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-storytellers
Apple Podcasts: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476
Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa
YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA

Chapters

00:00 – Introducing Taylor Black and his role at Microsoft
04:00 – From bootstrapped SaaS founder to data driven decision making
10:30 – Learning to translate technical insight into business value
16:30 – Venture studios, zero to one work, and entering Microsoft
23:30 – How ChatGPT changed perception inside large organizations
28:00 – The steam engine analogy and why infrastructure matters
33:30 – AI, work, and why modern jobs already felt broken
40:00 – Presence, remote work, and the limits of digital collaboration
46:00 – AI optimism, AI fear, and misunderstanding the technology
52:00 – What large language models actually are and are not
58:30 – Governance, moral responsibility, and who shapes AI
1:05:30 – Agentic systems, job disruption, and what comes next

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 means
• The shift from data providers to decision enablers
• Why user-centered design matters as much for dashboards as it does for iPhones
• How to build analytics products that drive action, not just curiosity
• The mindset shift that earns analytics a seat at the table

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/UCz9e56lhYUfORiOHMiLlPmA

Chapters:

00:00 – Introduction to Anthony Jackel and his role at Ferrara
03:35 – Building Ferrara’s analytics team from scratch
07:42 – The problem with dashboards and low-value data work
11:50 – Connecting analytics to business decisions and revenue
16:25 – The importance of commercial empathy for data leaders
20:18 – Getting out of the service provider mindset
25:04 – Translating technical insights for non-technical audiences
28:45 – Why building trust is more important than being right
33:29 – Lessons from finance that shaped his analytics approach
37:12 – Coaching and developing high-performing analytics talent
41:05 – The challenge of balancing curiosity with execution
45:18 – Internal marketing and the power of repeatable wins
49:56 – 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 skepticism
04:30 Traditional AI vs generative AI and why prompting skill matters
08:00 Why enterprise ROI lags and why most failures are cultural
12:00 The economics of LLMs, energy constraints, and infrastructure realities
18:00 Agentic AI, undocumented processes, and why value requires redesign
25:00 Automation, talent pipelines, and shifts in labor markets
34:00 Cultural reactions to AI content and the value of human creation
45:00 The case for micro bubbles and faster boom bust cycles
57:00 Ylan’s newsletter, what he is writing about, and closing thoughts

In this episode of The Data Storytellers Podcast, we sit down with Ron Hetrick, Principal Economist at Lightcast, to unpack the deep structural issues driving the U.S. labor shortage. Ron explains why this is not a short-term blip but a long-term demographic shift that will redefine how companies hire, grow, and operate. From fertility rate collapse and immigration policy to flawed assumptions about productivity, he helps us understand the real economic forces reshaping the talent landscape.

We explore why AI and automation are not the silver bullets many executives hope for, and what leaders should focus on instead. Ron also shares what economists missed about remote work, the dangers of oversimplified labor data, and how companies can rethink workforce strategy in an age of persistent scarcity.

Chapters:

00:00 – Introductions and Ron’s career path from TV to economics
03:12 – What’s really driving the U.S. labor shortage
07:21 – Why this isn’t a temporary workforce dip
11:44 – The demographic math no one is talking about
15:32 – Immigration, fertility, and the myth of the "lazy worker"
20:10 – Why unemployment stats don’t tell the full story
25:33 – The silent shift from job scarcity to talent scarcity
30:48 – What economists missed about remote work and productivity
36:15 – The false hope of automation as a labor solution
41:09 – How companies are overestimating what AI can replace
46:27 – The hard ceiling of productivity without people
51:12 – Forecasting the long-term consequences of talent shortages
56:33 – What future-ready organizations are doing differently
1:01:44 – Redesigning workforce strategy for a shrinking labor pool
1:06:28 – Advice for leaders on adapting to the new labor reality
1:10:13 – Final thoughts on demographic truth vs tech hype

On this episode of The Data Storytellers Podcast, Rich Heimann, author of Doing AI, GenAI Revealed, and the newly released Sutskever's List, joins us for a deep and provocative conversation on why most enterprise AI initiatives fail.

Rich unpacks how internal power structures, belief systems, and corporate incentives shape what AI gets funded, how it's framed, and why so many efforts fall short. We explore the gap between organizational theater and technical progress, and why understanding human systems matters more than mastering the technical stack.

If you're an AI or data leader struggling with implementation, this episode offers a fresh lens that might explain why the problem isn't your model.

Want more? Rich’s new book Sutskever’s List: Foundational Ideas of Modern AI is now available — and you can get it HERE with 45% off.

Chapters:

00:00 – Introductions and Rich’s background
02:44 – The real reasons AI adoption fails in enterprise settings
07:12 – Power, status, and the illusion of AI progress
12:25 – Why many AI initiatives are more about performance than impact
17:31 – Data science vs business theater: who’s really calling the shots?
21:50 – Cultural friction: how belief systems shape technical outcomes
26:43 – Rich on the danger of over-rationalizing human systems
32:17 – Lessons from behavioral economics and AI ethics
37:09 – What leaders pretend to want vs what they actually support
42:03 – Redefining AI success: who gets to decide what matters?
47:58 – Stories vs truth: how narratives become strategy
53:11 – The objectivity myth and why some orgs cling to it
59:04 – LLMs, RAGs, and the current moment of hype
1:04:50 – Why technologists need to understand organizational design
1:09:44 – Advice for AI leaders trying to make change stick
1:14:02 – Final reflections on trust, transformation, and learning

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/UCz9e56lhYUfORiOHMiLlPmA

Chapters:

00:00 – Introduction and Jodi Blomberg’s role at Cox Automotive
02:44 – The mission of the AI and Machine Learning team
06:12 – Where AI is driving value in automotive ecosystems
10:25 – Lessons learned leading data teams in legacy orgs
14:01 – Balancing long-term R&D with business impact
18:17 – Why successful AI teams don’t chase shiny objects
22:39 – The cultural change required for enterprise AI
26:15 – From proof of concept to production in a regulated space
30:34 – Jodi’s career path from econometrics to applied AI
35:48 – The role of humility and empathy in AI leadership
40:09 – 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-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/UCz9e56lhYUfORiOHMiLlPmA

Chapters:

00:00 – Introduction and Elena’s current role at TE Connectivity
02:41 – Why AI transformation is a leadership challenge, not a tech one
06:50 – How to build trust across functions when driving change
10:22 – The power of storytelling in operational environments
14:36 – Creating a data strategy that scales globally
18:58 – Why speed alone is not a data strategy
23:11 – Aligning AI with core business priorities
27:04 – Moving from isolated use cases to enterprise-wide value
31:17 – Lessons from Nestlé and Johnson & Johnson
36:03 – Defining value clearly in industrial use cases
40:40 – The evolution of the Chief Data and AI Officer role
44:12 – Building multidisciplinary teams that earn business trust
49:18 – Advice for data leaders entering legacy-heavy environments
53:46 – Why curiosity, empathy, and resilience matter most
58:03 – How Elena built her career across industries and functions
1:03:40 – What’s next in data and AI at TE Connectivity
1:08:12 – 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 conversations
01:15 – Why agentic AI is the real deal for enterprises
04:32 – Comparing agentic AI to RPA and automation
07:55 – The multiplier effect of AI-driven reasoning
12:41 – Do today’s AI models really reason?
17:14 – Understanding the driver behind customer problems
19:03 – Key risks and pitfalls in deploying agentic AI
25:12 – Why stakeholder buy-in is essential for adoption
28:40 – AI strategy as part of a holistic transformation
32:58 – How AI transformation mirrors the digital revolution
36:46 – Managing cost concerns for AI at scale
40:33 – Rangan’s career path into data and analytics
46:12 – The importance of storytelling in analytics leadership
52:10 – Advice for aspiring professionals in data and AI
54:40 – Closing reflections and future collaboration

In this episode of The Data Storytellers Podcast we speak with Eric Gonzalez, CEO of Omnificity. Eric explains why strong data foundations are more critical than chasing shiny AI solutions and shares how to educate stakeholders without killing their enthusiasm.

We explore how to avoid falling into proof of concept purgatory, how to prioritize initiatives based on impact and feasibility, and how to navigate the organizational egos that often get in the way of real progress. Eric also reflects on his career journey from neuroscience and economics to data leadership and offers actionable advice for building trust and clarity in your organization.

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/UCz9e56lhYUfORiOHMiLlPmA

Chapters:

00:00 – Introduction and Eric’s take on AI’s rapid democratization
01:08 – Why most enterprises are not ready for AI yet
05:30 – How to inspire executives without shutting down excitement
07:26 – Examples of AI replacing boring and monotonous tasks
14:44 – Why companies get stuck in proof of concept purgatory
19:41 – The pitfalls of synthetic data and model collapse
24:16 – The limitations of large language models and what comes next
28:35 – How to approach stakeholders about real business impact
32:57 – Eric’s journey from neuroscience to business intelligence
40:29 – Lessons learned working across industries
44:31 – Why data teams must align information flows with decision flows
55:26 – A prioritization framework for enterprise use cases
1:02:16 – The biggest bottleneck in organizations is ego
1:04:01 – What Dan Gilbert taught about listening to every idea
1:07:53 – Closing thoughts and where to follow Eric’s work

In this episode of The Data Storytellers Podcast, we sit down with Sirisha Nukala, Head of Data Insights & Enterprise Analytics at Louis Vuitton. With a background spanning pharma, consulting, and now luxury retail, Sirisha shares how she builds global analytics capabilities that drive strategic decisions across one of the world’s most iconic brands.

We dive into what it takes to lead a multicultural team, operationalize data across business lines, and turn analytics into a trusted partner for global leadership. She also reflects on building trust, driving adoption, and navigating the cultural and organizational nuances of scaling enterprise analytics worldwide.

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/UCz9e56lhYUfORiOHMiLlPmA

Chapters:

00:00 – Welcome: Meet Sirisha Nukala, Head of Data Insights & Enterprise Analytics at Louis Vuitton
03:07 – Sirisha’s journey from pharma to luxury retail
06:15 – What analytics means in a heritage luxury brand
09:22 – Leading a global team and managing cultural complexity
12:30 – Creating business alignment through data insights
15:38 – From dashboards to strategic conversations
18:45 – Building credibility and trust with stakeholders
21:52 – Empowering local markets with enterprise analytics
24:59 – Gaining buy-in: storytelling, influence, and persistence
28:06 – Driving change in a traditionally creative industry
31:13 – Early AI adoption: what’s working in luxury retail
34:20 – Building analytics infrastructure for scale and agility
37:27 – Sirisha’s leadership values: empathy, clarity, empowerment
40:34 – Navigating resistance and legacy ways of working
43:41 – Lessons from pharma and consulting: adaptability and agility
46:48 – Advice to data leaders in global organizations
49:55 – Final reflections on trust, culture, and business alignment