LEAD WITH DATA Podcast

When most people think about data and AI transformation, they don't think of tree crews and vegetation management. Active Tree Services runs field crews, GPS tracking, drone LiDAR and its own in-house software. Denis left the world of big corporates to lead its data and AI capability. He shares what he has learned about turning data into decisions, bringing a blue collar workforce on the AI journey and leading a team that is not afraid to get things wrong.Key pointsWhy Denis moved from large listed companies to a business that could "move as fast as AI"The big bang single source of truth trap, and how to build the platform progressively while delivering valueLetting the business choose the first use case, while shaping the options so a quick win is possibleA three-stream AI rollout: AI for all, AI for specific roles, then agents for specific tasksMeasuring AI success first by adoption and now by ROIReplacing vendor software with internally built tools and what you should never build yourself (ERP, payroll, finance)Why "people with AI will replace people without AI", and why redundancy-led AI projects backfireHiring for curiosity, business acumen and relationships over pure technical skillSponsor Thank you to Experian Asia Pacific for supporting Lead with Data. Through solutions like Aperture Data Studio, Experian helps organisations govern, improve and trust their data. Learn more at experian.com.au.

Nathan walked into Slater and Gordon as one of the firm's first data science hires, in an industry not known for embracing new capability quickly. In this episode he explains how he built trust and momentum from scratch, how his team's build versus buy framework has evolved and why governance now sits at the centre of AI strategy rather than at the edge of it.Key points:Nathan's non-linear path from computer engineering to drug discovery research to AI leadership at Slater and GordonCold emailing across the org chart and coffee chats in the first two months to map a hundred potential problemsWhy Slater and Gordon's existing data maturity created a different starting point to most businessesHow the build versus buy framework has shifted now that AI coding tools make proof of concepts faster to produceThe three stage gates and checklist used to make build versus buy calls on larger systemsWhy governance and innovation now sit under the same remit and how an internal ethics committee with an external ethicist supports thatThe eighteen AI solutions delivered in the past year and the launch of a direct to consumer legal assistantWhat Nathan looks for when hiring for data and AI roles, with growth mindset ranked above specific technical skills

Guest: Noel Toal, Chief Technology and Transformation Officer of Repurpose ItNoel joins Rina to unpack how he rebuilt trust in data at Repurpose It, an industrial resource recovery business where data and analytics can easily sit low on the agenda. He shares how he pulled over 70 dashboards down to just five, the mindset shift that changed how his board engaged with technology, how he won over his toughest sceptic and how the business is beginning to explore what AI can do for its operations.Key points:Industrial businesses often deprioritise data, even when the opportunity is significantOver 70 dashboards were consolidated down to five, rebuilding trust in the processPresenting strategy in business terms rather than technical terms changes how a board engagesThe most resistant executive in the room can become your strongest advocateEarly signs of AI adoption are starting to shape how the business operatesTakeaway: In an industrial setting where data rarely gets top billing, the businesses that get board buy-in are the ones that translate technology into outcomes the board already cares about.

I sit down with Natalie Hogan who shares how her organisation moved governance off the page and into daily practice by using AI to rebuild it. Static policies became a living, chunked knowledge base that both people and AI agents could read, query and act on, closing the long-standing gap between governance written down and governance actually followed.Key points:Traditional governance was static, siloed and hard to embed; AI governance needed its own rule book from scratchPolicy as code: breaking frameworks into knowledge chunks that both humans and AI can read and updateEnterprise wide model governance, an agent governance framework and an AI risk management framework were introduced to cover the gapAgents are now treated as part of a "silicon workforce," each with an owner, access level and place in a registerEarly approach built siloed agents for each governance function before the team realised they needed one connected knowledge base insteadAdvice for teams starting out: take a risk based approach, don't wait for every foundation to be perfect, and keep humans in the loop throughoutTakeaway: Governance only works once it's embedded in the flow of daily work rather than sitting in a document. This team's shift was to stop treating it as a series of automations and start treating it as one connected system that AI could help run. Tune to learn how!

Healthcare has some of the highest ambition and the tightest funding for AI of any sector. In this episode Lachlan MacBean walks through how his team built a ten person AI capability without a dedicated AI budget, the partnering model that made it possible, and why they made the call to shut down an early project despite strong results.Key discussion pointsJoining three months before COVID and the operational chaos that followed, and how the crisis proved the value of the analytics functionThe partnering strategy: using clinicians' grant funding and dual academic clinical roles to fund AI work that wouldn't otherwise get a business caseStructuring the team on a part revenue target model, operating internally like a consulting practice within the hospitalA handful of models now running in production, built on a mix of structured data and clinical notesWhy an early AI project got strong results but was shut down: a solution looking for a problem, and a pricing model that made it commercially unsustainable at the timeThe "shark tank" process now used to pressure test new ideas before committing delivery resourcesKeeping teams engaged through all hands meetings, a cross organisation community of practice, and tying work back to patient impact

Moneyball for Golf: How a Lean Team Can Punch Above Its WeightLead with Data, with Jarred, Head of Data and Intelligence at Golf AustraliaMost data leaders inherit a mandate from the board, a budget and teams they control. Jarred has none of that in the traditional sense. He runs the data function for a national sporting body, which means the data he works with is created by clubs and third parties he doesn't control, the team is lean and the budget is nowhere near enterprise scale. He still built one of the most strategically mature data capabilities in Australian sport.This one is for you whether you lead a data function on tight constraints, or you just want to know how data is quietly reshaping the game.What we get into:Why owning your data end to end matters more than the shiny front end, and why the most important work happened under the hoodMigrating 25 years of legacy data with almost no documentation and what that actually takesHow Golf Australia moved from subjective, intuition led athlete decisions to models built on a decade of historical outcomesRunning a serious data capability with a lean teamThe half a million handicaps behind one of the most personal datasets in the countryIf you have ever argued about your handicap, that is Jarred's data.Find Jarred on LinkedIn, where he is happy to chat about any of this.

Data governance has stopped being about rules and started being about survival. In this episode, Belinda walks through how her team shifted from gatekeeper to guide pairing technical and business minds, educating instead of enforcing, and learning to say no to perfection. We talk about what actually works when you're moving at speed, how AI forced organisations to finally care about data quality and the honest part nobody mentions: what it feels like to be seen as the insurance policy nobody wants to buy.

In this episode, I sit down with Tracy Moore, GM of AI and Data at MYOB, for one of the most refreshingly honest conversations I've had about what it takes to build AI products that reach real customers. Tracy and her team have been quietly doing the hard work of shipping AI to millions of small businesses across Australia and New Zealand and the lessons they've learned along the way are ones every data and AI leader needs to hear.We talk about why most AI initiatives stall before they ever get shipped, the difference between exploring and actually building and why having a commercial mindset is the thing that changes everything. Tracy also shares how why treating data and AI as a team sport has been central to their success. If you've ever felt like your organisation is stuck between experimentation and real impact this one is for you.🎧 Listen to the full episode on Spotify or Apple Pod (Links in the comment below)

HR doesn’t lose influence because it lacks insight.It loses influence because it talks in the wrong language.In this episode of Lead With Data, we speak to a senior business executive who didn’t come from a data background like our usual guests but fundamentally changed her seat at the table by doing one thing differently: connecting workforce data directly to revenue, margin and productivity.No dashboards for show. Just data translated into decisions executives actually care about.This conversation shows what happens when people metrics stop being “HR numbers” and start being business performance indicators.If you work in HR, people and culture or any function that wants to influence outcomes, not just report activity, this one is worth your time.Key takeawaysPeople data is business performance data, not HR reportingVacancies aren’t an HR problem, they’re delayed revenueIf you can’t link initiatives to margin or productivity, they won’t get tractionInfluence comes from translation, not more dashboards

Why Data Teams Need Translators, Not Just ToolsIn this episode of Lead With Data, Rina Gami sits down with Clare Kitching, a senior data leader who works closely with organisations navigating complex data and AI decisions. The conversation explores why many data and AI initiatives stall and not because of tooling gaps.The conversation unpacks how data teams are navigating generative AI, agentic AI and increasing pressure to move quickly, often without the shared language, context and alignment required to succeed. Clare shares what she is seeing first-hand across organisations grappling with executive expectations, shadow AI, governance concerns and the widening distance between technical teams and business leaders.This episode focuses on the growing importance of translators in data teams' people who can bridge strategy, technology and value and what leaders should be paying attention to before scaling AI.In this episode, we cover:Why tools alone do not solve AI and data challengesThe shift from traditional data initiatives to generative AI and agentic-based AIThe critical role of senior executive sponsorship in AI successHow translators help close the gap between technical teams and the businessPractical ways organisations can start or scale AI responsiblyEvolving roles in data teams, including analytics translators and AI governance specialistsCommon challenges such as shadow AI, data quality and organisational buy-inIndicators that signal true readiness to scale AIWhy value-led AI initiatives matter more than experimentationTimestamps00:00 – AI as a high-return capability in modern data teams01:32 – Claire’s career journey and move into consulting03:16 – What has changed in data and AI over the last five years04:00 – Where organisations are really at in their AI journeys05:24 – Pressure to move quickly and what gets missed06:03 – Executive sponsorship and its impact on outcomes07:30 – Data team structures and emerging gaps08:53 – Organisational barriers to AI adoption10:38 – Shadow AI and responsible use12:34 – Initial assessment points when working with organisations14:47 – Asking the right questions to align AI to value17:11 – Why communication is now a core data capability19:09 – Telling the data story in a way the business understands22:18 – Structuring data teams for sustainable AI outcomes25:26 – Evolving roles across data, engineering and governance28:11 – Everyday AI, generative AI and agent AI explained32:21 – Leadership priorities when advancing AI initiatives33:49 – Signs an organisation is ready to scale AI responsibly35:11 – Maintaining momentum without creating risk36:08 – Hiring for AI capability beyond technical skills38:25 – When and why to involve external expertise41:17 – The value of an external perspective44:48 – Avoiding stagnation in AI programs45:27 – Communication and storytelling as critical capabilities46:16 – Leadership alignment and organisational readiness46:44 – Final reflections and next stepsResources & LinksExperian Data Studio – supporting stronger data governance and trustClaire Kitching Linkedin Website - cambiq.com.au