Data: Heaven or Hell? (Adastra Podcast)

90: “The Best Airlines Will Use AI to Build Trust, Not Just Revenue,” Says Shingai George, Aviation Expert

Shingai George, Aviation Consultant specializing in data analytics, AI, and sustainability, shares how modern data platforms, AI-driven decision-making, and shared data ecosystems are helping the aviation industry adapt to a new era of geopolitical volatility, regulatory pressure, and sustainability demands. He explains how airlines are moving beyond decades of cost-and-efficiency optimization toward resilience as a competitive advantage, and how AI is reshaping everything from flight planning and predictive maintenance to passenger experience and emissions management. He also explores why the future of airline success lies in shifting from short-term yield to long-term customer value, using AI to build loyalty and trust, not just revenue. Drawing on experience across customer service, flight operations, route development, flight safety, MRO, and sustainability, he highlights why aviation, one of the world’s most interconnected industries, must move from fragmented optimization toward network-wide intelligence. This episode will answer: How can airlines build resilience into flight planning, fuel forecasting, and operational decisions when geopolitical shocks can reroute entire networks overnight? What role does AI play in transforming the passenger journey from reactive service to predictive engagement, without crossing the line between personalization and perceived unfairness? How can the industry responsibly share data across airlines, airports, and regulators, and why is federated data sharing key to the future of air traffic management, sustainability, and network resilience?

89: „Bez AI nepůjde změny ve školách škálovat“, říká Tomáš Kaplan z NF Eduzměna

Jak se měří dopad systémové změny ve vzdělávání? Proč je těžší pracovat s kvalitativními rozhovory než s tabulkou počtů škol a učitelů? A jak může AI pomoci školám doporučovat vhodné formy podpory podle jejich konkrétních potřeb?

88: "Context is King," Says Chris Peart, Snowflake

Chris Peart, Sales Leader at Snowflake Canada, shares how unified data, governed context, and agentic AI are reshaping how enterprises turn information into action. He explains why "context is king" as frontier models become commoditized, how a single AI Data Cloud across AWS, Azure, and GCP removes the brittleness of traditional architectures, and how Snowflake Cortex and Coworker give knowledge workers immediate answers instead of waiting weeks for engineering teams. He also digs into the agentic future and the cultural shift required to win with AI: why "nobody sells anybody anything" and customers buy outcomes, why Canadian enterprises are falling behind global peers by being too cautious, and how the next frontier is autonomous agents negotiating with other agents across organizational boundaries. The episode answers: Why is your data, not the model you choose, the true competitive differentiator in the agentic era? What does a governed context layer look like, and why is it the foundation for agents you can trust? Why are Canadian enterprises falling behind global peers on AI, and what does it take to start swinging?

81: “The technology is good enough. The real hurdle now is people, fear, and change management,” says Shannon Bell, CIO, OpenText

Shannon Bell, EVP, Chief Digital Officer and Chief Information Officer at OpenText, shares how “information first” thinking, simplicity, and agentic AI are reshaping how large enterprises work. She explains why most enterprises don’t have an AI problem but an information problem, how to go slow to go fast with AI, and how a blended workforce of humans and AI agents turns scarce skills and fragmented processes into scalable value. Crucially, she digs into change management and the future of jobs: why fear of displacement is often higher than the reality, how to position AI as a copilot rather than a competitor, and what it means to give 22,000+ employees an AI development goal so they can actively shape how their roles evolve. She also shows where agentic AI is ready now, such as search and summarize, root cause analysis, and software delivery, and why success depends on clear roles, governed data, and using HR and SRE teams as early champions to build an “AI fabric” across the enterprise. What does it really take to make AI an assistant, not a threat, for your workforce? How can you start small on messy, real-world systems and still build toward an AI ready data estate? Which foundations, guardrails, and operating model let you decentralize AI innovation without losing control?

84: "Start with Business Challenges, Not Solutions," Says Justin Rister, Microsoft

Justin Rister, Senior Cloud and AI Specialist at Microsoft, explains why leaders should start with business pain points, not technology. He shares how Fabric unifies the analytics stack for teams of all skillsets, why Databricks and Fabric are a better-together story, and how an AI layer on unified data empowers business users to ask questions and get answers without waiting on IT. What does it mean to be a strategic partner instead of a product pusher? How do you remove bottlenecks by letting business users access insights directly? Why should leaders think big, start small, and scale fast?

86: "31,000 customers have adopted Fabric in the last two and a half years," says Tamer Farag, Microsoft

Tamer Farag, Global Fabric Partner Lead at Microsoft, shares how the fastest-growing analytics platform in the world is helping 31,000 customers unify fragmented data estates and unlock AI value. He highlights why you don't need to move your data to govern it, how mirroring is offered free to accelerate adoption, and what makes partners like Adastra critical to scaling Fabric globally. What does it take to connect AI to your data without a massive migration project? How is Fabric enabling customers to move from static reports to asking questions directly to their data? Which trends, from real-time intelligence to chat with your data, are driving customer demand in 2026?

82: Risk in AI-Developed Cancer Drugs with Jon Steffey, Tolmar

83: AI není zkratka k lepšímu reportingu. Je to spíš test připravenosti vašich dat, říká Kristýna Knížová (Adastra)

Jak připravit data, tak aby AI skutečně pomáhala a neškodila? Jak funguje „chat with your data“ v praxi? A proč bez kontextu AI odpovídá špatně, i když má správná data? Zjistěte více o řešení Power BI.

80: "Helpful, not creepy: personalization that earns trust," says Kevin McCurdy, Global CPG Partner Lead, AWS

Kevin McCurdy, Global Partner Lead, Consumer Goods, AWS, shows how Gen AI, trusted data, and risk-based guardrails turn experiments into repeatable CPG value. He highlights AWS and partner capabilities (Amazon Bedrock, SageMaker, secure integrations) with real wins such as demand forecasting, planogram automation, and Adastra’s Mark Anthony Group solution that scales assortment optimization and auto-generates seller scripts, plus quick-win assistants, cost controls, and an enterprise AI program with clear budgets, ownership, and accountability across product, employee, and customer use cases. What does it take to move from quick wins with Amazon Q to custom, domain-aware agents on Bedrock that scale across the enterprise? When is “good enough” data enough to start, and how can AI assistants surface gaps while improving data quality over time? Which operating model and risk-based guardrails help leaders control cost and compliance while accelerating adoption?

79: “Good enough to start, governed enough to scale," says Rehan Shah, AWS

Rehan Shah, General Manager and Head of Channel and Partner Sales for US Greenfield at AWS, explains how the right mix of AI tools, trustworthy data, and strong controls turns early AI trials into real business results. He shows how AWS provides access to top models, better value, responsible AI practices, and secure ways to connect your systems. Examples include instant insights from manufacturing data and Breakthru Beverage moving hundreds of servers, plus quick AI helpers like a Sales Coach and a Legal Assistant. He also shares how to keep costs in check and set up a company-wide AI program with clear budgets and accountability. What does it take to move from quick wins with Amazon Q to custom agents on Bedrock that scale across the enterprise? When is “good enough” data enough to start, and how can AI assistants surface gaps while improving data quality over time? Which operating model and risk-based guardrails help leaders control cost and compliance while accelerating adoption?

77: “Think of it as a three-layer cake: platform, data, AI,” says Glenn Remoreras, CIO, Breakthru Beverage Group

Glenn Remoreras, EVP, Chief Information Officer at Breakthru Beverage Group, shares how a cloud-first “platform, data, AI” architecture and executive-led AI readiness turn market pressures into value. He highlights migrating 300+ services to AWS, why the data layer is the most critical, risk-based guardrails, and quick-win pilots like Legal GPT and an AI Sales Coach. What does it take to build a foundation that learns fast and scales AI beyond hype? When is “good enough” data enough, and how can AI expose and fix the gaps? Which operating model and governance enable adoption without slowing delivery?

78: "The car is becoming a smartphone on wheels, an extension of your living room," says Chris-Markus Kratz, AWS Global Director of Automotive and Manufacturing

Chris‑Markus “CMK” Kratz, AWS Global Director of Automotive and Manufacturing, explains how outcome‑first, customer‑obsessed transformation and ecosystem partnerships are reshaping the industry. He details the shift to software‑defined vehicles and the car as a proactive companion, how GenAI is collapsing mainframe refactoring from years to months, and what it takes to move beyond pilots to production. Kratz shares lessons from Amazon’s own “shop floor” in its fulfillment centers, why the cloud is ready for OT, and why critical thinking and change management matter as much as technology. He also covers autonomy at scale, the equalizing effect of AI for SMBs and OEMs alike, and the “better together” role of SIs like Adastra. How do OEMs and suppliers work backwards from outcomes to deploy GenAI in real production? What makes the factory floor ready for cloud and AI, and how do you ensure resilience? How does mainframe modernization unlock microservices and accelerate transformation? Which ecosystem partnerships and governance practices deliver value without slowing execution? Is AI the great equalizer across company sizes, and how should leaders manage the cultural shift?

76: Data pomáhají lidem z dluhů. Digitalizace mění práci dluhových poraden (David Borges, Člověk v tísni)

Jak dlouho trval vývoj a rollout řešení? Proč byl pilot klíčový pro přijetí mezi poradci? A jaké další procesy chtějí v Člověku v tísni digitalizovat dál – od komunikace s finančním arbitrem až po soudy? Více o řešeních Power Platform a kvalitě dat.

82: AI makes no sense without customer impact. Implementing it just for the sake of technology is a dead end (Jan Vacek, DHL)

How do you set up an AI strategy in a large organization? Why is security more important than speed? And how do you prevent AI from turning into an uncontrolled “agent zoo”?

75: "It’s not about replacing people—it’s about empowering people," says Kevin Harmer, Chief Cloud Officer at Adastra

Learn more about: Adastra AI

74: Don’t wait for perfect data—start, then improve (Sam Wong, Mark Anthony Group)

What does it take to build an incubator that learns fast and delivers real value? When is “good enough” data enough—and how can AI expose and improve the gaps? Which operating model and governance practices drive adoption without slowing execution? Learn more about: GenAI, Adastra AI

73: Gorillas with Digital Wallets: How AI Helps Understand the Needs of Other Species (Jonathan Ledgard, Tehanu)

How can artificial intelligence help protect biodiversity? Will we ever objectively understand what is in the “interest” of other species? And what happens when the interests of different species collide?

71: The Future of Data Platforms? SaaS and AI, says Adam Wojtkowski from Snowflake

How does Snowflake work with AI models to keep data in a secure environment Why is going back to on-prem solutions no longer an option? And why does Wojtkowski believe SaaS and AI will dominate the future of data platforms?

72: Fabric unifies data work: one version for reporting, SQL, and AI (Lars Andersen, Microsoft)

What use cases are companies solving with Fabric most often? How can organizations start with smaller projects and scale them to an enterprise level? And why does Andersen believe it’s always worth building new solutions “on a blank sheet of paper”?

70: AI-Ready Data Starts with Observability, Not Governance, says Elton Martins, former data leader at the NFL and Genius Sports

How can data observability organizations detect silent failures before they impact business decisions? What’s the difference between data quality management and data observability—and why does it matter for AI readiness? What should organizations consider when deciding to build or buy a data observability solution? Learn more about the solution: AI-ready data