Enterprise AI breaks down when pilots, governance, and workforce capability are treated as separate problems.
The real challenge is building an operating model that can bring them together and turn scattered progress into repeatable execution.
At the Data & AI Edge 5th Data & AI Edge, Mike Lau, Chief Data and Analytics Officer at the Australian Digital Health Agency, Samrat Seal, Head of Transformation and Governance, AI and Cyber at Kmart Group, and Satya Tammareddy, Head of GTM, ANZ at OpenAI, came together to examine what that shift demands from governance, capability, and organisational design.
Key takeaways:
- AI value comes from operating‑model change, not tools alone. People, process and governance must evolve together.
- Foundations enable scale. Strong data governance and shared platforms prevent fragmentation and unlock industrialisation.
- ROI is multidimensional. Adoption, impact and trust are as critical as financial returns when measuring AI success.
Data‑driven operating models are about people, process and feedback loops
The panellists agreed that a data‑driven operating model is not about autonomous decision‑making alone, but about how organisations consistently generate value through feedback loops connecting data, technology, people and processes.
As Mike emphasises, AI enables far more meaningful use of data, but technology alone never delivers value without human judgement to interpret context, manage bias and apply insights responsibly.
This view was echoed by Samrat, who highlighted the need for strong foundations in data governance before AI can scale, and by Satya, who reinforced that AI literacy and practical capability across the workforce are essential to turning advanced tools into real‑world outcomes, particularly where personalisation and societal impact are involved.
Industrialising AI requires foundations, not fragmented innovation
Retail and public sector experiences highlight the risks of fragmented, project‑based AI efforts: some use cases succeed, but scalability, portability and trust quickly break down.
Industrialising AI means getting the basics right first (data quality, lineage, ownership and governance), before scaling.
Frameworks that align AI investment to core business processes, supported by unified platforms and shared standards, allow organisations to move from months‑long experiments to repeatable capability delivered in weeks, not quarters.
Redefining ROI: adoption, impact and trust
The panel challenges narrow financial definitions of ROI.
Value from AI shows up through adoption (workforce capability and mindset shift), impact (productivity, speed to market, new capabilities), and trust (security, explainability, bias and reliability).
From an executive perspective, ROI is strongest when AI aligns to organisational mission, improving outcomes that matter to customers and citizens while enabling work that was previously impossible.
AI literacy, leadership endorsement and portfolio‑level thinking are what turn experimentation into sustained value.