Season 6 - Episode 4 “Build, Ship, Improve” - The Commercial Mindset Behind AI That Reaches Real Customers" with Tracy Moore

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 points Why 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 value Letting the business choose the first use case, while shaping the options so a quick win is possible A three-stream AI rollout: AI for all, AI for specific roles, then agents for specific tasks Measuring AI success first by adoption and now by ROI Replacing 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 backfire Hiring for curiosity, business acumen and relationships over pure technical skill

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 Gordon Cold emailing across the org chart and coffee chats in the first two months to map a hundred potential problems Why Slater and Gordon's existing data maturity created a different starting point to most businesses How the build versus buy framework has shifted now that AI coding tools make proof of concepts faster to produce The three stage gates and checklist used to make build versus buy calls on larger systems Why governance and innovation now sit under the same remit and how an internal ethics committee with an external ethicist supports that The eighteen AI solutions delivered in the past year and the launch of a direct to consumer legal assistant What 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 It Noel 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 significant Over 70 dashboards were consolidated down to five, rebuilding trust in the process Presenting strategy in business terms rather than technical terms changes how a board engages The most resistant executive in the room can become your strongest advocate Early signs of AI adoption are starting to shape how the business operates

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 scratch Policy as code: breaking frameworks into knowledge chunks that both humans and AI can read and update Enterprise wide model governance, an agent governance framework and an AI risk management framework were introduced to cover the gap Agents are now treated as part of a "silicon workforce," each with an owner, access level and place in a register Early approach built siloed agents for each governance function before the team realised they needed one connected knowledge base instead Advice for teams starting out: take a risk based approach, don't wait for every foundation to be perfect, and keep humans in the loop throughout

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 points Joining three months before COVID and the operational chaos that followed, and how the crisis proved the value of the analytics function The partnering strategy: using clinicians' grant funding and dual academic clinical roles to fund AI work that wouldn't otherwise get a business case Structuring the team on a part revenue target model, operating internally like a consulting practice within the hospital A handful of models now running in production, built on a mix of structured data and clinical notes Why 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 time The "shark tank" process now used to pressure test new ideas before committing delivery resources Keeping teams engaged through all hands meetings, a cross organisation community of practice, and tying work back to patient impact