Data: Heaven or Hell? (Adastra Podcast)

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?

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?

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.

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?

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?

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”?

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

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?

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

How do you measure the success of data governance? Where do you find both the technical and “human” profiles for the team? And why should data governance be a natural part of work — not just another extra activity? In the podcast, Pavlína Vajgarová, Data Intelligence Lead at one of the largest Czech banks, explains how Česká spořitelna gradually transformed its approach to data over three years — from an Excel sheet of business terms to a dedicated data governance team and a full organizational rollout.

V CETIN nepodléhají každému hype. Když přišla AI vlna, místo chatbotů a hezkých demo ukázek začali hledat konkrétní byznysové případy, kde může umělá inteligence reálně pomoct. „Nepotřebujeme víc sexy nástrojů. Potřebujeme nástroje, které fungují,“ říká Jan Štěpánovský, CIO CETIN, v novém dílu podcastu Adastry. Zjistěte více o řešení: Data governance

Tracking ESG data in Excel? With over 100 sites in 50 countries, that simply wouldn’t work at KUKA. “To manage ESG effectively, it’s not enough to just collect data. We need to understand the impact – across divisions, regions, and products,” says Kerstin Heinrich, Head of Corporate Sustainability at KUKA. In the latest Adastra podcast episode, she explains how ESG reporting is no longer just a compliance formality. It’s becoming a new standard—much like financial accounting. KUKA implemented Microsoft Sustainability Manager, built a custom data collector, and developed detailed guidelines for data governance and estimations.