Enterprise AI’s Foundation Is Only as Strong as People’s Trust in Data

The way we work and live as practitioners is shifting before our eyes. Tasks that once required hours of manual data collection and validation are being transformed into opportunities for deeper, more strategic analysis. And we’re getting home from work at a reasonable hour for a change.

Why? AI, of course. What once seemed like an impossible technology, born from sci-fi movies, is now embedded in the daily workflows of people across the globe.

From a leadership and culture standpoint, AI's true power lies in its ability to foster a culture of experimentation. By providing the right tools, companies can unlock new levels of creativity and problem-solving across their workforce.

Additionally, I often joke that AI has made it possible to step away for lunch for the first time in years, a small but telling shift that illustrates its potential for individual impact and a shift shared by many across the finance sector. To wit, one member of Cognizant’s reporting team has automated 90% of their routine work, freeing up time to focus on more meaningful contributions.

Yet while there’s no denying the benefits of AI anymore, a gap is emerging — and widening — between organizational ambition and actual readiness. According to Workiva’s latest survey, 84% of Australian professionals report that AI is delivering a positive ROI. Still, only 62% are using it in their daily work, well below the global average of 74%.

This discrepancy is a symptom of a deeper issue: namely, many organizations lack the necessary foundations to support effective use of AI. That same survey revealed more than 60% of professionals say their organizations lack high-quality data, clear governance policies, and role-specific tools and training.

Similarly, nearly half (47%) of Australian practitioners believe a lack of readiness among leaders is holding them back from leveraging AI effectively.

This isn’t just a technology problem. It’s a business problem that impacts employee productivity and a company’s competitive agility and ability to innovate. AI maturity requires leadership, technology, and people to be aligned around a common goal. If AI is redefining the way we work, then we must also redefine our roles, team dynamics, and collaboration across functions.

Human oversight vital to trusting data

The biggest barrier to AI’s full impact isn’t the technology itself. It’s the absence of strong foundational infrastructure. AI depends on clean, connected, and trustworthy data. Yet most organizations — including 64% of those in Australia — are still struggling to fully trust the data they have.

In domains such as sustainability reporting, data often originates from disconnected systems or manual inputs, and in some cases, it has yet to be formally audited. This introduces risk and undermines the integrity of AI-driven outputs.

AI tools designed specifically for finance, risk, audit and sustainability roles — rather than generic models — are emerging as a key differentiator. These domain-focused solutions are better equipped to navigate compliance-driven environments, while still understanding industry context.

However, without clear governance frameworks anchored in board-level oversight and integrated into enterprise strategy, scaling AI introduces significant operational and ethical risks. Deploying AI tools without guardrails is like driving a high-performance vehicle without brakes. At first glance, the speed is exciting. But it’s not a matter of if things go wrong, but when.

This is where the 80/20 principle of AI is vital.

AI can handle 80% of the heavy lifting for many of the grunt work tasks in finance — such as data processing, summarization, and task automation — but humans must do the remaining 20% to ensure accuracy, governance, and oversight. That final human touch is essential to reduce risk and uphold accountability.

Ensuring human oversight is paramount — although this will go a long way toward ensuring accountability and accuracy, it doesn’t alone close the trust gap.

And that trust must trickle down from leadership.

Lead with vision and outcomes

Implementing AI is not just an exercise in change management, but also an opportunity to lead by example.

Clarity must come from the top, particularly from the chief information officer, where aligning technology with business outcomes must take precedence over chasing trends. At Workiva, every AI initiative starts with one question: “What business outcome are we trying to achieve?”

AI implementations will vary across organizations, but some universal principles apply. Start by investing in the quality, accuracy, and connectivity of your data. Whenever possible, lean on existing platforms and systems that already house your core data to reduce friction and drive faster results.

Establish governance policies that address the ethical use, compliance, and security of your organization. These policies should be developed collaboratively with cross-functional stakeholders and tested in pilots with well-defined success metrics.

Provide employees with the training and support needed to use AI confidently and effectively. When teams understand not just how to use AI tools, but when and why to apply them, their impact increases significantly. In Australia, 65% of professionals report their organizations lack role-specific AI training — a gap that must be closed.

The same Workiva survey found that when companies invest in quality data, robust governance, and role-specific training, employee confidence in AI rises significantly. That confidence, in turn, fuels a more strategic and empowered workforce that is equipped to move beyond repetitive tasks and toward deeper analysis and innovative problem-solving.

Collaboration key to closing the trust gap

Closing the AI gap requires collaboration between IT, business teams, and executive leadership. No one function can drive this transformation alone. Cross-functional alignment is the engine that moves AI’s impact from potential to performance.

Confidence in AI is vital, but real value comes from disciplined execution. Maturity is not measured by how fast you adopt AI across the business, but by how well you prepare the foundation that supports it. When that foundation is solid, companies will see smoother operations, stronger engagement, and smarter decisions.

The most important question for any leader is this: Are you waiting for AI to deliver value, or are you actively building the data, governance, and workforce scaffolding that it needs to thrive? The future of work in Australia depends on it.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/renaschild