Data Science With Sam

Sam sits down with Ethan Lyon — principal AI engineer, Denver-based, MIT professional education background — for one of the most technically honest conversations on the show. Ethan has built AI systems, thrown them away, and learned more from the failures than the wins. He coined the term "cognitive surrender" to describe the mistake most AI builders make: handing everything to the AI without understanding it, and wondering why the project falls apart. This episode is a practitioner's guide to building AI systems that actually hold up — data trust, MCP architecture, knowledge graphs, self-improving skills, and the planning-first workflow that changes everything. WHAT YOU'LL LEARN ▪ The verification tax — why AI that needs constant fact-checking adds work instead of saving it, and how to fix it at the data layer ▪ Cognitive offloading vs. cognitive surrender — the distinction that determines whether AI makes you better or just faster at being wrong ▪ The two architectures Ethan threw away — a linked-document knowledge graph and a Neo4j personal knowledge graph — and what he rebuilt ▪ The 100% accurate conversational analytics architecture: LookML agents + custom MCP server + semantic layer ▪ The voice-to-design-to-code planning process: 30-40% upfront thinking before any code gets written ▪ How to build a skill that heals other skills: automated trials, golden datasets, and recursive self-improvement ▪ The dreaming simulation: a scheduled Claude Cowork job that reviews sessions and proposes CLAUDE.md improvements GUEST BIO Ethan Lyon is an AI Architect based in Denver, Colorado, with a background in MIT professional education. He has spent years building and scaling data and AI infrastructure, and writes publicly about what AI is actually doing to engineering teams — including the honest reality that some of the most elegant-sounding architectures don't survive contact with production. He built systems, threw them away, and learned more from the failures than the wins. GUEST LINKS Ethan Lyon on LinkedIn SUBSCRIBE Enjoying the show? Subscribe on YouTube, Spotify, Apple Podcasts, Amazon Music, iHeartRadio, and Podbean. Leave a rating and review — it helps more practitioners and engineers find the show.

Sam sits down with Dr. Zahra Timsah — co-founder and CEO of Igentic AI and author of The AI CEO — for a direct conversation about what AI is actually doing to leadership. Dr. Timsah holds a PhD in biochemistry and an MBA in strategy, has led AI transformation at IQBia, Mass Mutual, and GSK, and has advised the FDA and US Senate on AI policy. She's also one of the clearest voices making the case that the AI era is a particular opportunity for women who know how to seize it. WHAT YOU'LL LEARN ▪ What "The AI CEO" actually means — leadership when AI becomes part of every decision ▪ How the experience advantage has shifted: from knowing answers to asking the right questions ▪ Why the most experienced leaders often resist AI the most — and how to move past that ▪ AI as an equalizer for women: building ownership, visibility, and income — not just saving time ▪ The three human qualities companies are hiring executives for now: judgment, courage, and accountability ▪ One practical action for any leader who feels behind on AI — something you can do this week ▪ The next leadership frontier: AI that acts, not just advises — and what "collective intelligence" means in practice GUEST BIO Dr. Zahra Timsah is co-founder and CEO of Igentic AI and author of The AI CEO. She holds a PhD in biochemistry, an MBA in strategy, and an MSc, and has led AI transformation at IQVIA, Mass Mutual, and GSK. She has advised the FDA and the US Senate on AI policy, contributes to the World Economic Forum's AI governance work, and is an executive member of Chief. Igentic AI's Gini platform turns compliance from a brake into a business accelerator. BOOK & GUEST LINKS Dr. Zahra Timsah on LinkedIn Igentic AI — igentic.ai The AI CEO — https://www.amazon.com/dp/B0HBXGXT16?lv=shuf&channelId=500&plpRedirect=mhFallback SUBSCRIBE Enjoying the show? Subscribe on YouTube, Spotify, Apple Podcasts, Amazon Music, and iHeartRadio. Leave a rating and review - it helps more practitioners and leaders find the show.

Sam sits down with Shawn Kercher — Co-Founder & CEO of HummingAgent.ai — to cut through the noise around AI agents. Shawn built his company on a simple but powerful observation: most AI products are just ChatGPT wrappers. HummingAgent is his answer — a full-custom software shop that builds private, in-environment AI workflows for companies that run on real data, messy CRMs, and processes that only exist in someone's head. WHAT YOU'LL LEARN IN THIS EPISODE ▪ Why generic AI tools fail in real business environments — and what full-custom actually means ▪ The wrapper test: if Anthropic adds this next quarter, does your product still exist? ▪ HummingAgent's Private GPT — lives in your environment, zero data sent for external training ▪ Humcrew.com — voice AI, texting, proposals, and full company command centre in one product ▪ Real client story: 44 years of PDF data, research time cut from hours to minutes ▪ Why discovery-first, paid-scope engagements beat "move fast and ship demos" ▪ The baby steps rule for agent permissions: one task, one sandbox — expand only after validation GUEST BIO Shawn Kercher is Co-Founder and CEO of HummingAgent.ai, based in Lone Tree, Colorado. With over a decade of engineering leadership experience, Shawn has built HummingAgent as a full-custom software shop — not a generic AI tool vendor. HummingAgent builds private, in-environment AI workflows and agents for small and mid-sized businesses, including the Private GPT product and Humcrew.com command centre. Shawn writes publicly about the risk of AI startups becoming ChatGPT wrappers — and what it actually takes to build AI systems with real domain expertise and data integration. GUEST LINKS Shawn Kercher on LinkedIn SUBSCRIBE Enjoying the show? Subscribe on YouTube, Spotify, Apple Podcasts, Amazon Music, iHeartRadio, and Podbean. Leave a rating and review — it helps more builders and data professionals find the show.

Shashank Akinapalli is a Senior Data Engineer at TCS where he leads cloud data lakehouse initiatives and real-time streaming architectures for supply chain intelligence. With 14 years of experience across IBM DataStage, Databricks, Snowflake, dbt, AWS, and Spark-based ecosystems, he has previously worked with USAA in banking and Blue Cross Blue Shield in healthcare. IN THIS EPISODE: ▪ The biggest architectural shift when migrating from IBM DataStage to a modern cloud lakehouse — and why the technical migration is the easier half, while reconstructing encoded business knowledge is the real challenge ▪ How to choose between Snowflake and Databricks: Databricks for heavy transformation and Python workloads, Snowflake for structured SQL and BI, and where dbt fits as the layer that eliminates repetitive SQL code ▪ The real UNFI example: how batch processing caused inventory misrepresentation (800 units sold, system still showing 1,000 available), and how real-time Databricks pipelines eliminated that problem and unlocked fraud detection on top ▪ Data governance across three regulated domains: the four pillars — data quality, redundancy, least-privilege access, and lineage — and the three questions every framework must answer first ▪ Why CI/CD transformed data engineering: from manual, multi-team deployments with risky rollbacks to automated quality checks, Git version control, and the specific silent failure that finally convinces a team to invest ▪ How the data engineer role shifts over the next three years: from pipeline development to data product engineering, from reactive operations to AI-predicted failure prevention, and from human dashboards to AI agents making autonomous predictions ▪ Practical advice for data engineers who want to stay relevant: Python, PySpark, cloud architecture, and understanding the full data lifecycle from ingestion to consumption FIND SHASHANK: LinkedIn: https://www.linkedin.com/in/shashank-a-2aa629119/ Twitter / Instagram: @shashi001git Happy to answer questions on enterprise data warehouse, data engineering, and cloud architecture modernisation — reach out directly via LinkedIn or email. DATASCIENCEWITHSAM: If you work in data engineering and are navigating a legacy modernisation right now — this is the episode to send your team. Subscribe on Apple Podcasts, Spotify, Amazon Music, iHeartRadio, Podbean, and YouTube. If you enjoyed this episode, please share it with your network. DataScienceWithSam is always looking for new guests for captivating discussions — if you have a topic you'd like to discuss on a 30–45 minute podcast, feel free to reach out.

Karima Sharif-Ali — known affectionately in the industry as the Fresh Princess of Media — is the SVP of Strategic Accounts at Doceree, a platform transforming point-of-care communications for healthcare brands. Over her career, Karima has shaped the marketing journeys of more than 100 pharmaceutical and medical device brands across agencies including WPP, Publicis, and IPG MediaBrands. She is a thought leader featured in AdWeek and MM+M, co-founder of the Women of Color Affinity Group (now the Mosaic Collective Leadership), and a global board member of the Healthcare Businesswomen's Association. She holds an MBA from Rosemont College and a BA in Communications from Temple University. IN THIS EPISODE: ▪ Karima's one word for AI's biggest impact on healthcare media: SPEED — media plans down from two months to two weeks, data available daily instead of monthly, and media planners freed to focus on strategy rather than back-office operations ▪ The difference between AI pilots and real AI adoption: asking 'how does this make what I'm doing better?' rather than experimenting theoretically — and the meeting notes example: two hours reduced to 15 minutes, giving planners 90 minutes back for strategy ▪ Why creative strategy must remain human-led: the concept, the vision, the cultural intelligence. AI reformats and scales what humans create — it cannot originate the strategy itself ▪ The deep fake crisis in healthcare: why AI-generated misinformation targeted at vulnerable audiences (patients, elderly populations) is Karima's biggest concern, and why authenticity and trust are non-negotiable in healthcare media ▪ The copyright and IP question: should AI companies establish partnerships before inheriting creative work — especially in healthcare patient journey content? ▪ The three things that keep Karima up at night: deep fakes, data privacy, and the erosion of trust and authenticity ▪ The five-year vision: AI embedded in daily operations, more regulation, more nimble media companies — and Karima's call for more women and Black executives at the table where AI decisions are made, not just using the tools FIND KARIMA: LinkedIn: https://www.linkedin.com/in/karima-sharif-ali-mba-9520727/ HBA Global Board: hbanet.org DATASCIENCEWITHSAM: If this episode resonated — share it with someone in media or healthcare who is navigating the AI conversation right now. Subscribe on Apple Podcasts, Spotify, Amazon Music, iHeartRadio, Podbean, and YouTube. If you enjoyed this episode, please don't forget to share it with your network. DataScienceWithSam is always looking for new guests for captivating discussions. If you have a topic you'd like to discuss on a 30–45 minute podcast, feel free to reach out.