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

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

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

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 

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

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

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

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

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?

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