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DataCamp

DataFramed

AI moves fast, and the news cycle can feel like a fire hose. New tools like agents and digital twins promise to help, but they also add more choices and noise. In day-to-day work, the challenge is less about knowing every breakthrough and more about deciding what matters, then making time to act. How do you cut meetings down, say no without friction, and still ship real work? How do you open your mind to new ideas while avoiding hype? And when you do spot a signal, how do you turn it into action across teams, stakeholders, and shifting priorities.

As the Head of Global Innovation at ServiceNow, Brian Solis drives vision and strategy for future-focused innovation. He has three decades of experience as a technology leader, and Forbes called him "one of the more creative and brilliant business minds of our time". Previously, Brian was VP of Global Innovation at Salesforce. He has written nine books, including the best selling "Mindshift". Brian is an author of the ServiceNow Enterprise AI Maturity Index 2025 Report.

In the episode, Richie and Brian explore the challenges of staying updated with AI advancements, the importance of mindset shifts for innovation, the role of storytelling in driving change, and practical strategies for managing information overload, fostering organizational transformation, and much more.

Links Mentioned in the Show:

Brian’s Book: MindshiftServiceNowConnect with BrianAI-Native Course: Intro to AI for WorkRelated Episode: The New Paradigm for Enterprise AI Governance with Blake Brannon, Chief Innovation Officer at OneTrustExplore AI-Native Learning on DataCamp

New to DataCamp?

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#345 How to Drive Innovation with Brian Solis, Head of Global Innovation at ServiceNow

AI leaders talk about innovation, but the wider reality is messy: fast change, uneven guardrails, and threats that span cyber, reputation, and customer harm. Industry-wide, organizations are shifting from one-off compliance to lifecycle governance—from inception to decommissioning—supported by boards, CEOs, and frontline teams. For professionals, that shows up as coordination work: shared metrics, incentives for responsible delivery, embedded ethics partners, and rapid-response groups when a new risk appears. How do you decide who is accountable for model behavior? What signals should trigger escalation? And what sources can you trust to stay informed without getting overwhelmed?

Andrea Bonime-Blanc, JD/PhD, is founder and CEO of GEC Risk Advisory, a board member, strategic advisor, and award-winning author. She specializes in the governance of change, advising companies, NGOs, and governments on global strategic risk, leadership trust, geopolitics, sustainability, cyber resilience, and exponential technologies. A former C-suite executive at four global companies, including Bertelsmann and PSEG, she has held roles spanning legal, risk, ethics, sustainability, and cybersecurity, and currently serves on multiple boards and advisory boards.

Andrea is a Senior Fellow at The Conference Board, NYU’s Center for Global Affairs, and an AI Ethics Strategy Fellow at the American College for Financial Services. She is a sought-after keynote speaker and media commentator, appearing in outlets such as Bloomberg, the Financial Times, and The New York Times. She is the author of several books, including Gloom to Boom and most recently, Governing Pandora: Leading in the Age of Generative AI and Exponential Technology.

In the episode, Richie and Andrea explore the rapid advancements in AI, the balance between innovation and risk, the importance of adaptive governance, the role of leadership in tech governance, and the integration of ethics in AI development, and much more.

Links Mentioned in the Show:

Andrea’s Book—Governing Pandora: Leading in the Age of Generative AI and Exponential TechnologyMIT AI Risk Repository 1. Connect with 
AndreaAI-Native Course: Intro to AI for WorkRelated Episode: Rebuilding Trust in the Digital Age with Jimmy Wales, Founder at WikipediaExplore 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

#344 Governing Pandora's Box: Managing AI Risks with Andrea Bonime-Blanc, CEO at GEC Risk Advisory

Data and AI teams are drowning in tools, but the big trend is consolidation and speed. AI-driven building is making dashboards, internal apps, and even data workflows feel more like products than reports. Custom interfaces, interactive presentations, and ad hoc apps are becoming easier to create than traditional BI artifacts.

For working professionals, this raises practical questions: should you build a bespoke reporting site instead of another spreadsheet? Can you connect secure data views and prevent leaks by design? What does quality control look like when an agent writes the code—separate chats, clear plans, and tests? And what’s the real cost of going from idea to deployed app: a few dollars, or hundreds?

Matt Palmer works at the intersection of developer experience, product marketing, and AI education. Leading Developer Relations at Replit, he helped grow Replit's revenue from $5M to $100M+. He creates content on vibe-coding, data transformation, AI, and more—blending technical depth with accessibility to empower developers and make complex tools approachable.

In the episode, Richie and Matt explore the power of vibe coding, how non-developers are building impactful tools, the potential of AI in app development, the role of Replit in simplifying coding, and the future of personalized applications in data teams, and much more.

Links Mentioned in the Show:

ReplitCourse: Vibe Coding with ReplitYour Guide to Replit 1. Connect with 
MattAI-Native Course: Intro to AI for WorkRelated Episode: Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpotExplore 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

#343 Vibe Coding and the Rise of the Non-Developer Builder with Matt Palmer, Developer Relations at Replit

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:

Wharton School 1. Connect with 
StefanoMIT Report—The GenAI Divide: State of AI in Business 2025Wharton Report—Gen AI Fast-Tracks Into the EnterpriseAI-Native Course: Intro to AI for WorkRelated Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at TricentisExplore 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

#342 The Secrets to High AI Adoption with Stefano Puntoni, Professor at Wharton

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:

Blog: The Junior Hiring CrisisBlog: The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchantsAlex Banks on the ChatGPT era endingSpec & Evals Driven Agent Development (SEDAD) TemplateAI-Native Course: Intro to AI for WorkRelated Episode: Reviewing Our Data Trends & Predictions of 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn TheuwissenExplore 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

#341 Our Data Trends & Predictions of 2026 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen

﻿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:

The DataCamp Data & AI Literacy Report 2025AI-Native Course: Intro to AI for WorkRelated Episode: Data Trends & Predictions 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn TheuwissenExplore 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

#340 Reviewing Our Data Trends & Predictions of 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen

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:

Sigma 1. Connect with 
MikeCourse: Introduction to SigmaAI-Native Course: Intro to AI for WorkRelated Episode: Self-Service Generative AI Product Development at Credit Karma with Madelaine Daianu, Head of Data & AI at Credit KarmaExplore 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

#339 Modern Analytics with Mike Palmer, CEO at Sigma

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:

OneTrust 1. Connect with 
BlakeAI-Native Course: Intro to AI for WorkRelated Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAIExplore 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

#338 The New Paradigm for Enterprise AI Governance with Blake Brannon, Chief Innovation Officer at OneTrust

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

#337 DataFramed, Distilled. The Best Moments of 2025 with Richie Cotton

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:

ArtifexAIRuss’ Website • Connect with 
RussAI-Native Course: Intro to AI for WorkRelated Episode: Harnessing AI to Help Humanity with Sandy Pentland, HAI Fellow at StanfordRewatch RADAR AI • 

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

#336 From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with this role often including software engineering tasks. In particular, one of the key functions of a full stack data scientist is to take machine learning models and get them into production inside software. So, what separates projects from production?

Savin Goyal is the Co-Founder & CTO at Outerbounds. In addition to his work at Outerbounds, Savin is the creator of the open source machine learning management platform Metaflow. Previously Savin has worked as a Software Engineer at Netflix and LinkedIn.

In the episode, Richie and Savin explore the definition of production in data science, steps to move from internal projects to production, the lifecycle of a machine learning project, success stories in data science, challenges in quality control, Metaflow, scalability and robustness in production, AI and MLOps, advice for organizations and much more. 

Links Mentioned in the Show:

OuterboundsMetaflowConnect with Savin on Linkedin[Course] Developing Machine Learning Models for Production • Related Episode: 
Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling AuthorRewatch sessions from RADAR: AI Edition

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

#225 The Full Stack Data Scientist with Savin Goyal, Co-Founder & CTO at Outerbounds

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