• 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?

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?

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?

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?

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?

• 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?

• 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”?