Applied AI Australia

Ramon Rodriguez
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Applied AI Australia is the podcast for Australian executives turning AI into measurable business outcomes. Hosted by Ramon Rodriguez - an executive, not a career consultant. This show cuts through AI hype to focus on what matters: Growth and Margins. Now part of Acquire Intelligence, the podcast brings commercially grounded AI strategy and practical executive insight to CEOs, CIOs,CFOs, boards, and senior operators navigating rapid change and competitive pressure. Each week, Applied AI Australia delivers real-world conversations, operator perspectives, and practical frameworks leaders can apply quickly , often within 48 hours. This is not a tech podcast. It is practical AI leadership for executives accountable for results. Subscribe.

  1. Sep 20

    Where Do You Start With AI at Scale? | Managing Director, Coca-Cola, Orlando Rodriguez

    Where Do You Start With AI at Scale? | Managing Director, Coca-Cola Europacific Partners Aus, Orlando Rodriguez. MIT's 2025 research found that 95% of organizations get no measurable P&L impact from generative AI investment. How does a multi-billion dollar, asset-heavy company actually make AI work at scale? Success requires discipline, business-led prioritization, and applying AI to genuine business problems. In this episode of Applied AI Australia, powered by Acquire Intelligence, Ramon Rodriguez speaks with Orlando Rodriguez, Managing Director of Coca-Cola Europacific Partners Australia, about how one of Australia's largest brands approaches AI at scale. CCEP operates across Australia, serving 100,000 customers with 500 products, creating 50 million permutations at any time. Its AI work spans live manufacturing diagnostics, commercial planning, and agricultural crop genetics. Key results discussed: - Live diagnostics on manufacturing lines replacing hours of manual analysis with real-time recommendations for technicians - Commercial planning reviews automated, saving 6-7 hours a fortnight - AI-enhanced sugarcane crops with Avalo improving yield, drought resistance, and fertilizer efficiency - Driverless forklifts and a robot dog for quality checks deployed across manufacturing sites - 50 million customer-product permutations managed with AI precision Key takeaways: - Approaching AI as a core business tool - Fixing the root causes behind failing AI investments - Why business leaders must own and drive AI strategy - Prioritizing use cases using the "headroom to grow" framework - Applying the "point of departure, point of arrival" transformation framework - Why data quality is the essential foundation you can't skip - How hiring shifts toward EQ, learning agility, and curiosity as machines augment IQ 5 Key Leadership Questions: - Where is the headroom to grow in our business? - What genuine business problem or opportunity does this AI solve? - Do we have the data quality and governance to support this? - Does this initiative deliver measurable value? - If we try to do everything at once, what will we actually do well? Your 48-hour action: Choose one AI initiative in your organization and ask: Does this solve a genuine business problem with headroom to grow the top line or take out cost? If not, deprioritize it. Chapters 00:00 Cold open: Business leadership in AI 01:32 Orlando's background and Coca-Cola's scale 04:27 Three core tenets: team, customers, platforms 05:16 100,000 customers, 500 products, 50 million permutations 09:29 AI for live manufacturing line diagnostics 11:17 Partnering with Avalo on AI-enhanced sugarcane genetics 17:26 When Coca-Cola started focusing on generative AI 17:53 Robot dogs and driverless forklifts 20:37 Why AI must be led by business leaders 24:05 Have you seen tangible benefits? 25:05 The 700 million question: who's accountable? 29:01 Point of departure, point of arrival framework 31:26 Lessons learned: data is the basement 36:07 Zero FTE departments and the future of jobs 38:12 Obsessed with benchmarking to top quartile 38:38 What's Pepsi doing with AI? 39:02 Top tips for executives starting their AI journey 42:16 Final takeaways Applied AI Australia is powered by Acquire Intelligence. We help Australian companies turn AI investment into measurable business value, deciding what to fund, what work must change, where value should land, and who owns the result. Get in touch: https://www.appliedaiaustralia.com.au/book-briefing Ramon: linkedin.com/in/ramonrod Guest: Orlando Rodriguez, Managing Director, Coca-Cola Europacific Partners Australia

    Where Do You Start With AI at Scale? | Managing Director, Coca-Cola, Orlando Rodriguez

  2. Sep 11

    You Can't Slap AI Onto a Broken Process: | EasyPark - Jason Marks

    You can't afford to fall behind on AI. Subscribe to Australia's #1 executive AI podcast. ------------------------------------------------- You Can’t Slap AI Onto a Broken Process | Jason Marks, GM, EasyPark for Business (Arrive) AI doesn’t magically fix messy processes or poor data. Often, it exposes them. Jason Marks, GM at EasyPark for Business, joins Ramon Rodriguez to unpack what AI adoption looks like inside an operating business, covering product experimentation and the practical application of AI across sales and commercial workflows. Key topics covered: Why clean and consolidated data is essential before introducing AI agents Using hackathons and controlled experimentation to stress-test workflows Accelerating tender responses by reusing proven answers and identifying content gaps Deploying "Parker" (EasyPark's Salesforce SDR agent) within strict knowledge boundaries rather than open internet access Why human judgement remains non-negotiable at key operational gates 00:00 You can’t slap AI onto a broken process00:37 Meet Jason Marks and EasyPark01:08 What work looked like before AI01:46 Why EasyPark started pushing into AI02:19 The evolution from product AI to business AI02:42 Why adoption happened iteratively03:42 Where AI is working today04:43 Hackathons, trial and error, and trying to break the product07:31 What actually got faster08:44 Tender responses as a practical AI use case11:59 Where humans stay in the loop13:42 More people, more AI - not simply fewer jobs15:48 Governance and controlled experimentation18:10 Meet Parker, EasyPark’s SDR agent18:55 Why data readiness matters before agents22:28 Avoiding AI slop24:49 What messy data exposes28:16 Knowing when to switch AI off29:16 Jason’s personal AI toolkit30:36 The leadership takeaway Applied AI Australia is powered by Acquire Intelligence. We help Australian companies turn AI investment into measurable business value - deciding what to fund, what work needs to change, where value should land, and who owns the result. Book a briefing: https://www.appliedaiaustralia.com.au/book-briefing Ramon Rodriguez: https://www.linkedin.com/in/ramonrod/ Jason Marks: https://www.linkedin.com/in/jason-marks-32bb28a1/

    You Can't Slap AI Onto a Broken Process: | EasyPark - Jason Marks

  3. Aug 26

    Build vs Buy: What Should Your Business Own in AI? | Chief Digital Officer, Fisher & Paykel, Rudi Khoury

    Build vs Buy: What Should Your Business Own in AI?What should your business build with AI versus buy? What is too strategically important to hand over? The answer depends on where you create value, what data makes you different, and which parts of the AI stack are becoming commodity infrastructure. In this episode of Applied AI Australia, powered by Acquire Intelligence, Ramon Rodriguez speaks with Rudi Khoury, Chief Digital Officer at Fisher & Paykel, about making that decision inside a real enterprise. Fisher & Paykel has more than 90 years of organisational knowledge. Its AI work includes making roughly 35,000 documents and 100GB of information usable, applying AI across hundreds of thousands of customer enquiries, and deciding which capabilities to own versus where external technology and partners make sense. The results discussed include: AI resolving around 60-80% of enquiries across targeted use casesA double-digit reduction in contact-centre volumeAI-agent volume increasing roughly fourfoldDecades of product and organisational knowledge made accessible to AICustomer experience measured through satisfaction and effort, not just traditional NPSHow to decide what AI capability to build, buy or access through a partnerWhy build versus buy is not one enterprise-wide decisionIdentifying the layers of AI where ownership mattersWhy proprietary data, product knowledge and customer experience change the equationWhat Fisher & Paykel learned from turning 90+ years of knowledge into usable AI contextHow AI is used across hundreds of thousands of customer enquiriesWhy resolution rate, customer satisfaction and effort matter more than measuring AI adoptionWhy narrowing the scope of an AI agent can improve customer experienceHow privacy and customer data affect build-versus-buy decisionsWhy businesses should avoid rebuilding technology layers that are rapidly becoming commoditiesHow hackathons build internal AI capability even when technology is bought externallyWhat part of this differentiates us?What proprietary data, knowledge or customer experience sits underneath it?Do we need to own that layer, or control how it is used?What could a specialist partner or platform do better than us?If the underlying model changes dramatically in 12 months, what have we built that still matters?The bigger leadership question is where your organisation creates unique value - and how much control you need over the AI that touches it. Your 48-hour action: Choose one significant AI initiative already being discussed inside your organisation and ask: Chapters00:00 Build vs buy: the decision every executive is about to face01:00 Where Fisher & Paykel is focusing its AI strategy04:38 More than 90 years of organisational knowledge05:06 Making roughly 100GB of information usable by AI06:35 AI handling hundreds of thousands of customer enquiries06:56 Resolution rates of around 60-80%07:14 Double-digit reduction in contact-centre volume07:18 AI-agent volume increasing approximately fourfold09:18 Measuring AI with customer satisfaction and effort10:35 Rudi’s framework for deciding build versus buy14:10 Why narrowing the scope of an AI agent can improve the experience14:51 Privacy, customer data and changing Australian obligations17:30 Where AI should sit inside the organisation20:48 What rapidly changing AI models mean for businesses building today24:52 Why hackathons have been one of Fisher & Paykel’s most powerful AI initiatives27:28 Final takeaways: know what you own and where your value sitsApplied AI Australia is powered by Acquire Intelligence. We help Australian companies turn AI investment into measurable business value, deciding what to fund, what work must change, where value should land and who owns the result. Get in touch - https://www.appliedaiaustralia.com.au/book-briefing Ramon: linkedin.com/in/ramonrod Guest: Rudi Khoury, Chief Digital Officer, Fisher & Paykel

    Build vs Buy: What Should Your Business Own in AI? | Chief Digital Officer, Fisher & Paykel, Rudi Khoury

  4. Jul 27

    Does AI Always Mean Fewer People? How CEOs Move Teams Into Higher-Value Work | Emma Fawcett, CEO Compare Club

    Does AI always mean fewer people? In some parts of a business, it can. The leadership challenge begins when AI removes, compresses or changes existing work. Leaders need to decide what stops, where the capacity goes, what higher-value work replaces it and what standard the team must now meet. In this episode of Applied AI Australia, powered by Acquire Intelligence, Ramon Rodriguez speaks with Emma Fawcett, CEO of Compare Club, about leading that shift across a real business. AI does not make people less important. It makes the right people, the right culture and the right work more important. Emma draws on her experience, including leading MYOB’s largest division, where one million customers generated four million calls a year and waited up to 40 minutes for support. Before approving an eight-figure technology program, Emma’s team stepped back to understand why customers were calling. They found that around 20% of calls came from customers who could not understand their bill. By fixing those root causes before investing in new technology, the team cut call volume by roughly half. The business later reached 86% digital service and lifted customer satisfaction by around 20 percentage points. At Compare Club, the same discipline underpins the Make Work Better program. The business has identified 135 potential AI use cases, with each required to define the problem, expected return and where the freed capacity will go. The results include: • Board reporting reduced from nine days to two• A monthly finance task reduced from 39 hours to around 25 minutes• A recruitment tool that narrowed 400 applications to 30 for human review Each example shows what happens when leaders treat saved time as capacity to be deliberately redirected, rather than the final result. What you’ll learn • Why AI programs stall when leaders begin with the tool• What the right team looks like in an AI-enabled business• Why emotional intelligence, curiosity and adaptability matter• How to raise standards without creating fear• Why saved time is not value until leaders decide where the capacity goes• How to communicate changing roles and higher-value work honestly Your 48-hour action Choose one AI initiative already being discussed and ask: What low-value work does this remove? Where will the freed capacity go? What higher-value work should people do instead? What capability must the team build next? How will we explain the shift in a way that builds trust rather than fear? If the leadership team cannot answer those questions, the initiative is not ready to scale. Chapters • 00:00 Outcomes before technology• 00:16 Welcome and guest introduction• 01:10 Why AI starts in the wrong place• 02:18 Four million calls and the eight-figure trap• 08:58 EQ over IQ in the AI era• 15:17 Who owns AI and Make Work Better• 17:35 Nine days to two, 39 hours to 25 minutes• 20:22 Cathy Bot and 135 AI use cases• 29:38 Reducing 400 CVs to 30• 35:50 Leading people through AI change• 38:51 Moving people into higher-value work• 46:40 From channel chaos to 86% digital service• 49:57 Stop the Work and the 70/20/10 model• 56:38 Put AI on the calendar• 1:00:03 Where to find Emma and Compare Club• 1:01:09 Final takeaways About Applied AI Australia Applied AI Australia is powered by Acquire Intelligence. We help Australian companies turn AI investment into measurable business value. We help you, decide what to fund, what work must change, where value should land and who owns the result. Get in touch - https://www.appliedaiaustralia.com.au/book-briefing Ramon: linkedin.com/in/ramonrodGuest: Emma Fawcett, CEO of Compare Club

    Does AI Always Mean Fewer People? How CEOs Move Teams Into Higher-Value Work | Emma Fawcett, CEO Compare Club

  5. Jul 20

    Fast Tech, Slow Org: The AI Execution Gap | Salesforce SVP & CMO ANZ Leandro Perez

    Fast Tech, Slow Org: The AI Execution Gap | Salesforce SVP & CMO ANZ Leandro Perez Subscribe if your leadership team is investing in AI but still cannot clearly explain what work has changed. AI progress is easy to perform. Pilots launch. Platforms get bought. Agents get tested. Boards receive updates. Value is harder. It shows up when workflows change, ownership is clear, metrics move, customers get a better outcome, and the organisation knows what the agent is allowed to do. That is the AI execution gap. In this episode of Applied AI Australia, powered by Acquire Intelligence, Ramon Rodriguez speaks with Leandro Perez, SVP & CMO ANZ at Salesforce, about what has to change before AI capability becomes business value. The conversation goes past tools and into the operating reality: agents need owners, metrics, supervision, escalation paths, business context, clean data and better work design. Leandro also raises one of the sharper leadership shifts: this may be the last generation of managers who only manage people. As agents enter workflows, leaders will need to manage both people and AI systems. The core message: The tool is not the transformation. The proof is whether the work changed. What you’ll learn: Why AI pilots, tools and training do not automatically create business valueWhat changes when AI moves from answering questions to acting inside workflowsWhy agents need owners, metrics, supervision and escalation pathsHow poor data and weak context limit AI performanceWhy customer service, sales and marketing metrics need to changeWhat leaders should ask before scaling AI agentsKey stats and examples: 76% of Australian service leaders are looking at or using AI56% of employees are using personal AI tools without disclosing itSalesforce says 84% of cases are now handled autonomously in one support use caseMore than 2.2 million cases have been deflected through Salesforce’s support agentSalesforce has more than 300 agents operating internallyOne prospecting agent had to be slowed down after following up outside normal business expectations 48-hour action: Pick one AI agent, pilot or workflow and ask: What work has changed?Who owns the outcome?What metric proves value?What authority does the agent have?Where does a human step in?What outcome has improved?What needs to change before this scales?If you cannot answer those questions, you may be scaling AI activity, not AI value. “Treat agents like an employee. You don’t just hire someone and leave them in the corner.” - Leandro Perez Chapters: Fast tech, slow orgAI activity versus AI progressWhy AI pilots fail to become valueWhy personal AI does not equal enterprise AISalesforce’s internal agent lessonsWhy business experts need to manage agentsData, privacy and guardrailsWho owns an agent when it goes wrong?How to set success metrics for agentsWhy AI changes old performance metricsWhat leaders should do next About Applied AI Australia: Applied AI Australia is powered by Acquire Intelligence. We help Australian companies turn AI into revenue, margin, time back, and better operating discipline. Need immediate execution support? From 10 December 2026, APP 1.7 requires organisations using automated systems to make, or substantially influence, decisions about individuals to disclose that fact in their privacy policy. Serious or repeated privacy interferences can carry penalties up to $50 million. If your AI agents, scoring tools, triage systems or automated workflows touch customer decisions, you need a decision map before you can know what must be disclosed. Acquire Intelligence runs six-week, governance-led sprints to build compliant decision maps from scratch before enforcement arrives. Websites: www.appliedaiaustralia.com.auwww.acquireintelligence.ai Ramon:linkedin.com/in/ramonrod Guest: Leandro PerezSVP & CMO ANZ, Salesforcelinkedin.com/in/leandro-perez

    Fast Tech, Slow Org: The AI Execution Gap | Salesforce SVP & CMO ANZ Leandro Perez

  6. Jul 2

    10 December 2026: The AI Decision Deadline Executives Can’t Ignore

    10 December 2026: The AI Decision Deadline Executives Can’t Ignore From 10 December 2026, Australian organisations covered by the Privacy Act face new automated decision-making disclosure obligations under APP 1.7. If AI, automation, scoring tools, workflow engines or legacy SaaS platforms help make decisions about customers, employees or applicants, executives need to know where those decisions happen and whether they are disclosed. This episode gives leaders a practical way to find the gap before the deadline. Map The Decisions Before You Rewrite The Policy Many organisations can list their AI tools. Fewer can show every automated or AI-assisted decision those tools shape. That is the exposure. APP 1.7 makes automated decision-making a privacy policy issue. But the real work starts earlier. Executives need a decision inventory. Send One Email To Expose The Gap Send this to your General Counsel: “Can you show me the current map of every automated or AI-assisted decision that affects customers, employees or applicants, and confirm which of those decisions are already disclosed in our privacy policy?” A clear answer means the organisation has visibility. An unclear answer means the organisation has mapping work to do. Challenge The Three False Comforts This episode breaks down three assumptions that can leave executives exposed: * Vendor contracts do not replace internal accountability. * Human approval does not always remove automated decision-making risk. * Older automation can still be caught by technology-neutral rules. Build The Decision Inventory From Outcomes The practical fix starts with the people affected by the decision. Start with customer, employee and applicant outcomes. Then work backwards to the systems, data, vendors, workflows and approvals that shape those outcomes. That is how leaders find the decisions that a tool register misses. What You’ll Learn * Why 10 December 2026 matters for Australian executives using AI and automation * What APP 1.7 changes under the Privacy Act * Why automated decision-making is broader than generative AI * How to test whether your organisation has a decision inventory * Why vendor contracts and human review may not be enough * How old SaaS platforms and scoring tools can still create disclosure obligations * How to map automated decisions from customer outcomes backwards * Why larger organisations may need 8 to 16 weeks to build a usable decision map Timestamps 0:00 — The boardroom story 0:54 — Welcome to Applied AI Australia 1:09 — APP 1.7 and the new disclosure rule 1:42 — What executives need to know 2:14 — Send this email to your GC 3:55 — Why the answer may be unclear 4:31 — Build a decision map, not just a vendor list 6:52 — Test vendor accountability 8:27 — Test human-in-the-loop approvals 11:50 — Test legacy automation 14:00 — Understand the penalty exposure 15:28 — Start with customer outcomes About Applied AI Australia Applied AI Australia helps executives turn AI complexity into business outcomes: growth, margin, time back and better operating discipline. Each week, Ramon Rodriguez breaks down the AI shifts that matter for Australian leaders, boards and executive teams, so they can stay clear, current and in control with AI. Need Execution Support? Acquire Intelligence runs governance-led sprints to help organisations identify, map and manage automated and AI-assisted decisions before the 10 December 2026 deadline. Visit Acquire Intelligence to close the gap. Legal & Scope Disclaimer This episode provides commercial and operational strategy only. It does not constitute formal legal advice. Your obligations under the Privacy Act depend on your specific circumstances. You must obtain independent advice from your General Counsel or external legal team to settle your compliance posture and privacy policy wording.

  7. Jun 29

    Season 3 Launch: Weekly AI Conversations for Leaders

    Season 3 of Applied AI Australia starts now - with new episodes every week. In this short update, Ramon shares the next chapter for the show, including Applied AI Australia joining Acquire Intelligence and a sharper focus on helping Australian leaders stay clear, current and in control with AI. Expect weekly executive conversations with leaders from Salesforce, Workday, Finder, Compare Club and other Australian businesses making real decisions about AI, strategy, risk, work, growth and governance. The goal is simple: turn AI complexity into practical business outcomes, with conversations and advice leaders can take into Monday morning. Follow the show and share it with a leader who needs it.

  8. May 27

    Machine Customers Are Here. Your Business Was Built for Humans | Katja Forbes

    Machine Customers Are Here. Your Business Was Built for Humans | Katja Forbes, Author of The Machine Customers. Subscribe: If your board is talking AI strategy but nobody has raised machine customers, this episode is the briefing. Your Next Customer Won't Be Human: 5 Machine Buyer Types Hitting Australian Commerce. Adobe reported a 4,700% year-on-year increase in AI agent traffic to retail websites. That is not a forecast. That already happened. Your commerce channels, your checkout, your fraud stack, your entire customer journey were built for humans. There are five distinct types of non-human buyer already trying to transact in the Australian economy, and most mid-market businesses are blocking revenue they do not even know is walking in. Katja Forbes is a executive director at Standard Chartered's corporate and investment bank, AFR 100 Women of Influence, and author of The Machine Customers. She has been mapping the taxonomy of machine buyers since before most boards acknowledged the category existed. If your board's AI strategy does not account for the fact that your next customer might not be human, it has a gap. What you'll learn: The five machine customer types already in market: co-buyer, delegated agent, autonomous buyer, multi-agent network, intermediary broker, and why each one needs a different receptorWhy Salesforce just removed the human UI layer entirely and rearchitected its platform for AI agents as the primary actorsThe shift from KYC to KYA: what agent verification looks like and why American Express is now covering losses from registered agent errorsHow Kyriba's agentic AI is already executing autonomous FX transactions inside a $208 trillion cross-border payments flowWhat went wrong when an AI agent was given $100K and told to open a retail storeKey stats: 4,700% YoY increase in AI agent traffic to retail websites (Adobe).$30 trillion machine customer economy forecast by 2030 (Gartner).$208 trillion in cross-border payment flows, $625 billion in bank revenue. Banks that build headless receptors for machine customers will amplify that number.48-hour action: Identify which of the five machine customer types is most likely to arrive at your business first. Check whether your current channels can serve it. Discoverability gets you into the consideration set. Operational trust is the minimum. Values-based differentiation is what makes the agent choose you over the competitor. "When everyone's discoverable, everyone becomes interchangeable." - Katja Forbes Timestamps: 00:00 - The machine customer economy hits $30 trillion01:00 - Adobe: 4,700% AI agent traffic increase05:00 - The five types of machine customer09:00 - Amazon Rufus, Woolworths Olive, and the neutrality problem13:00 - Salesforce goes headless15:00 - Receptors: why each type needs a different customer journey19:00 - From KYC to KYA: agent verification and privacy22:00 - Ramon's own machine commerce misfire26:00 - The $208T cross-border FX opportunity31:00 - Luna's $100K autonomous business experiment33:00 - Anthropic: Opus vs Haiku trading outcomes36:00 - Your 48-hour action38:00 - Closewww.appliedaiaustralia.com.aulinkedin.com/in/ramonrod Guest:Katja Forbes, Author of The Machine Customers | AFR 100 Women of Influence | linkedin.com/in/katjaforbes Book: amazon.com/dp/1923630008Machine Customer Canvas (free): thecxevolutionist.ai/resources/machine-customer-canvas About Applied AI Australia:We help Australian companies between $100M and $1B turn AI into revenue, margin, time back, and better operating discipline. One podcast and one newsletter each week, built so you can brief a board in under an hour.

    Machine Customers Are Here. Your Business Was Built for Humans | Katja Forbes
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About

Applied AI Australia is the podcast for Australian executives turning AI into measurable business outcomes. Hosted by Ramon Rodriguez - an executive, not a career consultant. This show cuts through AI hype to focus on what matters: Growth and Margins. Now part of Acquire Intelligence, the podcast brings commercially grounded AI strategy and practical executive insight to CEOs, CIOs,CFOs, boards, and senior operators navigating rapid change and competitive pressure. Each week, Applied AI Australia delivers real-world conversations, operator perspectives, and practical frameworks leaders can apply quickly , often within 48 hours. This is not a tech podcast. It is practical AI leadership for executives accountable for results. Subscribe.

Information

  • Creator
    Ramon Rodriguez
  • Years Active
    2025 - 2026
  • Episodes
    24
  • Rating
    Clean
  • Copyright
    © Ramon Rodriguez
  • Show Website
    Applied AI Australia

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