As organisations continue to increasingly embrace AI to unlock new operations, industry analysts – at the Gartner Data & Analytics Summit in Sydney – urged a critical reminder that without trustworthy data, even the most advanced AI systems can lead businesses astray.
Amid rising interest in generative AI and autonomous agents, business leaders are being reminded that flashy AI capabilities are meaningless if built on unreliable data. According to information technology research and advisory company Gartner’s 2024 survey, data quality and availability remain the biggest barriers to effective AI implementation. If the foundation is flawed, so is the intelligence built on top of it.
While achieving perfect data governance is an admirable goal, it’s often impractical in fast-moving business environments. Instead, analysts recommend implementing “trust models” that assess the reliability of data based on its origin, lineage, and level of curation.
These models enable more nuanced, risk-aware decision-making and can prevent the misuse of data without stalling innovation.
Richard Bovey, Chief for Data at AND Digital, comments, “Trust in data isn’t just a technical challenge, it’s deeply cultural and organisational. While advanced tools and trust models can help address the reliability of data, true confidence in data quality comes from clear ownership, clear practices, and company-wide commitment to transparency.
“Too often, organisations are rushing into AI initiatives without fixing the basics. According to our research, 56% of businesses are implementing AI despite knowing their data may not be accurate in order to prevent from falling behind their competitors.
“Businesses must consider taking a data and AI approach to their technical operations to build trust, cross-functional collaboration, and ongoing education. Only then can AI initiatives truly succeed.”
At the summit, autonomy was a central theme. AI systems may act independently in low-risk or time-sensitive situations, but full autonomy still raises concerns as, while users accept AI advice, they’re still adjusting to autonomous AI decision-making.
Stuart Harvey, CEO of Datactics, argues, “One of the biggest misconceptions we see is the belief that AI performance is purely a function of the model itself, when in reality, it all starts with data. Without well-governed, high-quality data, even the most sophisticated AI systems will produce inconsistent or misleading results.
“Organisations often underestimate the foundational role of data management, but these aren’t back-office tasks, they’re strategic enablers of trustworthy AI and those businesses that rush into AI without addressing fragmented or unverified data sources put themselves at significant risk. Strong data foundations aren’t just nice to have in today’s technical landscape, they’re essential for reliable, ethical, and scalable AI adoption.”
Gartner predicts that by 2027, 20% of business processes will be fully managed by autonomous analytics and these “perceptive” systems will move beyond dashboards, offering proactive, embedded insights.
The company also believes that by 2030, AI agents will replace 30% of SaaS interfaces, turning apps into intelligent data platforms. To thrive, data leaders should thus prioritise trust, influence, and organisational impact, or risk being sidelined.
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