What happens when a technology starts to look, sound, and reason like us?Taylor Black, Director of the AI Ventures Ecosystem in the CTO’s Office at Microsoft, has spent his career at the intersection of data, venture building, and emerging technology. From bootstrapping a SaaS company to helping shape early stage AI innovation inside one of the world’s largest technology organizations, Taylor brings a rare long term perspective on what AI is, and what it is not.In this conversation, we explore how today’s AI moment compares to past technological revolutions, why most current AI applications are still low hanging fruit, and where the real economic and organizational disruption is likely to come from next.We discuss the limits of automation, the rise of agentic systems, the moral responsibility of those building AI, and why the hardest work ahead is not technical, but cultural.This is a wide ranging discussion on AI, human work, governance, and the difference between making hard things easier versus making impossible things merely difficult.Connect with us:Website: https://thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcasts: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xaYouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters00:00 – Introducing Taylor Black and his role at Microsoft04:00 – From bootstrapped SaaS founder to data driven decision making10:30 – Learning to translate technical insight into business value16:30 – Venture studios, zero to one work, and entering Microsoft23:30 – How ChatGPT changed perception inside large organizations28:00 – The steam engine analogy and why infrastructure matters33:30 – AI, work, and why modern jobs already felt broken40:00 – Presence, remote work, and the limits of digital collaboration46:00 – AI optimism, AI fear, and misunderstanding the technology52:00 – What large language models actually are and are not58:30 – Governance, moral responsibility, and who shapes AI1:05:30 – Agentic systems, job disruption, and what comes next

In this episode of The Data Storytellers Podcast, we speak with Albert Mangahas, a data and analytics leader now at Roo. Albert shares how his path from industrial engineering and SQL led him into data leadership roles across PwC, Green Dot, Facebook, Turo, and now Roo.We explore how trust in data became a foundation of Albert’s career, how marketplaces like Turo balance supply, demand, safety, and user experience, and what it takes to scale data teams through rapid growth and crisis. Albert also discusses his time at Facebook, how Turo navigated the pandemic, and why the real opportunity with AI may be less about replacing people and more about scaling better business insight.Connect with us:Website: https://thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xaYouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00: Introduction and Albert’s first time in Vancouver01:50: From computer science to industrial engineering03:50: The SQL class that sparked Albert’s interest in data06:04: PwC, auditing, and building trust in data13:36: Moving into retail fintech at Green Dot17:28: Mobile banking, underbanked customers, and Instant Pay20:11: From BI reporting to analytics, finance, and FP&A21:57: Joining Turo and taking on analytics, FP&A, and fundraising26:43: The Turo business model and the case for peer-to-peer car sharing34:39: How two-sided marketplaces balance supply and demand40:14: Trust, safety, insurance, and managing marketplace risk45:53: Facebook, Cambridge Analytica, and data transparency48:42: Leadership, influence, and measuring complex platform problems57:46: Why Albert returned to Turo58:57: COVID, travel shutdowns, and wartime data leadership1:06:15: Profitability, supply growth, and marketplace resilience1:09:07: AI, trust, and the future of business decision-making

In this episode of The Data Storytellers Podcast, we speak with Dejan Mitkovski, a data and technology leader whose career has spanned Microsoft, Amazon, Google, FanDuel, and BetMGM. Dejan reflects on the last 25 years of digital transformation, from the early days of relational databases and BI tools to the rise of big data, data science, machine learning, and AI.We explore how data leadership has evolved from reporting to prediction, why legacy organizations struggle to adopt new technologies, and how leaders can prove value through small, practical tests. Dejan also discusses the difference between traditional machine learning and AI, the importance of data quality and governance, and why curiosity, discomfort, and people leadership remain central to building successful data organizations.Connect with us:Website: https://thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xaYouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00: Introduction and Dejan’s return to Vancouver03:43: The 25-year arc of data, digital transformation, and AI04:26: Early influences, science, and choosing a different career path09:40: First exposure to analytics and the move into Microsoft12:20: Relational databases, Hadoop, and the early digital era16:07: Stitching data together, personalization, and the rise of BI20:38: Big data, data science, and the shift from reporting to prediction29:35: Amazon, mentorship, forecasting, and thinking like an owner37:02: Google, legacy industries, and the challenge of changing how companies work42:35: Proving value through small tests and cultural adoption50:45: Machine learning, measurement, and what makes AI different59:41: FanDuel, data products, governance, and scaling a data organization1:10:51: ChatGPT, generative AI, and the limits of messy data1:18:18: BetMGM, AI enablement, and lessons for data leaders1:21:58: Curiosity, discomfort, and the human side of leadership

What separates analytics teams that get a seat at the table from those stuck in the reporting queue?Anthony Jackel, Senior Director of Business Intelligence & Analytics at Ferrara, has spent his career answering that question, from Kraft to one of America's largest candy companies.In this conversation, we explore:What "data-driven enterprise" actually meansThe shift from data providers to decision enablersWhy user-centered design matters as much for dashboards as it does for iPhonesHow to build analytics products that drive action, not just curiosityThe mindset shift that earns analytics a seat at the tableConnect with us:Website: https://thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xaYouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00 – Introduction to Anthony Jackel and his role at Ferrara03:35 – Building Ferrara’s analytics team from scratch07:42 – The problem with dashboards and low-value data work11:50 – Connecting analytics to business decisions and revenue16:25 – The importance of commercial empathy for data leaders20:18 – Getting out of the service provider mindset25:04 – Translating technical insights for non-technical audiences28:45 – Why building trust is more important than being right33:29 – Lessons from finance that shaped his analytics approach37:12 – Coaching and developing high-performing analytics talent41:05 – The challenge of balancing curiosity with execution45:18 – Internal marketing and the power of repeatable wins49:56 – Final advice for future analytics leaders📍 Chapter Timestamps (Finalized for 50:13 runtime)

In this episode of The Data Storytellers Podcast, we sit down with Ylan Kazi, Chief Data & AI Officer at Blue Cross Blue Shield of North Dakota, to cut through the noise around enterprise AI. Ylan explains why early machine learning hype never matched operational reality, how LLMs changed the game once they became accessible to anyone, and why most AI failures stem from cultural friction rather than technical limitations. He breaks down the gap between expectations and actual ROI, the overlooked complexity behind agentic AI, and the fundamental difference between inserting AI into old workflows and redesigning processes to be AI native.We also explore the economic and societal contours of the current AI cycle, including energy constraints, the cultural backlash against AI generated content, and why exponential progress looks slow until it doesn’t. Ylan shares what the coming micro bubbles might look like, how labor markets are shifting, and why new technology forces each of us to examine the meaning of human work. We end with a look at his AI Edge newsletter and what he is tracking as this transformation accelerates.Chapters:00:00 Introductions and Ylan’s early AI skepticism04:30 Traditional AI vs generative AI and why prompting skill matters08:00 Why enterprise ROI lags and why most failures are cultural12:00 The economics of LLMs, energy constraints, and infrastructure realities18:00 Agentic AI, undocumented processes, and why value requires redesign25:00 Automation, talent pipelines, and shifts in labor markets34:00 Cultural reactions to AI content and the value of human creation45:00 The case for micro bubbles and faster boom bust cycles57:00 Ylan’s newsletter, what he is writing about, and closing thoughts

In this episode of The Data Storytellers Podcast, we speak with Jodi Blomberg, VP of AI and Machine Learning at Cox Automotive. Jodi shares her experience leading AI transformation at scale in a highly regulated, legacy-rich environment.We explore how to balance long-term R&D with immediate impact, why trust and humility are critical in AI leadership, and how to move from proofs of concept to deployed products that make a real difference. Jodi also discusses the cultural and operational shifts required to truly embed AI in the enterprise and her own career journey from econometrics to machine learning.Connect with us:Website: https://thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xaYouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00 – Introduction and Jodi Blomberg’s role at Cox Automotive02:44 – The mission of the AI and Machine Learning team06:12 – Where AI is driving value in automotive ecosystems10:25 – Lessons learned leading data teams in legacy orgs14:01 – Balancing long-term R&D with business impact18:17 – Why successful AI teams don’t chase shiny objects22:39 – The cultural change required for enterprise AI26:15 – From proof of concept to production in a regulated space30:34 – Jodi’s career path from econometrics to applied AI35:48 – The role of humility and empathy in AI leadership40:09 – Bringing executive stakeholders on the journey

In this episode of The Data Storytellers Podcast, we speak with Elena Alikhachkina, Chief Data and AI Officer at TE Connectivity. With a background spanning Pfizer, Johnson & Johnson, Nestlé, and now one of the world’s largest industrial tech companies, Elena shares what it takes to lead AI transformation across global, complex organizations.We talk about the realities of operationalizing data and AI in manufacturing, the soft skills required to drive trust and adoption, and how to move from use cases to enterprise value. Elena also reflects on how she built her career across industries, why curiosity is her superpower, and how data leaders can move from technical execution to strategic leadership.Connect with us:Website: https://thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xaYouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00 – Introduction and Elena’s current role at TE Connectivity02:41 – Why AI transformation is a leadership challenge, not a tech one06:50 – How to build trust across functions when driving change10:22 – The power of storytelling in operational environments14:36 – Creating a data strategy that scales globally18:58 – Why speed alone is not a data strategy23:11 – Aligning AI with core business priorities27:04 – Moving from isolated use cases to enterprise-wide value31:17 – Lessons from Nestlé and Johnson & Johnson36:03 – Defining value clearly in industrial use cases40:40 – The evolution of the Chief Data and AI Officer role44:12 – Building multidisciplinary teams that earn business trust49:18 – Advice for data leaders entering legacy-heavy environments53:46 – Why curiosity, empathy, and resilience matter most58:03 – How Elena built her career across industries and functions1:03:40 – What’s next in data and AI at TE Connectivity1:08:12 – Closing thoughts on leadership, impact, and lifelong learning

In this episode of The Data Storytellers Podcast, we speak with Rangan Gangavaram, Senior Director of Analytics & Insights at Verizon, about the promise and pitfalls of agentic AI. Rangan shares why agentic AI is more than just another automation wave, how it differs from RPA, and why its reasoning capabilities could redefine customer interactions.We discuss the decision frameworks enterprises need to adopt, the importance of stakeholder-driven adoption, and how AI transformations resemble the digital era more than traditional data initiatives. Rangan also reflects on his career journey from engineering to analytics leadership and the pivotal role of storytelling in driving AI adoption.Connect with us:Website: https://thedatastorytellers.com/LinkedIn: https://www.linkedin.com/company/the-data-storytellersApple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xaYouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00 – Introduction and reconnecting after past conversations01:15 – Why agentic AI is the real deal for enterprises04:32 – Comparing agentic AI to RPA and automation07:55 – The multiplier effect of AI-driven reasoning12:41 – Do today’s AI models really reason?17:14 – Understanding the driver behind customer problems19:03 – Key risks and pitfalls in deploying agentic AI25:12 – Why stakeholder buy-in is essential for adoption28:40 – AI strategy as part of a holistic transformation32:58 – How AI transformation mirrors the digital revolution36:46 – Managing cost concerns for AI at scale40:33 – Rangan’s career path into data and analytics46:12 – The importance of storytelling in analytics leadership52:10 – Advice for aspiring professionals in data and AI54:40 – Closing reflections and future collaboration

In this episode of The Data Storytellers Podcast, we speak with Nurtekin Savas, VP and Head of Global Credit Infrastructure, Data & AI at PayPal. Nurtekin shares how PayPal balances innovation with responsibility as it scales global credit products powered by data and AI.From structuring modern analytics platforms to influencing stakeholders and fostering experimentation in regulated environments, Nurtekin offers actionable insights for data leaders navigating fintech complexity. He also reflects on the value of storytelling, leadership trust, and aligning infrastructure with business goals at scale.Connect with us:• Website: https://thedatastorytellers.com/• LinkedIn: https://www.linkedin.com/company/the-data-storytellers• Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476• Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa• YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00 – Intro: Meet Nurtekin Savas, PayPal’s Head of Global Credit Infrastructure, Data & AI04:52 – What Nurtekin’s team owns across data, platforms, and credit09:45 – The connective tissue between data, infrastructure, and product14:38 – From AOL to PayPal: a 20+ year fintech journey19:31 – Why compliance and experimentation must coexist24:24 – Building the muscle for responsible innovation29:17 – Creating space to learn and fail in a high-stakes environment34:10 – Platform architecture: building what’s needed vs. shiny objects39:03 – Gaining buy-in from senior leadership on infrastructure investments43:56 – The importance of storytelling in analytics and AI48:49 – Decision rights, influence, and organizational trust53:42 – Growing talent: what makes a great data leader58:35 – Lessons from past roles: Visa, Amazon, Capital One, Fidelity63:28 – Building systems that scale responsibly across borders68:21 – Trends Nurtekin is watching in AI and credit innovation73:14 – Final thoughts on leadership, curiosity, and execution78:07 – Wrap-up and closing insights

On this episode of The Data Storytellers Podcast, we speak with Mikel Davis, VP of Strategic Analytics at Peacock. Mikel shares her journey from early analytics roles in sports and entertainment—including the Cleveland Cavaliers and Legendary Pictures—to leading customer and growth strategies at one of the world’s top streaming platforms.We explore how data is transforming marketing, content, and customer experience across industries, and the leadership skills that help drive real cultural change in large organizations.Connect With Us:• Website: https://thedatastorytellers.com/• LinkedIn: https://www.linkedin.com/company/the-data-storytellers• Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476• Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa• YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmAChapters:00:00 – Introduction00:36 – The rise of AI: déjà vu from the early data days02:15 – From data literacy to AI fluency: is it just rebranding?04:52 – Laszlo’s path from military intel to analytics through marketing06:21 – Mikel’s current role at Peacock and team’s mandate07:33 – How Mikel accidentally discovered analytics in college09:30 – Economics + statistics = the original analytics degree11:03 – From Accenture to the Cleveland Cavaliers13:15 – Selling tickets with data: pre-LeBron vs. post-LeBron15:02 – Pandora’s predictive personalization and data obsession17:35 – Streaming disruption: what seemed crazy is now normal18:24 – How LeBron changed Mikel’s job and career path21:43 – Moving to film: data innovation at Legendary Entertainment23:55 – F1 Movie example: modernizing film marketing with data27:56 – Using data to greenlight and cast films more strategically29:52 – Balancing art and science in content creation31:29 – Transition to AT&T: data for customer retention33:33 – The big challenge: keeping cable customers in the streaming era34:02 – The streaming boom and Netflix as a data pioneer35:33 – Joining NBCUniversal: centralizing customer data36:59 – Launching Peacock and driving strategic growth37:46 – Mikel’s career playbook: building data-driven cultures38:15 – Adapting insights to executives vs. operations40:24 – Building trust through stakeholder empathy and flexibility42:24 – The power of asking good questions and connecting people43:46 – The #1 blocker: when success without data breeds resistance44:56 – AI parallels: augmenting, not replacing, human judgment46:02 – Closing thoughts