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?

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?

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?

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?

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?