Tech Diary: AI Roundtable Event - August 2025

Last Thursday, the Tank Stream Labs in Melbourne was buzzing with a group of finance leaders and innovators for a deep-dive conversation on a topic that’s on everyone’s minds: the adoption of AI in finance an beyond. The event, an exclusive roundtable, wasn't about the futuristic promises of AI, but rather a frank, "compare notes" session on what's working right now, and where the challenges still lie.

Here's a summary of the key takeaways from our discussions:

From "Why AI?" to "How is AI helping us?

The conversation quickly moved past the theoretical benefits of AI and into the practical applications. We heard how teams are leveraging AI for everything from automating routine data entry and reconciliation to more sophisticated tasks like fraud detection and cash flow forecasting. The consensus was clear: AI is proving its worth by tackling the "heavy lifting" of transactional and analytical work, freeing up finance professionals to focus on strategic insights and business partnering.

Full Process Automation: The ERP Puzzle

A major point of discussion was the journey toward full process automation, particularly within different ERP environments. It's not as simple as flipping a switch. The group shared experiences on how they are navigating legacy systems, data silos, and the complexities of integrating new AI tools. A key insight was the need for a phased approach, starting with high-impact, low-risk processes to build momentum and prove ROI before tackling more complex, end-to-end automation.

The Evolving Finance Team: More Than Just Numbers

The advent of AI is reshaping the finance function itself. We explored how team structures are evolving, with some organizations adopting Embedded Shared Development (ESD) models to bring tech and finance expertise closer together. The discussion around skills was particularly lively. It's no longer just about accounting and financial analysis. The new, in-demand skills include data literacy, a basic understanding of machine learning principles, and, most importantly, the ability to act as a bridge between the business and technology teams.

Building Internal Capability: From Training to Talent

One of the most pressing questions was how to build internal capability to support this shift. We discussed strategies ranging from upskilling existing teams through targeted training programs to a more aggressive approach of hiring new talent with data science and technical backgrounds. The general consensus was that a hybrid model is most effective, combining a commitment to continuous learning with strategic new hires to inject fresh expertise.

Practical AI Strategy and Measuring ROI

How do you evaluate AI tools and, more importantly, prove their value? The group shared practical tips on developing a practical AI strategy. It's not just about cost savings; ROI can also be measured in terms of reduced risk, improved accuracy, and the ability to generate deeper insights. A key takeaway was the importance of starting with a clear business problem, rather than a technology looking for a home.

The Road Ahead: Risks and What's Next

We wrapped up by discussing the risks and pitfalls to avoid, based on early experiences. Data access and security were highlighted as top concerns, along with the risk of "black box" algorithms that lack transparency. The group agreed that a measured, ethical approach is crucial.

The event was a powerful reminder that while the hype around AI is real, the true value lies in its practical application. It’s not about replacing finance professionals, but about empowering them to do more, better, and faster.

A big thank you to everyone who joined us for this incredibly insightful discussion. We look forward to continuing the conversation as the landscape of AI continues to evolve.