Australian data and AI executive
Brad Starr works with boards, CDOs, CIOs and technology leaders to turn governed data into AI systems people trust. He brings business strategy, data governance, enterprise AI architecture and control design together so organisations can scale AI safely, prove compliance continuously and keep leadership focused on core business outcomes.
About Brad
Brad works at the intersection of business strategy, data management and enterprise AI. His focus is practical. Define ownership. Establish shared context. Enforce quality. Build operating controls. Measure value.
Across more than two decades in enterprise technology, Brad has led technical and solution engineering teams across JAPAC. He has advised organisations on data governance, metadata, master data, AI readiness and the architecture required to scale intelligent systems.
Brad also works with boards and regulated organisations to move beyond manual, point-in-time control compliance. He translates agreed obligations into data, AI and operating controls that produce evidence as work happens. Financial services and Australian superannuation are one example of this approach, where the same trusted-data foundations support APRA alignment, operational resilience and better member outcomes.
His keynote work connects trust in data and AI with real outcomes in healthcare, emergency response, customer experience and enterprise decision-making.
View Brad’s professional profileRegulatory compliance and assurance
Many organisations still prove control effectiveness through spreadsheets, document repositories, manual attestations and audit-time evidence collection. The model is expensive, repetitive and difficult to keep current.
Brad works with boards, CDOs, CIOs, risk leaders and business owners to translate agreed obligations into data, AI and operating controls. Ownership, lineage, quality, access, policy decisions, exceptions and remediation become part of normal workflows rather than a separate compliance exercise.
The target is self-documenting compliance. Evidence stays connected to the obligation, accountable owner, policy, process, data, system, control result and approval. Leaders spend less time reconstructing proof and more time on customers, growth, productivity and transformation.
Manual control compliance
Self-documenting target state
How Brad engages
Compliance is one application of Brad's broader approach to trusted data and AI. The same operating model improves decision quality, accountability, resilience and the ability to scale AI across the organisation.
Connect strategy, risk appetite, business outcomes and accountability before selecting controls or technology.
Direction before toolingTranslate obligations and policies into owners, control objectives, data, systems, AI products and evidence requirements.
Clear accountabilityUse governance, quality, master and reference data, metadata and lineage to create a reliable evidence base.
Evidence with contextInstrument quality, access, security, workflow, AI lifecycle and service controls as part of normal operations.
Continuous telemetryRetain results, exceptions, decisions, approvals and remediation so assurance is assembled from live operational records.
Proof by designReduce repeated evidence collection so CDOs, CIOs and their teams spend more time delivering business value.
Less compliance overheadOne industry example
Brad applies this same approach in highly regulated financial services environments. For Australian superannuation, the data and AI lens includes operational risk, information security, data risk, member outcomes, accountability and AI governance under APRA and related frameworks.
Self-documenting compliance
The aim is not more documentation. The aim is an operating model where evidence is produced continuously by governed data, workflows and platforms.
Map regulation, policy and internal standards to control objectives and evidence requirements.
Link accountable people, control owners, data owners, systems, services, vendors and AI products.
Capture definitions, provenance, transformations, dependencies and downstream impact.
Measure quality, access, security, policy and service controls as part of normal operations.
Record breaches, exceptions, decisions, actions, approvals and closure in one traceable workflow.
Produce current dashboards, attestations and assurance packs from the same operational records.
Point of view
AI programs succeed when governance, context and delivery discipline work together.
AI needs shared definitions, lineage, business meaning and trusted reference points. More data does not solve weak context.
Controls belong in workflows, platforms and decision rights. Policy documents alone do not change behaviour.
Boards need traceable proof of ownership, quality, access, model use and control performance.
Pilots hide operating complexity. Enterprise architecture must account for security, cost, integration and change.
Automation changes work. It does not remove responsibility for outcomes, exceptions or customer impact.
The trusted enterprise stack
Speaking and executive sessions
A personal and practical keynote on how data and AI shape brands, businesses and lives. The session links trusted foundations with real outcomes in healthcare, emergency response and enterprise decision-making.
Why agents, analytics and operational systems need shared meaning, not isolated data pipelines.
A practical model for decision rights, evidence, risk controls and measurable value.
The architecture, governance and delivery choices required to scale AI safely.
How governance, metadata, lineage, data quality, control telemetry and workflow turn regulatory evidence into an output of daily operations across regulated organisations.
A working session covering ownership, metadata, master data, quality and roadmap priorities.
Selected insights
How business meaning, lineage and context improve AI decisions.
Read article ↗ Agentic AIWhy operating discipline matters before autonomous workflows scale.
View on LinkedIn ↗ Data governanceWhy data governance and management remain core to AI value.
View on LinkedIn ↗“The question is no longer whether AI will shape your organisation. The question is whether your data, controls and accountability are ready.”
Brad Starr
Speaking and advisory enquiries
Keynotes, panels, board briefings and executive workshops across Australia and JAPAC, covering trusted AI, data governance, enterprise AI architecture and self-documenting compliance. Financial services and superannuation are one example of Brad applying the same approach to a complex regulated environment.
Contact Brad on LinkedIn