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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?

28. 4. 2026

Jon Steffey, Senior Director of Enterprise Software and Analytics at Tolmar, shares how a unified data platform, risk‑aware governance, and AI‑enabled workflows are helping a mid‑sized pharma manufacturer modernize without losing sight of quality. He explains how Tolmar is digitizing manual processes, breaking down post‑M&A data silos, and using Microsoft Fabric to move leaders from data overload and gut feel to more empirical decisions. Drawing on experience in aerospace, medical devices, and pharma, he shows why data is foundational to process control in regulated manufacturing and how to match the rigor of controls to real product and patient risk. How do you modernize a legacy, siloed pharma data landscape while focusing on fundamentals like ingestion, transformation, and governance instead of chasing a single “killer” use case? What changes when leaders move from fragmented reports to a unified view of end‑to‑end manufacturing and quality data? How can pharma organizations tune development and validation rigor to the impact and risk of each use case, from low‑risk UI changes to high‑impact, AI‑influenced therapies?

20. 4. 2026

• Jak připravit data, tak aby AI (https://adastracorp.com/cs/adastra-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 (https://adastracorp.com/cs/power-bi/) .

13. 4. 2026

AI v reportingu nepřináší jen zrychlení. Velmi rychle také odhalí slabiny: špatně definované metriky, nejasné pojmy nebo chybějící kontext. A právě to je dnes pro mnoho organizací větší problém než samotná technologie. Podcast Adastry.

12. 4. 2026

In under two months, Tolmar unified data from a dozen systems into Microsoft Fabric and built production-ready, cross-domain insights across manufacturing, quality, and inventory. With Adastra’s metadata-driven ingestion, AI-accelerated Data 360, and a Fabric data agent, business users now ask natural language questions on governed data, eliminating Excel stitching, accelerating decisions, and proactively protecting patient supply and outcomes.

24. 3. 2026

At the Canadian Manufacturing Technology Show (CMTS), we teamed up with AWS to show how agentic AI helps manufacturers keep production running, improve quality, and react to changes faster.

23. 3. 2026

Sam Wong, Senior Director of Data and AI at the Mark Anthony Group, explains how a business-first AI incubation program can turn experiments into production value. He outlines a five-part framework, the importance of executive and business sponsorship, partnering with a vendor ecosystem, and prioritizing use cases by value versus effort. Wong discusses why you shouldn’t wait for perfect data, how AI projects catalyze data governance and quality, and how a blended federated/centralized operating model scales delivery in a mid‑market company. He also tackles change management and job-loss fears, positioning GenAI as augmented intelligence.  What does it take to build an incubator that learns fast and ships real value?    When is “good enough” data enough—and how can AI expose and improve the gaps?   Which operating model and governance practices unlock adoption without slowing delivery?

23. 3. 2026

Kevin Harmer, Chief Cloud Officer at Adastra, demystifies Agentic AI and how it frees teams from repetitive work to focus on higher-value outcomes. He lays out a three-horizon path—from decision insights to decision augmentation to enterprise-scale decision automation—with managed autonomy, human-in-the-loop controls, and confidence thresholds. Harmer explains how to move beyond personal productivity tools like Copilot to an enterprise agent framework and catalog (e.g., a reusable KYC agent), and a three-step program: strategy and process mining, lighthouse proof, and a governed platform for scale. He shares real-world results, including invoice automation saving 200,000 hours annually, a revenue cycle management “agentic workforce” that cuts costs and accelerates payments, and “Happier Trucks” logistics that reduce empty space and boost revenue with route-aware sales recommendations.  What does it take to move from insights and recommendations to trusted, enterprise-scale automation?  When is “good enough” data enough—and how can agents surface gaps and improve accuracy over time?  Which governance, operating model, and change practices build trust without slowing execution?

23. 3. 2026

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: demand forecasting, planogram automation, and Adastra’s Mark Anthony Group solution that scales assortment optimization and auto-generates seller scripts. He also outlines quick-win assistants, cost controls, and a company-wide 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?

23. 3. 2026

In under two months, Tolmar unified data from a dozen systems into Microsoft Fabric and built production-ready, cross-domain insights across manufacturing, quality, and inventory. With Adastra’s metadata-driven ingestion, AI-accelerated Data 360, and a Fabric data agent, business users now ask natural language questions on governed data, eliminating Excel stitching, accelerating decisions, and proactively protecting patient supply and outcomes.

23. 3. 2026

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?

23. 3. 2026

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?

23. 3. 2026

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?

23. 3. 2026

Jan Vacek explains how DHL approaches AI adoption at the enterprise level–from a strong emphasis on data security to building an internal AI community across hundreds of employees. Learn more about AI solutions: https://adastracorp.com/adastra-ai/

23. 3. 2026

Heritage Grocers Group set out to better serve diverse ethnic communities, especially its Hispanic customers, by unlocking deeper insights from its growing data ecosystem. With siloed systems, 1.5TB of POS data across 115 stores, and multiple banner acquisitions, speed and scalability were critical. In partnership with Adastra and powered by Microsoft Fabric, Heritage consolidated three banners in just two months and achieved: - 70% faster data acquisition - 400x reduction in POS onboarding time - 20x increase in development speed - 10x cost savings The result is faster M&A integration, actionable customer insights, and a strong foundation for intelligent, AI-ready retail operations.

19. 3. 2026