How AI is transforming retail at H&M

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  [https://studio.youtube.com/channel/UCtz1lDuJXH7ShIa6n4UAEAg], 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

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

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

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?

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