Learn from the Best

Hear directly from IDC analysts, global experts, and pioneering CIOs on how to scale AI, GenAI, and agentic systems across industries.

Discover Real-World Use Cases

Gain insights into how leading organizations are implementing AI-driven automation to improve agility, resilience, and customer value.

Future-Proof Your Strategy

Understand the long-term business implications of agentic systems, ensuring your organization thrives in a rapidly evolving digital economy.

Master AI Security & Trust

Explore strategies to tackle data privacy, AI observability, model integrity, and digital trust challenges.

Cloud as the AI Foundation

Discover how cloud platforms, edge computing, and 5G enable scalable, secure, and sovereign AI deployments

Regulation for Value Creation, Not Just Compliance

Frame Canada’s forthcoming Artificial Intelligence and Data Act (AIDA) and related provincial privacy frameworks (such as Quebec’s Law 25) as catalysts for responsible innovation. Rather than viewing regulation as a limitation, position it as a design opportunity to build trusted, transparent, and auditable AI systems through principles like risk-by-design, data lineage, and model governance.

Data Sovereignty and Operating Models

Address Canada’s growing focus on data residency, digital sovereignty, and cross-border data flows, particularly with U.S. and global partners. Emphasize the role of sovereign and hybrid cloud strategies in mitigating extra-territorial data access risks while enabling collaboration and compliance.

From Pilot to Production

A limited share of AI proofs of concept move into production in Canada, mirroring global trends. Leaders need clear roadmaps that include MLOps frameworks, performance controls, and business outcome tracking to ensure AI projects evolve from experimentation to scalable, enterprise-wide adoption.

Data Foundations First

Many Canadian organizations still face fragmentation and silos across their data ecosystems. Strengthening data integration, governance, and observability (as well as modernizing platforms for flexibility and scalability) remains essential for building a foundation that can fully support AI and analytics maturity.