and broader understanding of data quality, context, usage, and impact. This is essential for enabling organisations to discover, trust, manage, and leverage high-value data for better decisions and outcomes and to better protect against risk.
The report further saw activity around metadata harvesting, classification, and curation experiencing a 94% surge in response between 2023 and 2024 as organisations prepared for future AI initiatives.
Actions such as those described above can play a key role in not only improving data quality and the breaking down of silos but also in democratising access to data by making it more useful and accessible to AI-driven use cases across the organisation.
This in turn stimulates a more agile and adaptable approach to AI which can accelerate its uptake and the returns that the organisation can expect.
For any organisation keen to take a leadership position on AI adoption -- or that simply wants to avoid rapidly falling behind those that do -- making the necessary investments in data governance may provide a many-fold return as the benefits of AI itself scale over time.
According to Kulkarni: "Businesses that resolve their data complexities now will gain the agility to respond faster to market changes, ensuring competitive advantage in 2025 and beyond."