#319 Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpot

Financial institutions are racing to harness the power of AI, but the path to implementation is filled with challenges. From feature engineering to model deployment, the technical complexities of AI adoption in finance require careful navigation of both technological and regulatory landscapes. How do you build AI systems that satisfy strict compliance requirements while still delivering business value? What skills should teams prioritize as AI tools become more accessible through natural language interfaces? With the pressure to reduce model development time from months to days, how can organizations maintain proper governance while still moving at the speed modern business demands?Vijay is a seasoned analytics, product, and technology executive. As EVP of Global Solutions & Analytics at Experian, he runs the department that creates Experian's Ascend financial AI platform. Promoted multiple times in eight years, Vijay now leads a team of more than 70 at Experian. He is one of the youngest execs at Experian, believing strongly in understanding and accepting risk. He has built and run data, engineering, and IT teams, and created market-leading products.In the episode, Richie and Vijay explore the impact of generative AI on the finance industry, the development of Experian's Ascend platform, the challenges of fraud prevention, education and compliance in AI deployment, and much more.Links Mentioned in the Show:ExperianExperian AscendConnect with VijayCourse: Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Transforming Finance with Andrew Reiskind, CDO at MastercardRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The manufacturing floor is undergoing a technological revolution with industrial AI at its center. From predictive maintenance to quality control, AI is transforming how products are designed, produced, and maintained. But implementing these technologies isn't just about installing sensors and software—it's about empowering your workforce to embrace new tools and processes. How do you overcome AI hesitancy among experienced workers? What skills should your team develop to make the most of these new capabilities? And with limited resources, how do you prioritize which AI applications will deliver the greatest impact for your specific manufacturing challenges? The answers might be simpler than you think.Barbara Humpton is President and CEO of Siemens Corporation, responsible for strategy and engagement in Siemens’ largest market. Under her leadership, Siemens USA operates across all 50 states and Puerto Rico with 45,000 employees and generated $21.1 billion in revenue in fiscal year 2024. She champions the role of technology in expanding what’s humanly possible and is a strong advocate for workforce development, mentorship, and building sustainable work-life integration. Previously, she was President and CEO of Siemens Government Technologies, leading delivery of Siemens’ products and services to U.S. federal agencies. Before joining Siemens in 2011, she held senior roles at Booz Allen Hamilton and Lockheed Martin, where she oversaw programs in national security, biometrics, border protection, and critical infrastructure, including the FBI’s Next Generation Identification and TSA’s Transportation Workers’ Identification Credential.Olympia Brikis is a seasoned technology and business leader with over a decade of experience in AI research. As the Technology and Engineering Director for Siemens' Industrial AI Research in the U.S., she leads AI strategy, technology roadmapping, and R&D for next-gen AI products. Olympia has a strong track record in developing Generative AI products that integrate industrial and digital ecosystems, driving real-world business impact. She is a recognized thought leader with numerous patents and peer-reviewed publications in AI for manufacturing, predictive analytics, and digital twins. Olympia actively engages with executives, policymakers, and AI practitioners on AI's role in enterprise strategy and workforce transformation. With a background in Computer Science from LMU Munich and an MBA from Wharton, she bridges AI research, product strategy, and enterprise adoption, mentoring the next generation of AI leaders.In the episode, Richie, Barbara, and Olympia explore the transformative power of AI in manufacturing, from predictive maintenance to digital twins, the role of industrial AI in enhancing productivity, the importance of empowering workers with new technology, real-world applications, overcoming AI hesitancy, and much more.Links Mentioned in the Show:Siemens Industrial AI SuiteConnect with Barbara and OlympiaCourse: Implementing AI Solutions in BusinessRelated Episode: Master Your Inner Game to Avoid Burnout with Klaus Kleinfeld, Former CEO at Alcoa and SiemensRewatch RADAR AI where...

The line between human work and AI capabilities is blurring in today's business environment. AI agents are now handling autonomous tasks across customer support, data management, and sales prospecting with increasing sophistication. But how do you effectively integrate these agents into your existing workflows? What's the right approach to training and evaluating AI team members? With data quality being the foundation of successful AI implementation, how can you ensure your systems have the unified context they need while maintaining proper governance and privacy controls?Karen Ng is the Head of Product at HubSpot, where she leads product strategy, design, and partnerships with the mission of helping millions of organizations grow better. Since joining in 2022, she has driven innovation across Smart CRM, Operations Hub, Breeze Intelligence, and the developer ecosystem, with a focus on unifying structured and unstructured data to make AI truly useful for businesses. Known for leading with clarity and “AI speed,” she pushes HubSpot to stay ahead of disruption and empower customers to thrive.Previously, Karen held senior product leadership roles at Common Room, Google, and Microsoft. At Common Room, she built the product and data science teams from the ground up, while at Google she directed Android’s product frameworks like Jetpack and Jetpack Compose. During more than a decade at Microsoft, she helped shape the company’s .NET strategy and launched the Roslyn compiler platform. Recognized as a Product 50 Winner and recipient of the PM Award for Technical Strategist, she also advises and invests in high-growth technology companies.In the episode, Richie and Karen explore the evolving role of AI agents in sales, marketing, and support, the distinction between chatbots, co-pilots, and autonomous agents, the importance of data quality and context, the concept of hybrid teams, the future of AI-driven business processes, and much more.Links Mentioned in the Show:Hubspot Breeze AgentsConnect with KarenWebinar: Pricing & Monetizing Your AI Products with Sam Lee, VP of Pricing Strategy & Product Operations at HubSpotRelated Episode: Enterprise AI Agents with Jun Qian, VP of Generative AI Services at OracleRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The modern workplace often glorifies constant productivity and hustle culture, but at what cost? More professionals are burning out earlier in their careers, while elite athletes are extending their peak performance years. What can business leaders learn from high-performance sports about energy management and sustainable success? How do you distinguish between your 'inner game'—managing your energy and purpose—and your 'outer game' of business skills and execution? Could simple techniques like compartmentalization, breathing exercises, and finding deeper purpose transform your professional effectiveness? What if the key to avoiding burnout isn't working less, but working differently?Dr. Klaus Kleinfeld is an international executive, investor, and entrepreneur. He is the Founder and CEO of K2Elevation, which develops and invests in technology and biotech ventures across Germany, Austria, and the U.S. He serves as Chairman of KONUX and FERNRIDE, sits on the supervisory boards of GreyOrange, Fero Labs, and NEOM, and is an Advisory Partner at EMH Partners. Previously, he was the first CEO of NEOM, where he remains on the board and advises the Kingdom of Saudi Arabia on economic development. Earlier in his career, Dr. Kleinfeld was Chairman and CEO of Alcoa/Arconic, leading the company through a major transformation and successful split, and spent two decades at Siemens, ultimately becoming CEO of Siemens AG. He has also served on numerous global boards and advisory councils, including the Brookings Institution, Council on Foreign Relations, and World Economic Forum, and advised U.S. Presidents and international leaders. Born in Bremen, Germany, he holds an MBA from the University of Göttingen, a PhD from the University of Würzburg, and dual U.S.-German citizenship.In the episode, Richie and Klaus explore the causes of workplace burnout, the parallels between high-performing workers and athletes, the importance of managing energy and purpose, practical techniques for emotional and mental control, the role of downtime in productivity, and strategies for creating a supportive work culture, and much more.Links Mentioned in the Show:Klaus’ Book - Leading to ThriveConnect with KlausCourse: Understanding Prompt EngineeringRelated Episode: Becoming Remarkable with Guy Kawasaki, Author and Chief Evangelist at CanvaRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Welcome to DataFramed Industry Roundups! In this series of episodes, we sit down to discuss the latest and greatest in data & AI. In this episode, with special guest, DataCamp Editor Alex, we touch upon the launch of GPT-5, scaling limits in AI, Meta’s leaked chatbot guidelines, trust in AI tools from the Stack Overflow survey, why OpenAI and Anthropic are giving models away to the US government, AI safety concerns around reasoning, and much more.Links Mentioned in the Show:GPT-5 Is an Evolution, Not a RevolutionMeta’s AI rules have let bots hold ‘sensual’ chats with kids, offer false medical infoAI | 2025 Stack Overflow Developer SurveyOpenAI, Anthropic, both giving AI to federal workers for $1/agencyNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Every day, knowledge workers face the challenge of managing competing priorities and constant interruptions. When systems are managing us rather than us managing them, productivity suffers and morale plummets. But what if the key to improvement isn't complex reorganization but rather understanding how work actually flows through your team or organization? How can visualizing your workflow and regulating for flow transform productivity? What small, incremental changes might lead to dramatic improvements in both output and job satisfaction?Nelson P. Repenning is the Faculty Director of the MIT Leadership Center and the School of Management Distinguished Professor of System Dynamics and Organization Studies at the MIT Sloan School of Management. His early work focused on understanding the inability of organizations to leverage well-established tools and practices. He has worked extensively with organizations trying to develop new capabilities in both manufacturing and new product development. Nelson has also studied the failure to use the safety practices that often lead to industrial accidents and has helped investigate several major incidents. This line of research has been recognized with several awards, including best paper recognition from both the California Management Review and the Journal of Product Innovation Management. Building on his earlier work, Nelson now focuses on developing the theory and practice of Dynamic Work Design—a new approach to designing work that is both effective and engaging—and Dynamic Management Systems, a method for ensuring that day-to-day work is tightly linked to the strategic objectives of the firm. His book (co-authored with Don Kieffer) There Has Got to Be a Better Way describing Dynamic Work Design will be published by Public Affairs in 2025. He is also a partner at ShiftGear Work Design and serves as its chief social scientist. In 2003, Nelson received the International System Dynamics Society’s Jay Wright Forrester Award, which recognizes the best work in the field in the previous five years. In 2011 he received the Jamieson Prize for Excellence in Teaching. He was recently recognized by Poets and Quants as one of the country's top instructors in executive education.Donald Kieffer is a Senior Lecturer in Operations Management at MIT Sloan.He is a career operations executive and co-creator of Dynamic Work Design. Kieffer started working running equipment in factories at age 17. He was VP of operational excellence at Harley-Davidson where he worked for 15 years. Since 2007, he has been advising executive teams around the globe in a range of areas including strategy deployment, product development, and operational improvement. Don has worked with industries as diverse as oil/gas, medical, biomedical, and banking. His guidance was instrumental in transforming both the production and technical development areas of a Cambridge-based genomic sequencing organization, now an industry leader, using the techniques of Dynamic Work Design. He is founder of ShiftGear Work Design, LLC and also teaches Operations Management at AVT in Copenhagen.In the episode, Richie, Nelson and Don explore the challenges of daily firefighting at work, the principles of dynamic work design, how to improve productivity by addressing real problems, the role of AI in business, the importance of setting clear priorities, and much more.Links Mentioned in the Show:Nelson &amp; Don’s Book - There's Got to Be a Better Way: How to Deliver Results and Get Rid of the Stuff That Gets in the Way of Real WorkConnect with Nelson &amp; Don<a...

Combining LLMs with enterprise knowledge bases is creating powerful new agents that can transform business operations. These systems are dramatically improving on traditional chatbots by understanding context, following conversations naturally, and accessing up-to-date information. But how do you effectively manage the knowledge that powers these agents? What governance structures need to be in place before deployment? And as we look toward a future with physical AI and robotics, what fundamental computing challenges must we solve to ensure these technologies enhance rather than complicate our lives?Jun Qian is an accomplished technology leader with extensive experience in artificial intelligence and machine learning. Currently serving as Vice President of Generative AI Services at Oracle since May 2020, Jun founded and leads the Engineering and Science group, focusing on the creation and enhancement of Generative AI services and AI Agents. Previously held roles include Vice President of AI Science and Development at Oracle, Head of AI and Machine Learning at Sift, and Principal Group Engineering Manager at Microsoft, where Jun co-founded Microsoft Power Virtual Agents. Jun's career also includes significant contributions as the Founding Manager of Amazon Machine Learning at AWS and as a Principal Investigator at Verizon.In the episode, Richie and Jun explore the evolution of AI agents, the unique features of ChatGPT, the challenges and advancements in chatbot technology, the importance of data management and security in AI, and the future of AI in computing and robotics, and much more.Links Mentioned in the Show:OracleConnect with JunCourse: Introduction to AI AgentsJun at DataCamp RADARRelated Episode: A Framework for GenAI App and Agent Development with Jerry Liu, CEO at LlamaIndexRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The relationship between AI and data professionals is evolving rapidly, creating both opportunities and challenges. As companies embrace AI-first strategies and experiment with AI agents, the skills needed to thrive in data roles are fundamentally changing. Is coding knowledge still essential when AI can generate code for you? How important is domain expertise when automated tools can handle technical tasks? With data engineering and analytics engineering gaining prominence, the focus is shifting toward ensuring data quality and building reliable pipelines. But where does the human fit in this increasingly automated landscape, and how can you position yourself to thrive amid these transformations?Megan Bowers is Senior Content Manager, Digital Customer Success at Alteryx, where she develops resources for the Maveryx Community. She writes technical blogs and hosts the Alter Everything podcast, spotlighting best practices from data professionals across the industry.Before joining Alteryx, Megan worked as a data analyst at Stanley Black &amp; Decker, where she led ETL and dashboarding projects and trained teams on Alteryx and Power BI. Her transition into data began after earning a degree in Industrial Engineering and completing a data science bootcamp. Today, she focuses on creating accessible, high-impact content that helps data practitioners grow. Her favorite topics include switching career paths after college, building a professional brand on LinkedIn, writing technical blogs people actually want to read, and best practices in Alteryx, data visualization, and data storytelling.Presented by Alteryx, Alter Everything serves as a podcast dedicated to the culture of data science and analytics, showcasing insights from industry specialists. Covering a range of subjects from the use of machine learning to various analytics career trajectories, and all that lies between, Alter Everything stands as a celebration of the critical role of data literacy in a data-driven world.In the episode, Richie and Megan explore the impact of AI on job functions, the rise of AI agents in business, and the importance of domain knowledge and process analytics in data roles. They also discuss strategies for staying updated in the fast-paced world of AI and data science, and much more.Links Mentioned in the Show:Alter EverythingConnect with MeganSkill Track: Alteryx FundamentalsRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Data science continues to evolve in the age of AI, but is it still the 'sexiest job of the 21st century'? While generative AI has transformed the landscape, it hasn't replaced data scientists—instead, it's created more demand for their skills. Data professionals now incorporate AI into their workflows to boost efficiency, analyze data faster, and communicate insights more effectively. But with these technological advances come questions: How should you adapt your skills to stay relevant? What's the right balance between traditional data science techniques and new AI capabilities? And as roles like analytics engineer and machine learning engineer emerge, how do you position yourself for success in this rapidly changing field?Dawn Choo is the Co-Founder of Interview Master, a platform designed to streamline technical interview preparation. With a foundation in data science, financial analysis, and product strategy, she brings a cross-disciplinary lens to building data-driven tools that improve hiring outcomes. Her career spans roles at leading tech firms, including ClassDojo, Patreon, and Instagram, where she delivered insights to support product development and user engagement.Earlier, Dawn held analytical and engineering positions at Amazon and Bank of America, focusing on business intelligence, financial modeling, and risk analysis. She began her career at Facebook as a marketing analyst and continues to be a visible figure in the data science community—offering practical guidance to job seekers navigating technical interviews and career transitions.In the episode, Richie and Dawn explore the evolving role of data scientists in the age of AI, the impact of generative AI on workflows, the importance of foundational skills, and the nuances of the hiring process in data science. They also discuss the integration of AI in products and the future of personalized AI models, and much more.Links Mentioned in the Show:Interview MasterConnect with DawnDawn’s Newsletter: Ask Data DawnGet Certified: AI Engineer for Data Scientists Associate CertificationRelated Episode: How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO &amp; Founder at Azurelib AcademyRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The structured data that powers business decisions is more complex than the sequences processed by traditional AI models. Enterprise databases with their interconnected tables of customers, products, and transactions form intricate graphs that contain valuable predictive signals. But how can we effectively extract insights from these complex relationships without extensive manual feature engineering?Graph transformers are revolutionizing this space by treating databases as networks and learning directly from raw data. What if you could build models in hours instead of months while achieving better accuracy? How might this technology change the role of data scientists, allowing them to focus on business impact rather than data preparation? Could this be the missing piece that brings the AI revolution to predictive modeling?Jure Leskovec is a Professor of Computer Science at Stanford University, where he is affiliated with the Stanford AI Lab, the Machine Learning Group, and the Center for Research on Foundation Models.Previously, he served as Chief Scientist at Pinterest and held a research role at the Chan Zuckerberg Biohub. He is also a co-founder of Kumo.AI, a machine learning startup. Leskovec has contributed significantly to the development of Graph Neural Networks and co-authored PyG, a widely-used library in the field. Research from his lab has supported public health efforts during the COVID-19 pandemic and informed product development at companies including Facebook, Pinterest, Uber, YouTube, and Amazon.His work has received several recognitions, including the Microsoft Research Faculty Fellowship (2011), the Okawa Research Award (2012), the Alfred P. Sloan Fellowship (2012), the Lagrange Prize (2015), and the ICDM Research Contributions Award (2019). His research spans social networks, machine learning, data mining, and computational biomedicine, with a focus on drug discovery. He has received 12 best paper awards and five 10-year Test of Time awards at leading academic conferences.In the episode, Richie and Jure explore the need for a foundation model for enterprise data, the limitations of current AI models in predictive tasks, the potential of graph transformers for business data, and the transformative impact of relational foundation models on machine learning workflows, and much more.Links Mentioned in the Show:Jure’s PublicationsKumo AIConnect with JureCourse - Transformer Models with PyTorchRelated Episode: High Performance Generative AI Applications with Ram Sriharsha, CTO at PineconeRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business