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Builders by Proxify

Proxify

This is Builders, the podcast where we discuss the ups and downs of building great tech products with the people behind innovative tech products and services.

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  • 25 episodes
  • weekly
  • Avg 39 min
  • English

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  • Thursday · 30 min

    The hidden environmental cost of AI, with Charlotta Brask | Builders

    AI promises to help build a more sustainable future. But how sustainable is AI itself?Behind every AI model is a physical infrastructure that consumes energy, water, and natural resources. And as companies race to adopt AI, its environmental footprint is becoming harder to ignore.In this episode of Builders, Lee Haynes sits down with Charlotta Brask, Head of Sustainability at Telia Sweden, to discuss the hidden environmental costs of AI and what companies can do to build more responsibly.With over 20 years of experience in sustainability, Charlotta shares her perspective on why bigger AI models aren’t always better, how companies can measure their AI footprint, and why sustainability needs to become part of everyday engineering decisions.They discuss the environmental impact of data centers, the resources required to manufacture AI hardware, and how Telia is integrating sustainability into its AI governance.In this episode: The difference between AI for sustainability and sustainable AI The hidden environmental and social costs of AI Why smaller AI models can be a smarter choice How to measure energy, water, and resource consumption The role of engineering teams in building sustainable AI How Telia approaches responsible AI development Can AI help solve environmental challenges without creating new ones?Subscribe to Builders for more conversations with the people building the future of technology.

  • October 14 · 28 min

    Are we losing control of AI? The future of human autonomy, with Ingo Paas | Builders

    Are we losing control of AI? As intelligent systems become increasingly autonomous, who takes responsibility for the decisions they make?In Part 2 of Builders, our host Lee Haynes continues his conversation with Ingo Paas, former CIO of Green Cargo, now board member at Svenska kraftnät, and author of A Billion Times Smarter and Digital Composable Enterprises.Drawing on his experience leading digital transformation and researching the implications of superintelligence, Ingo explores what happens when decision-making authority shifts from humans to intelligent systems.They discuss what it means to preserve human agency in an AI-driven world, whether we can maintain control over increasingly autonomous systems, and why traditional approaches to AI governance may no longer be enough.The conversation also explores how AI is changing leadership, why five-year business strategies are becoming harder to defend, and what skills will matter as intelligent systems become more capable. In this episode: • Why human agency matters in the age of AI • What happens when machines start making decisions for us • How AI twins could help protect human autonomy • Why AI’s impact matters more than its intelligence • Whether losing control of AI is inevitable • Who takes responsibility when autonomous systems make decisions • The rise of superintelligence and intelligent infrastructure • What skills will matter in an AI-driven world • Why traditional business strategies need to change • How leaders can prepare for the future of AI Chapters: 00:00 Introduction: The rise of human agency 03:31 How do we preserve human autonomy? 05:08 The concept of an AI twin 06:34 Can we measure AI's impact on society? 07:48 Are we asking the wrong questions about AI? 10:58 Can we avoid losing control of AI? 13:21 Who is responsible for autonomous AI? 14:46 Superintelligence and the future of humanity 17:43 What skills will matter in an AI-driven world? 19:47 Knowledge vs knowing how to apply it 21:12 How businesses need to change 21:44 Why long-term strategy is becoming obsolete 23:28 Strategy inside intelligent infrastructure 25:17 What leaders should do in the next 12 months 26:00 Final thoughts About BuildersBuilders is a podcast by Proxify exploring the people, ideas, and technologies shaping the future of business and technology. Hosted by Lee Haynes, each episode features conversations with founders, executives, and technology leaders about building, leading, and adapting in a changing world. Subscribe for more conversations about AI, technology, leadership, and the future of business.

  • October 8 · 31 min

    The end of SaaS: Why AI will replace your software, with Ingo Paas | Builders

    What if the future of AI in business isn’t about adding AI to your existing software, but replacing the software altogether? In Part 1 of this conversation, our host Lee Haynes sits down with technology executive, board member, and author Ingo Paas to examine why most companies may be approaching AI transformation from the wrong direction. Organizations are adding AI to existing processes, SaaS applications, and fragmented technology stacks. Ingo argues that this misses the bigger shift. If AI can take over the processes those applications were built to support, much of today’s SaaS stack could eventually disappear.They also discuss why traditional governance struggles to keep pace with AI, why boards are often not asking the right questions, and why three and five-year strategies become increasingly difficult when the underlying technology can change dramatically within months. In this episode: • Why traditional governance struggles with AI • The growing transfer of decision-making authority to machines • What boards are missing about AI transformation • Why AI could make organizational fragmentation worse • How leaders should balance experimentation with running the existing business • Why adding AI to existing SaaS may be the wrong approach • Ingo’s case for replacing SaaS applications with AI • Why companies need to start redesigning from the core Chapters: 00:00 Introduction 01:42 Ingo Paas’ background 03:06 Why AI transformation is only beginning 04:38 Why traditional governance fails with AI 06:45 What AI governance should look like 09:10 Governing AI in real time 11:28 The data problem companies already have 13:50 When machines start making decisions 14:40 What surveillance and warfare teach us about AI 19:10 What boards are getting wrong about AI 22:27 Why AI requires a different kind of leadership 24:32 The problem of fragmented organizations 25:54 Is AI the end of SaaS? 28:52 Experimentation vs paralysis

  • September 30 · 23 min

    How to become an AI engineer in 2026, with Joana Fonseca | Builders

    What does it take to build a career in AI when the technology changes faster than any university curriculum can keep up? Joana Fonseca is an AI engineer at TRATON with a PhD in machine learning and robotics from KTH Royal Institute of Technology. Yet even after a bachelor’s, master’s, and PhD, she had never learned or used generative AI. When she finished her PhD, she had to learn transformers and generative AI herself. In this episode of Builders, Joana talks about the foundations AI engineers need, why continuous learning is unavoidable, and what separates someone who can build an impressive prototype from someone who can build reliable, valuable AI systems. She also discusses the importance of domain collaboration and data engineering, what makes a company truly AI-native, where vision language models (VLMs) and vision language action models (VLAs) are heading, and what AI could look like by 2030. Joana also shares what she is seeing through Stockholm AI and why the conversation around AI increasingly extends beyond models into infrastructure, governance, policy, and societal impact. Chapters 00:00 VLMs and autonomous driving 00:34 Introduction 00:48 What does it take to become an AI engineer? 01:38 The foundations every AI engineer needs 02:03 Why continuous learning is unavoidable 02:37 Even PhDs had to learn generative AI 03:05 Learning AI after a PhD 04:39 How important is domain expertise? 05:04 Why collaboration matters in AI 06:15 What separates strong AI engineers? 06:31 Building AI beyond the prototype 07:04 The importance of data engineering 07:17 What the AI community is talking about 09:30 AI's impact beyond technology 10:50 What makes a company AI-native? 12:30 What's next for VLMs and VLAs? 13:30 The causality problem in autonomous driving 16:00 How researchers are improving VLAs 17:00 What will AI look like in 2030? 19:00 Advice for the next generation of AI engineers 19:22 Why lifelong learning matters

  • September 23 · 23 min

    Why autonomous driving is still so hard, with Joana Fonseca | Builders

    Joana Fonseca is an AI engineer at TRATON, working on AI for autonomous driving. In this episode of Builders, Joana explains how vision language models (VLMs) and vision language action models (VLAs) could change autonomous driving by helping vehicles understand entire scenarios rather than simply detecting individual objects. She also breaks down what makes deploying AI in the physical world so difficult, from latency and model size to safety, edge cases, testing, and the huge amounts of diverse real-world data required. Joana holds a PhD in machine learning and robotics from KTH Royal Institute of Technology. Her work has taken her from developing algorithms for autonomous submarines to building AI systems for autonomous trucks at TRATON.In this episode: How VLMs differ from traditional computer vision How AI can understand an entire driving scenario The role of VLAs in autonomous vehicles Why 99.99% reliability may still not be enough Why latency becomes critical when AI operates in the physical world The data problem behind autonomous driving Why moving from prototype to production remains so difficult What production-ready AI looks like for autonomous systems Subscribe to Builders for more conversations with the people building the future of technology, AI, and engineering. Chapters (00:00) Introduction (01:41)⁠ From robotics to autonomous driving (03:49)⁠ Building AI for autonomous trucks (⁠04:47)⁠ What are vision language models? (⁠05:48)⁠ VLMs vs traditional computer vision (⁠07:34)⁠ From VLMs to vision language action models (⁠09:51)⁠ The black box problem (⁠12:38)⁠ Why 99.99% safety isn’t enough (⁠13:57)⁠ When AI latency becomes dangerous (⁠15:01)⁠ Testing AI in the physical world (⁠16:12)⁠ The autonomous driving data problem (⁠17:28)⁠ Where current AI models fall short (⁠18:49)⁠ Why prototypes struggle to reach production (⁠19:52)⁠ Why robotaxis haven’t scaled everywhere (⁠21:36)⁠ What makes AI production-ready

  • July 27 · 22 min

    The Last AI Mile with Zimply.ai's Jesper Fredriksson | Proxify Talks

    AI is rapidly evolving from a powerful tool into a transformative force for organizations and society.In this keynote, Jesper Fredriksson explores the current state of AI, the accelerating pace of technological advancement, and the challenges organizations face when moving from experimentation to large-scale adoption.A central theme is the "last mile" problem—the gap between impressive AI capabilities and fully automated, real-world outcomes. Jesper discusses how AI is reshaping software engineering, productivity, and decision-making, while highlighting the importance of human oversight, guardrails, and responsible implementation.In this episode:- AI's evolving capabilities and future potential- The last mile challenge in automation- AI's impact on software engineering and productivity- Technical and organizational barriers to adoption- Human oversight and guardrails in AI systems- Risks and ethical considerations of advanced AI- Moving from AI experimentation to measurable outcomes#AI #Automation #ArtificialIntelligence #SoftwareEngineering #Productivity #FutureOfAI #AIInnovation

  • July 24 · 13 min

    The hidden security risks of AI agents, with Ali Leylani | Proxify Talks

    I agents are becoming more capable and more autonomous. But as they gain access to tools, memory, and company data, they also introduce new security risks. In this Proxify Talk, Ali Leylani, Co-founder & Chief AI Officer at Echo Alpha and Chairman of Stockholm AI, explains how AI agent systems work, where they’re vulnerable, and what engineering teams should think about before deploying them at scale. Drawing on his background in AI, cybersecurity, and defense technology, Ali explores the emerging attack surface created by autonomous and multi-agent systems. In this episode, you’ll learn: • How AI agents combine language models, memory, and external tools to perform complex tasks • Why multi-agent systems introduce new security challenges • How prompt injection, jailbreaks, and other attack techniques target AI systems • Why a single compromised agent or data source can affect an entire workflow • How malicious instructions can be hidden inside images and other multimodal inputs • What organizations should consider before giving AI agents greater autonomy. Whether you’re building AI products, leading an engineering team, or exploring agentic workflows, this talk offers a practical look at one of the biggest challenges facing AI adoption today. #AISecurity #ArtificialIntelligence #CyberSecurity #AIAgents #ProxifyTalks

  • July 15 · 29 min

    Why AI adoption fails with Louise Vanerell & Carl Carlheim-Gyllensköld | Proxify Talks

    AI implementation is easy. AI adoption is hard.In this episode, Louise Vanerell and Carl Carlheim-Gyllensköld explore why so many AI initiatives fail to create lasting organizational value despite strong technology investments. The conversation focuses on a critical but often overlooked element of successful AI transformation: the human layer.Together, they discuss how organizations can move beyond technical deployment and drive real behavioral change through effective communication, psychological safety, change management, and internal champions.Whether you’re leading an AI transformation, building an AI strategy, or helping teams adapt to new technologies, this episode offers practical insights for turning AI adoption into measurable business outcomes.In this episode: Why AI adoption often fails in organizations The importance of engineering the human layer Building trust and psychological safety around AI Effective communication and change management strategies The role of champions in technology diffusion Driving behavioral change across teams Moving from AI usage to organizational value Practical lessons for leaders navigating AI transformation If you enjoyed this conversation, subscribe for more discussions on AI strategy, organizational transformation, leadership, and the future of work.#AIAdoption #ArtificialIntelligence #ChangeManagement #AIStrategy #DigitalTransformation #Leadership #FutureOfWork #Innovation #BusinessTransformation #AI

  • S1 · E1
    July 8 · 22 min

    Closing the AI Gap, with Atlan AI’s Rocío Bachmaier | Proxify Talks

    How do companies move beyond AI pilots and actually scale AI across their organization?In this keynote from Proxify HQ, AI strategist and transformation expert Rocío Bachmaier shares practical lessons from helping organizations adopt, implement, and scale AI successfully. Drawing from real-world experience working with companies across industries, she explores the most common mistakes that prevent AI initiatives from delivering meaningful ROI, and what forward-thinking organizations are doing differently.You’ll learn:• The 3 biggest mistakes companies make when scaling AI• Why leadership involvement is critical for AI transformation• How to identify high-impact AI workflows• The difference between AI experimentation and AI adoption• Why AI governance shouldn’t be an afterthought• How AI-native companies are redesigning workflows• What agentic AI means for the future of work• The concept of compound learning in AI systems• How organizations can balance human expertise and AI capabilities• Why AI literacy and training are becoming essential business skillsWhether you’re a founder, technology leader, product manager, developer, or business executive, this keynote offers actionable insights for building AI strategies that create lasting value.This keynote is part of a new series from Proxify, where we share conversations, talks, and expert insights from our headquarters with the wider technology community. Our goal is simple: foster curiosity, share knowledge, and help professionals navigate the future of technology together. Subscribe for more keynotes, expert discussions, the Builders podcast, and insights on AI, software development, leadership, and the future of work.About Proxify:Proxify connects businesses with the world’s top remote software developers, helping companies scale engineering teams quickly and effectively.Learn more: https://proxify.io

  • July 1 · 47 min

    How Slack actually uses AI at work

    AI is changing technical roles faster than ever. But does that mean customer-facing engineers are becoming obsolete?In this episode of Builders, Lee Haynes sits down with Liliana Lindberg, Lead Solutions Engineer at Slack, who has also worked at Google and startups throughout her career. They discuss what solutions engineers actually do, why technical expertise still matters in the age of AI, and why blindly trusting AI can create bigger problems than it solves.Liliana also shares her unconventional journey into tech, why she thought programming wasn’t for her, and how she built a successful career without following the traditional management path.In this episode, you’ll learn:• What a Solutions Engineer actually does• Why AI won’t replace customer-facing technical roles• How Slack uses AI to improve productivity• The biggest mistakes people make when using AI• Why technical knowledge is still essential in the AI era• Startup vs. big tech, what each environment teaches you• Why becoming a manager isn’t the only path to career growth• The skills every modern Solutions Engineer needs• How curiosity became Liliana’s biggest career advantage• Practical advice for anyone building a career in technologyWhether you’re a software engineer, solutions engineer, engineering leader, founder, or simply curious about how AI is reshaping work, this conversation offers practical insights you can apply today.Subscribe for more conversations with engineering leaders, technology executives, and innovators building the future of software.Chapters00:00 Introduction01:09 From psychology to engineering03:13 Thinking she chose the wrong career04:37 What a Solutions Engineer actually does07:00 The biggest misconceptions about the role09:23 The skills that matter most12:41 Startups vs. big tech18:31 Why she chose the individual contributor path22:46 How AI is changing technical work26:32 Why you shouldn’t trust AI blindly28:54 Why Slack changed how she works31:18 AI agents and the future of collaboration34:57 Avoiding AI tool overload36:36 The best workflow she’s seen in Slack37:46 What she looks for when hiring41:58 Building trust in remote teams42:57 Career advice she wishes she’d received sooner45:15 What’s next for Slack46:42 Advice for aspiring Solutions Engineers

  • S3 · E20
    June 23 · 49 min

    The end of traditional companies? How AI Is reshaping leadership, hiring & work

    AI, organizational transformation, leadership, hiring, AI native companies, the future of work, organizational design, and AI adoption are changing how businesses operate. In this episode of Builders, Armin Catovic, Director of Data & AI at Funnel, shares a fascinating perspective on how AI is fundamentally transforming organizations—from leadership structures and hiring practices to decision-making, workflows, and team design. We dive into the critical difference between AI-enabled and AI-native companies, why many organizations are underestimating the scale of change ahead, and how AI could reshape competition across entire industries. Armin also explores the future of talent, the evolving role of managers, and why smaller, more agile teams may become the new standard in the AI era. If you're a founder, executive, manager, or technology leader trying to understand what AI means for the future of work, this conversation is packed with practical insights and forward-looking ideas. Topics covered :• AI-enabled vs. AI-native organizations • Organizational transformation through AI • Leadership and decision-making in the AI era • The future of hiring and talent development • AI-driven workflows and collaboration • Attention economics and competitive advantage • Smaller, more effective teams • Operationalizing AI across the business • The future of work and organizational design #AI #FutureOfWork #Leadership #ArtificialIntelligence #AINative #AIAdoption #OrganizationalTransformation #Hiring #Management #BuildersPodcast Chapters (00:00) Why AI Is Forcing Companies to Rethink Everything (01:12) Armin Catovic's Journey into Data, AI & Leadership (03:02) How AI Is Changing Organizations Faster Than Expected (04:42) AI-Enabled vs. AI-Native Companies: The Critical Difference (06:49) Why Traditional Organizational Structures May Not Survive AI (09:07) AI's Growing Role in Workflows, Decisions & Execution (12:17) Addressing Fear, Uncertainty & Workforce Concerns Around AI (16:21) The Surprising Relationship Between AI and Software Demand (18:13) Winning the Battle for Attention in the AI Era (20:53) How AI Is Reshaping Competition Across Industries (22:02) The Future of Talent and the Rise of Junior Inversion (24:39) Developing Talent in an AI-Driven Workplace (25:06) Why Investing in Future Talent Matters More Than Ever(27:09) Essential Skills for Success in the Age of AI (30:22) How Collaboration Is Evolving Across Modern Tech Teams (32:49) Leadership in the Age of AI: What Changes and What Doesn't (36:03) Why Smaller, More Agile Teams Are Winning (37:44) Moving AI from Experiments to Real Business Impact (43:31) Becoming an AI-Native Company: Practical Steps Forward (45:44) The Biggest AI Surprises Still Ahead

  • S3 · E19
    June 16 · 41 min

    Why most AI projects fail in production (and how Amazon solves it)

    Data Engineering, Machine Learning, AI, Data Trust, AI Governance, and ML Ops are transforming how companies scale intelligent systems. In this episode of Builders, Deepak Yadav, Engineering Leader at Amazon with 19+ years of experience in data engineering, analytics, and AI/ML, shares what it really takes to move machine learning from proof of concept to production. We explore the hidden challenges behind operationalizing AI, building trusted data platforms, implementing AI governance at scale, and creating systems that organizations can rely on. Deepak also discusses how AI is reshaping data engineering, the future of automation, and emerging trends like decision intelligence, synthetic data, and self-healing systems. Whether you're a tech executive, AI leader, VP of Engineering, CTO, Data Engineer, or ML engineer, this conversation offers practical insights into scaling AI responsibly and effectively. Topics covered: • Data Engineering and AI at scale • Machine Learning production challenges • Data Trust and AI Governance • ML Ops best practices • AI adoption in enterprises • Hiring and scaling data teams • Startups vs. enterprises in AI • Decision intelligence and synthetic data • Future AI trends and automation #DataEngineering #MachineLearning #AI #MLOps #DataTrust #AIGovernance #DataPlatforms #DataScience #AITech #BuildersPodcast

  • S3 · E16
    June 3 · 50 min

    Why most Software Engineers are preparing for the future wrong

    Leadership, engineering teams, AI in software development, responsible tech, career growth, and the future of work. How do great engineering leaders build high-performing teams in the age of AI? In this episode of Builders, Bosch’s Kamyar Gilak shares practical insights on leadership, team building, AI tools, software engineering, responsible tech, sustainability, and staying relevant in a rapidly changing industry. We explore how AI is transforming software development, hiring, code reviews, and career growth, while discussing what skills engineers and leaders need to thrive in the future of work. Kamyar also shares lessons from startups and large organizations, strategies for building learning-driven cultures, and why responsible AI and sustainable team practices matter more than ever. Whether you’re a software engineer, engineering manager, tech leader, founder, or someone navigating the impact of AI on your career, this conversation offers actionable advice for building resilient teams and staying ahead of industry shifts. Subscribe for more conversations with technology leaders, founders, and builders shaping the future. #Leadership #AI #SoftwareEngineering #EngineeringManagement #FutureOfWork #ResponsibleAI #CareerGrowth #TeamBuilding #Bosch #TechPodcast #ArtificialIntelligence #FutureOfWork #Developers #Programming #SoftwareDeveloper #TechCareers #StartupLife #Sweden #Germany #Netherlands #NorthAmerica #UK Chapters (00:00) Meet Kamyar Gilak: Leadership, AI & Engineering (02:19) The Leadership Framework: Trust, Clarity & Ownership (06:01) Connecting Talent, Culture & Business Success (08:16) Creating a Culture of Continuous Learning (11:44) How AI Is Reshaping Software Development (19:19) Startup Lessons from Large-Scale Organizations (23:32) The Hiring Challenge: AI’s Growing Influence (26:20) Why AI Is a Developer’s Tool, Not a Replacement (29:10) How AI Is Changing Candidate Evaluation (30:59) The Future of Software Engineering in an AI World (37:39) Protecting Team Focus in a High-Change Environment (43:59) The Most Exciting Advances in Engineering & AI (45:25) The Biggest Shift Happening in Software Development

  • May 27 · 43 min

    This data science mistake is killing AI projects

    Data science, AI, spam detection, fraud prevention, MLOps, and machine learning teams are reshaping how product companies build trust at scale. In this episode of Builders, Liniker Seixas, Senior Staff Data Scientist and Team Lead at @truecaller , explains how data science teams can move beyond experiments and build models that actually work in production.Why do so many companies fail to turn data science into business impact, and what does Truecaller do differently?Liniker shares:- How to build practical data science teams that ship real products- Why hiring “unicorn data scientists” is usually the wrong move- How data engineers, MLOps engineers, and product owners support model success- Why vanity metrics like F1 scores and accuracy are not enough- How Truecaller adapts models in a fast-moving spam and fraud environment- Why user feedback is essential for improving spam and fraud detection- How to hire data scientists for curiosity, adaptability, and learning speed- What senior data science hires bring to early-stage and scaling teams- How to build long-term technical strategy without betting everything on today’s AI trendsIf you’re building data science teams, scaling machine learning products, fighting fraud and spam, or trying to connect AI work to real business outcomes, this episode delivers practical lessons from one of the most demanding product environments in tech. 🎧 Subscribe to Builders for more conversations with leaders shaping the future of AI, data science, engineering, and product innovation.#DataScience #AI #MachineLearning #MLOps #FraudDetection #SpamDetection #Truecaller #DataEngineering #ProductLeadership #BuildersPodcastChapters(00:00) How Truecaller Builds Data Science Teams That Ship(01:21) Liniker Seixas’ Journey Into Data Science Leadership(03:35) Why Companies Get Data Science Teams Wrong(04:04) The Magic Wand Fallacy in Data Science Hiring(05:54) Why Data Scientists Shouldn’t Own Everything Alone(07:36) Why Data Science Needs Engineering Support to Scale(08:08) What a Well-Balanced Data Science Team Looks Like(10:09) How Truecaller Keeps AI Models Fresh Against Spam and Fraud(10:51) Why Fast Delivery Beats Eight-Month AI Projects(12:38) What Separates Successful Data Products From Failed Ones(13:22) Why Business Impact Matters More Than Perfect Models(14:28) How to Keep Data Science Anchored to Product Outcomes(16:11) How Truecaller Measures Success Through User Feedback(17:18) Why Guardrail Metrics Matter in Data Science Experiments(18:28) How Truecaller Reframed Spam Detection Around User Behavior(20:38) Building ML Models in a Cat-and-Mouse Fraud Environment(22:16) Why Model Drift and Continuous Learning Matter(24:07) How to Hire Data Scientists for Curiosity and Learning Speed(26:35) Internal Mobility and Growth Inside Data Science Teams(28:28) Why Adaptability Beats the Perfect CV in AI Hiring(30:00) How AI Is Changing Technical Skill Assessment(30:25) Why Data Scientists Must Stay Relevant(31:46) The Role of Senior Data Scientists in Scaling Teams(33:19) Building a Five-Year Vision for Data Science Teams(35:45) How to Prioritize Ideas Across a Long-Term Roadmap

  • May 27 · 43 min

    The tech behind IKEA: secrets to global loyalty success

    Discover how IKEA's Head of Engineering Loyalty, Jip Koudjis, is transforming global loyalty systems to deliver personalized experiences at scale. Learn how IKEA consolidated 11 local programs into one unified platform, driving data-driven growth and customer engagement. Explore the challenges of aligning teams across 31 countries and the strategic role of AI in personalization. This episode is a masterclass in scaling engineering and loyalty tech, essential for leaders in global tech transformation and organizational change. Chapters: 00:00 Introduction to Global Engineering Leadership 02:28 Transforming IKEA's Loyalty Systems 05:52 Aligning Teams Across 31 Countries 10:53 The Strategic Role of AI in Personalization 13:16 Challenges in Data-Driven Growth 18:20 Building a Unified Global Platform 24:14 Leadership's Role in Tech Transformation 27:42 Emerging Trends in Loyalty Tech 32:25 Future of Global Customer Engagement 39:12 Key Success Factors in Engineering Leadership

  • S3 · E15
    May 13 · 52 min

    How Philips is actually scaling AI

    Data Engineering, AI Experimentation, Health Tech, and Data Platforms are reshaping enterprise innovation. In this episode of Builders, Jonas Dieckmann, Global Manager of Data Intelligence & Team Lead of Data Engineering at Philips, explains how one of the world’s largest health tech companies is scaling AI through cross-functional collaboration, domain-driven data platforms, and rapid experimentation. Why do so many enterprise AI initiatives fail — and what is Philips doing differently? Jonas shares: How AI squads accelerate innovation inside large organizations Why short AI experiments lead to faster business impact The evolution from centralized platforms to data mesh architectures How metadata and data lineage are becoming critical for AI success The biggest challenges in healthcare data and governance What makes a great data engineer in the AI era The trends shaping the future of data and AI If you’re building data platforms, scaling AI teams, or navigating enterprise transformation, this episode delivers practical insights from the frontlines of global health tech. 🎧 Subscribe to Builders for more conversations with leaders shaping the future of AI, engineering, and innovation. #DataEngineering #AI #HealthTech #DataPlatform #DataMesh #Philips Chapters (00:00) How Philips Is Driving Data Innovation in Health Tech (01:24) Jonas Dieckmann’s Journey Into Data & AI Leadership (02:44) The Biggest Challenges of Data Platforms in Healthcare (05:27) Why Health Tech Data Is More Complex Than Most Industries (08:07) Inside Philips’ AI Squad Strategy for Innovation (13:15) How Philips Chooses AI Use Cases That Actually Matter (16:36) Why Fast AI Experiments Lead to Better Results (22:46) The Shift From Centralized Data Platforms to Data Mesh (28:35) Data Governance and Ownership in a Data Mesh World (30:37) What Future Data Platforms Must Support for AI (33:01) Why Metadata and Data Lineage Are Becoming Essential (35:26) What Separates Great Data Engineers From the Rest (39:58) How Philips Evaluates Talent for Data & AI Teams (45:30) The Most Exciting Trends in Data and AI Right Now (47:39) The Biggest Mistakes Companies Make When Scaling AI (50:03) Jonas Dieckmann’s Vision for the Future of Data at Philips

  • S3 · E14
    May 6 · 45 min

    The AI race has officially changed

    AI, Data Innovation, Data Strategy, and AI Leadership are redefining how companies compete. In this episode of Builders, Goran Cvetanovski, founder & CEO of Hyperite, shares key insights from the Data Innovation Summit in Stockholm, from operationalizing AI and building scalable data infrastructure to AI governance, leadership alignment, and the future of enterprise transformation.Why are some companies turning AI into a strategic advantage while others are stuck in endless experimentation?Goran breaks down: Why AI operationalization is the next big challenge How leadership teams should approach AI adoption The real value of data ecosystems and infrastructure Whether companies should build or rent AI capabilities The biggest hiring and talent shifts happening in AI right now What separates future AI winners from everyone else If you’re leading digital transformation, building AI products, or preparing your company for the next wave of Generative AI, this episode is packed with practical insights. Subscribe to Builders for more conversations with founders, operators, and tech leaders shaping the future. #AI #DataInnovation #GenerativeAI #AILeadership #DataStrategy #DigitalTransformation Chapters (00:00) Why the Data Innovation Summit Matters in 2026 (02:28) AI’s Biggest Shift: From Experimentation to Real Operations (05:52) How Companies Are Restructuring Around AI (10:53) Why AI and Data Are Becoming Core Business Assets (13:16) Should Companies Build or Rent Their AI Stack? (18:20) The Hidden Challenges of Data Management and AI Leadership (24:14) Why Leadership Determines AI Success or Failure (27:42) The Biggest AI Trends Emerging From the Summit (32:25) AI Hiring Trends: The Skills Companies Need Most (39:12) What Will Separate AI Winners From Everyone Else?

  • S3 · E13
    April 29 · 44 min

    How AI is transforming retail at H&M

    AI, retail, demand forecasting, personalization, organizational structure, AI trends—key insights in this episode. We talk with Sander Ardinois (VP of AI at H&M) about how AI is reshaping retail, from forecasting to personalization and beyond. Chapters (00:00) Introduction to AI in Retail (01:11) Sander’s Journey into AI and Machine Learning (04:10) The Intersection of Data and Retail (05:35) Building Data and AI Foundations at Scale (08:15) Creating Value through Cross-Functional Collaboration (10:29) Evolving AI Talent Landscape (15:43) Bridging Technical and Business Perspectives (18:06) From Experimentation to Real Impact (21:06) Data Readiness and Process Bottlenecks (24:23) AI Transformations in Retail (27:12) Innovations in Demand Forecasting (29:31) Untapped Opportunities in Customer Data (31:05) Generative AI and Creativity in Retail (35:15) The Future of AI-Powered Retail (37:00) Industry Trends and AI Maturity (39:46) Underrated AI Applications (41:04) Overhyped Aspects of AI

  • S3 · E12
    April 8 · 57 min

    AI is rewriting engineering careers, and here's how to stay ahead

    AI in engineering, career development, and engineering leadership are changing fast—and most engineers aren’t ready. In this episode, Lobo Olsson reveals how AI tooling, automation, and tech innovation are reshaping the future. Learn the truth about generalist vs specialist roles, how to build future-proof skills, and what it really takes to survive and thrive in the AI era. Chapters (00:00) The Journey of a Generalist (11:23) Navigating the Balance: Generalist vs Specialist (15:02) Challenges in AI Tool Adoption (22:21) AI’s Strengths and Limitations in Engineering (29:32) Leadership in a Dynamic Tech Landscape (30:00) Engaging and Motivating Teams in AI Adoption (34:59) AI Literacy: A New Essential for Developers (40:06) Evolving Recruitment Strategies in the AI Era (45:56) The Future of AI: Opportunities and Cautions (55:14) Advice for Future Tech Leaders

  • S3 · E11
    March 31 · 44 min

    How Siemens is powering a greener future (and what IT leaders must learn)

    Sustainability, IT, digital transformation: inside Siemens’ journey using AI, greener data centers, and smarter operations to drive decarbonization. Helena Babelon shares how strategy, circularity, and sustainability reporting come together to shape a more sustainable future. Chapters (00:00) Introduction to IT Sustainability and Helena’s Journey (02:54) The Role of IT in Driving Sustainability at Siemens (05:59) Siemens’ 360-Degree Sustainability Framework (08:50) Sustainable IT: Where to Start? (12:07) Embedding Sustainability in IT Procurement (14:47) Measuring Impact: Handprints, Footprints, and Heartprints (17:52) AI and Digitalization for Sustainability (24:02) Leadership and Collaboration in Sustainability (30:01) Future Trends in Sustainable IT

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