AI tools are becoming part of daily work for more professionals than ever before, yet adoption rates vary significantly across functions and company sizes. What separates organizations that successfully integrate AI from those that struggle? How do psychological factors like identity and autonomy shape how workers respond to AI implementation? And what role does corporate culture play in determining whether AI becomes a source of innovation or a point of resistance?

Stefano Puntoni is the Sebastian S. Kresge Professor of Marketing at The Wharton School. Prior to joining Penn, Stefano was a professor of marketing and head of department at the Rotterdam School of Management, Erasmus University, in the Netherlands. He holds a PhD in marketing from London Business School and a degree in Statistics and Economics from the University of Padova, in his native Italy.

His research has appeared in several leading journals, including Journal of Consumer Research, Journal of Marketing Research, Journal of Marketing, Nature Human Behavior, and Management Science. He also writes regularly for managerial outlets such as Harvard Business Review and MIT Sloan Management Review. Most of his ongoing research investigates how new technology is changing consumption and society, including how humans are adopting and evolving with AI.

He is a former MSI Young Scholar and MSI Scholar, and the winner of several grants and awards. He is currently an Associate Editor at the Journal of Consumer Research and at the Journal of Marketing. Stefano teaches in the areas of marketing strategy, new technologies, brand management, and decision making.

In the episode, Richie and Stefano explore the challenges of AI adoption in businesses, the psychological impacts on workers, the balance between human expertise and AI, the potential mental health effects of AI chatbots, and much more.

Links Mentioned in the Show:

1. Wharton School
2. Connect with Stefano
3. MIT Report—The GenAI Divide: State of AI in Business 2025
4. Wharton Report—Gen AI Fast-Tracks Into the Enterprise
5. AI-Native Course: Intro to AI for Work
6. Related Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis
7. Explore AI-Native Learning on DataCamp

New to DataCamp?

1. Learn on the go using the DataCamp mobile app
2. Empower your business with world-class data and AI skills with DataCamp for business

2026 is shaping up to be a pivotal year for data, AI, and how we work. From step-change improvements in foundation models to AI-native workflows reshaping careers, commerce, and education, the pace of change shows no signs of slowing. After revisiting and scoring their previous predictions, Richie, Jo, and Martijn turn their focus to what’s coming next in 2026.

Building on last year’s discussion, we explore how AI will transform hiring and career progression, why personal AI tutors could become the default learning experience, how AI agents may begin executing real economic activity, and whether we’re on the brink of another “GPT-3 moment” driven by new hardware and scaling.

Links Mentioned in the Show:

1. Blog: The Junior Hiring Crisis
2. Blog: The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchants
3. Alex Banks on the ChatGPT era ending
4. Spec & Evals Driven Agent Development (SEDAD) Template
5. AI-Native Course: Intro to AI for Work
6. Related Episode: Reviewing Our Data Trends & Predictions of 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen
7. Explore AI-Native Learning on DataCamp

New to DataCamp?

1. Learn on the go using the DataCamp mobile app
2. Empower your business with world-class data and AI skills with DataCamp for business

2025 was another huge year for data and AI. Generative AI continued to reshape how we work and interact with technology, with organizations moving beyond experimentation and pushing AI firmly into production. We saw major progress in foundation models, the rise of long-running AI agents, production-ready generative video, and wider adoption of synthetic data. At the same time, AI literacy, adoption, and ROI became central concerns for boards and executives, not just technical teams.

This time last year, DataCamp Co-Founders Jonathan and Martijn made a series of predictions about data and AI for 2025. Today, they join Richie to reflect on how those predictions played out—and to share their vision for where data and AI are headed next.

In the episode, Richie, Jonathan, and Martijn review the real-world adoption of generative AI, the shift from hype to production, the growing importance of AI literacy and usage at the executive level, the rise of longer-running AI agents, the near-mainstreaming of generative video, Europe’s position in the global AI race, why educators may be among the biggest AI adopters, and why AI hype continues to thrive—plus what they got right, what they got wrong, and what comes next.

Links Mentioned in the Show:

1. The DataCamp Data & AI Literacy Report 2025
2. AI-Native Course: Intro to AI for Work
3. Related Episode: Data Trends & Predictions 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen
4. Explore AI-Native Learning on DataCamp

New to DataCamp?

1. Learn on the go using the DataCamp mobile app
2. Empower your business with world-class data and AI skills with DataCamp for business

Self-service analytics has been a goal for data teams for years, but recent advances in AI are accelerating progress in unexpected ways. The combination of natural language interfaces and spreadsheet-like tools is lowering barriers to data access across organizations. But how do you balance the freedom of self-service with the need for governance and accuracy? What skills do analysts need to work effectively with AI systems that don't always produce the same results twice? And when AI-generated answers might be slightly off, how do you know when to trust them?

Mike Palmer is Chief Executive Officer of Sigma , where he leads the company’s strategy and growth as a cloud-native analytics and business intelligence platform. Since joining Sigma in 2020, he has focused on expanding access to cloud data by enabling business users to analyze data warehouses through familiar, spreadsheet-based workflows. Prior to Sigma, Mike served as Chief Product Officer at Druva, where he was part of the executive team scaling the company’s cloud data management platform and supporting rapid revenue growth. Before that, he was EVP and Chief Product Officer at Veritas Technologies, leading the transformation and modernization of a large enterprise data protection portfolio following its separation from Symantec. Earlier in his career, he held senior general management and executive roles at Seagate Technology and Verizon Enterprise Solutions, overseeing large-scale cloud, security, and enterprise infrastructure businesses. Mike is based in San Francisco and has spent his career building and operating enterprise data and analytics platforms at scale.

In the episode, Richie and Mike explore the journey towards self-service analytics, the role of AI in democratizing data access, the challenges of stochastic processes, the evolution of analytics applications, how businesses can leverage AI for personalized insights, the future of enterprise software, and much more.

Links Mentioned in the Show:

1. Sigma
2. Connect with Mike
3. Course: Introduction to Sigma
4. AI-Native Course: Intro to AI for Work
5. Related Episode: Self-Service Generative AI Product Development at Credit Karma with Madelaine Daianu, Head of Data & AI at Credit Karma
6. Explore AI-Native Learning on DataCamp

New to DataCamp?

1. Learn on the go using the DataCamp mobile app
2. Empower your business with world-class data and AI skills with DataCamp for business

AI governance is becoming critical as organizations deploy more intelligent systems across their operations. With predictions of over a billion AI agents entering the workforce in the coming years, traditional governance approaches simply cannot keep pace. How do you ensure your AI systems are using data responsibly without slowing down innovation? What happens when an AI agent makes decisions that were never explicitly programmed? And how do you build governance processes that scale alongside rapidly expanding AI adoption while maintaining trust with customers and regulators?

Blake Brannon is Chief Innovation Officer at OneTrust, where he leads product vision and strategic direction for the company’s AI-ready governance platform. He has been with OneTrust since 2017, previously serving as Chief Technology Officer, and has played a key role in scaling the platform to support privacy, data governance, risk, and responsible AI initiatives for large enterprises. Blake is based in Atlanta and holds an academic background from the Georgia Institute of Technology, with early research experience in network systems and wireless communications.

In the episode, Richie and Blake explore AI governance disasters, the importance of consent and data use, the rise of AI agents, the challenges of scaling governance processes, the need for continuous observability, the role of governance committees, strategies for effective AI governance in organizations, and much more.

Links Mentioned in the Show:

1. OneTrust
2. Connect with Blake
3. AI-Native Course: Intro to AI for Work
4. Related Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI
5. Explore AI-Native Learning on DataCamp

New to DataCamp?

1. Learn on the go using the DataCamp mobile app
2. Empower your business with world-class data and AI skills with DataCamp for business

2025 was the year AI stopped being a curiosity and started reshaping real work. From data analysts speeding up entire workflows in minutes, to managers learning how to lead hybrid teams of humans and agents, the pace of change has been relentless. Across DataFramed this year, one theme kept surfacing: AI isn’t replacing data professionals—it’s raising the bar on what good looks like. Skills are shifting, careers are becoming more fluid, and organizations are being forced to rethink how they build teams, make decisions, and govern technology that now reasons, plans, and acts on our behalf. This Best of 2025 episode pulls together the most important ideas, voices, and debates from a year that fundamentally changed how data and AI show up in practice.

In this special year-end roundup, Richie revisits the standout moments from DataFramed in 2025, spanning careers, business intelligence, data literacy, AI agents, industry use cases, and responsible AI foundations. You’ll hear why the data analyst role is evolving rather than disappearing, how hybrid human–AI teams are becoming the norm, and why communication remains the most underrated skill in data careers, the state of BI and data storytelling, the shift from training to behavior change in data and AI literacy, the rapid rise of agentic systems powered by reasoning at inference time. We also dive into real-world applications across healthcare, finance, and enterprise operations, alongside hard truths about data quality, governance, and model lineage. Finally, we spotlight advances in data science, NLP, and synthetic data—rounding out a year defined by faster cycles, higher expectations, and a renewed focus on getting the fundamentals right as AI scales.

Episodes Featured in this Recap:

• #326 Is the Data Analyst Role Dying Out? with Mo Chen, Data & Analytics Manager at NatWest Group
• #319 Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpot
• #295 How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO & Founder at Azurelib Academy
• #294 Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at Microsoft
• #333 Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at DuoLingo
• #310 The State of BI in 2025 with Howard Dresner, Godfather of BI
• #306 The Next Generation of Business Intelligence with Colin Zima, CEO at Omni
• #298 Data Storytelling Skills to Increase Your Impact with Kat Greenbrook, Author of The Data Storyteller's Handbook
• #323 The Evolution of Data Literacy & AI Literacy with Jordan Morrow, Godfather of Data Literacy
• #305

The concept of sovereign AI is becoming increasingly critical in our interconnected world. Nations and organizations are grappling with who controls the data, infrastructure, and technology that power artificial intelligence systems. But what does this mean for your work in data science and AI implementation? How do you navigate the complex landscape of data ownership when building AI solutions? As geopolitical tensions influence technology development, understanding the nuances of AI sovereignty isn't just for governments—it's essential for anyone working with data and AI systems to ensure resilience and compliance in an uncertain future.

Russ Wilcox is the CEO of ArtifexAI, advising organizations on technology strategy, AI governance, and policy analysis. With 16 years in machine learning and AI, he focuses on translating complex policy and emerging tech trends into actionable strategy. His work spans government, infrastructure, and enterprise, with a focus on connecting technical capabilities to real-world implementation. A two-time World Economic Forum speaker and TEDx presenter, Wilcox has advised government agencies and Fortune 500 companies on AI strategy, urban intelligence, and technology policy. He also serves on AI ethics boards, lectures at UCLA and Boston University, and develops NLP systems for public- and private-sector use. Russ provides strategic consulting and speaking on AI governance, technology competition, and sustainable infrastructure.

In the episode, Richie and Russ explore the US-China AI race, the philosophical differences in AI approaches, the concept of sovereign AI, the role of data sovereignty, and the potential for AI to transform infrastructure and governance, and much more.

Links Mentioned in the Show:

• ArtifexAI
• Russ’ Website
• Connect with Russ
• AI-Native Course: Intro to AI for Work
• Related Episode: Harnessing AI to Help Humanity with Sandy Pentland, HAI Fellow at Stanford
• Rewatch RADAR AI

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• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

The internet has transformed how we access information, but it's also created unprecedented challenges around trust and reliability. How do we build digital spaces where collaboration thrives and quality information prevails? What separates toxic online environments from productive ones? The principles of neutrality, transparency, and assuming good faith have proven essential in creating sustainable knowledge communities. But these same principles extend far beyond the digital realm—they're fundamental to effective leadership, successful business relationships, and even political discourse. When trust breaks down, everything becomes more difficult. So what practical steps can we take to foster trust in our organizations and communities?

Jimmy Wales is an American-British internet entrepreneur best known as the founder of Wikipedia and co-founder of Fandom. Trained in finance at Auburn University and the University of Alabama, he began his career in quantitative finance before moving into early web ventures, including Bomis and the free encyclopedia project Nupedia. In 2001, he launched Wikipedia, which quickly became one of the most visited websites in the world. To support its growth, he established the Wikimedia Foundation in 2003, where he continues to serve on the Board of Trustees and act as a public spokesperson. He later co-founded Fandom in 2004, expanding the wiki model to entertainment, gaming, and niche communities. Wales has also pursued experiments in collaborative journalism, including WikiTribune and its successor WT Social. His work in open knowledge has earned recognition from organizations such as the World Economic Forum, Time magazine, UNESCO, and the Electronic Frontier Foundation. He has held fellowships and board roles at institutions including Harvard’s Berkman Center and Creative Commons.

In the episode, Richie and Jimmy explore the early challenges of Wikipedia, the importance of trust and neutrality, the role of AI in content creation, and much more.

Links Mentioned in the Show:

• Wikipedia
• Jimmy’s New Book: The Seven Rules of Trust
• Trust Café
• Connect with Jimmy
• Blog: The Trust Triangle of Leadership
• AI-Native Course: Intro to AI for Work
• Related Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis
• Explore AI-Native Learning on DataCamp

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business