Agenda - CDAO Canada

DAY 1 March 24
DAY 2 March 25

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8:00

Registration & Light Breakfast
8:50

Chairperson's Opening Remarks
9:00

Opening Keynote: Canada Strong: Taking Ownership of Our Data & AI Future
- How can Canadian enterprises strengthen their leadership in AI and advanced analytics while building greater control over their digital future?
- What role do Canadian talent, innovation and domestic technology capabilities play in building a resilient and globally competitive data economy?
- How can organizations reduce critical technology dependencies while continuing to benefit from international partnerships and innovation?
- What strategies should Canadian data and AI leaders prioritize to build an independent, innovative and future-ready enterprise ecosystem?
9:30

Panel Discussion: The CDAO Power Shift: Who Owns AI Transformation in Canada's Largest Enterprises?
- How is the role of the CDAO evolving as AI moves from technical experimentation to enterprise-wide business transformation?
- Who should own AI strategy, accountability and governance as responsibilities increasingly overlap between CDAOs, CAIOs, CIOs and business leaders?
- How can Canadian enterprises establish the data foundations, operating models and governance capabilities needed to scale trusted AI?
- What must data and AI leaders change in their organizations today to move from ambitious AI strategies to measurable enterprise outcomes?
10:00

Keynote Presentation: The Agentic Enterprise, What Happens When AI Starts Making Decisions?
- Why are so many organisations struggling to move from AI experimentation to reliable, production-ready agentic systems?
- What technical and organisational foundations are needed to successfully deploy AI agents across complex enterprise workflows?
- How can businesses introduce autonomous AI capabilities while maintaining human oversight, accountability, and operational control?
- What practical lessons can organisations learn from early enterprise deployments to accelerate adoption and deliver measurable outcomes?
10:30

Panel Discussion: Show Me the Returns: Where Is Enterprise AI Actually Paying Off?
- Where are Canadian enterprises seeing measurable returns from AI investments, and which use cases are failing to deliver on expectations?
- How are senior data and AI leaders demonstrating the commercial impact of AI beyond efficiency gains and productivity improvements?
- What separates successful, enterprise-wide AI deployments from expensive pilots that never achieve meaningful scale?
- How should organizations rethink investment priorities, performance metrics and resource allocation to maximize long-term AI value?
11:00

Morning Break & Networking
11:30

Keynote Presentation: The Sovereign Data Dilemma: Who Really Controls Canada's Enterprise Data?
- How should Canadian enterprises approach data sovereignty as reliance on global cloud providers and AI platforms continues to grow?
- What are the practical trade-offs between data residency, jurisdictional control, security, accessibility and innovation?
- How can organisations design hybrid and multi-cloud architectures that support Canadian data requirements without restricting scalability?
- What role should data leaders play in evaluating sovereign infrastructure, cross-border dependencies, and long-term technology resilience?
11:50

Panel Discussion: Govern the Agent, Not Just the Model: Rethinking AI Risk in the Autonomous Enterprise
- How must traditional AI governance frameworks evolve as autonomous agents begin making decisions and executing business processes?
- What controls are needed to monitor agent behavior, manage access to sensitive data, and ensure accountability across AI workflows?
- How can data, technology, legal and risk leaders establish governance models that enable innovation rather than delay deployment?
- Where should organisations draw the line between AI autonomy and human intervention, particularly in high-impact or regulated environments?
12:20

Keynote Presentation: From Data Lakes to AI-Ready Ecosystems: Rebuilding the Modern Data Enterprise
- Why are traditional enterprise data architectures struggling to meet the demands of generative and agentic AI?
- How can organisations modernize fragmented data environments through data products, metadata management, semantic layers, and interoperability?
- What role do data quality, lineage and real-time access play in creating reliable and context-aware AI systems?
- How can data leaders balance architecture modernization, legacy infrastructure and investment priorities while delivering business value?
12:40

Keynote Presentation: The Rise of Decision Intelligence and Autonomous Analytics
- How is generative AI transforming business intelligence from traditional reporting towards conversational and increasingly autonomous analytics?
- What opportunities do natural-language querying, predictive intelligence and AI-assisted decision-making create for Canadian enterprises?
- How can organisations ensure AI-generated insights remain trustworthy, explainable and grounded in accurate enterprise data?
- What practical steps can data leaders take to embed intelligent analytics directly into business processes and everyday decisions?
1:00

Lunch Break & Networking
Track A - Enterprise Data & AI Transformation
2:00

Track Presentation: From Data to Decisions, building an AI-Ready Enterprise That Delivers Business Value
- How can Canadian enterprises connect fragmented data across business functions to create a trusted foundation for AI-driven decision-making?
- What role do data products, business context and integrated analytics play in enabling reliable, scalable AI across enterprise operations?
- How can organizations modernize their existing data environments while maintaining governance, interoperability, and access to real-time insights?
- What practical strategies can data leaders implement to translate AI-ready data foundations into measurable business outcomes and operational efficiencies?
2:30

Panel Discussion: Canada's Data Sovereignty Challenge: Balancing Global Innovation with Local Control
- How are Canadian enterprises navigating the tension between adopting global AI technologies and maintaining control over their data assets?
- What factors should organisations consider when deciding where sensitive data is stored, processed, and accessed?
- How can enterprises establish governance and technology frameworks that address cross-border data risks without limiting innovation?
- What role can Canada's domestic AI ecosystem and infrastructure providers play in supporting more resilient enterprise data strategies.
3:00

Track presentation: The AI-Ready Workforce: Turning Data Culture into Enterprise-Wide Adoption
- How can Canadian organisations build a workforce equipped to work effectively alongside AI and advanced analytics technologies?
- What are the biggest cultural and organisational barriers preventing employees from adopting AI in everyday workflows?
- How can data leaders develop AI literacy, practical skills and confidence across technical and non-technical teams?
- How should organisations measure the success of AI adoption initiatives beyond training completion and tool usage
Track B - Data & AI in Financial Services
2:00

The Intelligent Bank: Scaling Agentic AI Across Financial Services
- How are Canadian financial institutions moving beyond generative AI assistants towards more autonomous, agent-driven operations?
- Which banking and insurance workflows present the strongest opportunities for agentic AI, from customer servicing to risk and compliance?
- How can financial institutions integrate AI agents with legacy systems while protecting sensitive customer and financial data?
- What governance, monitoring, and human oversight mechanisms are necessary to scale agentic AI safely across regulated environments?
2:30

Panel Discussion: The Countdown to OSFI E-23: Is Canada's Financial Sector Ready for the Next Era of Model Risk?
- How are Canadian banks and insurers preparing for the updated OSFI E-23 model risk management requirements taking effect in May 2027?
- How can financial institutions align their E-21 data risk management frameworks with the emerging model governance requirements under E-23, particularly as AI adoption accelerates?
- What changes are needed to strengthen model inventories, validation, explainability and monitoring as AI use expands across financial services?
- How can institutions establish effective governance for generative and autonomous AI models while maintaining innovation and operational agility?
- What are the greatest implementation challenges facing financial institutions, and how can data, AI and risk leaders address them collaboratively?
3:00

Track Presentation: Beyond Fraud Detection: Reinventing Financial Intelligence with Real-Time AI
- How are Canadian financial institutions using real-time data and AI to improve fraud detection, risk assessment and customer intelligence?
- What opportunities exist to move from reactive fraud monitoring towards predictive, adaptive and proactive risk management?
- How can financial institutions combine transaction data, behavioral insights, and advanced analytics to strengthen decision-making?
- What data infrastructure, governance and model monitoring capabilities are needed to deliver reliable real-time intelligence at scale?
3:30

Afternoon Break & Networking
Track A - Enterprise Data & AI Transformation
4:00

Track Presentation: The Hidden Cost of AI: Managing Compute, Infrastructure and the Economics of Scale
- How can Canadian enterprises accurately assess the total cost of ownership of AI initiatives, including compute, infrastructure, deployment and ongoing maintenance?
- What strategies can organisations use to optimize model selection, inference costs and cloud consumption without compromising performance?
- How should data and technology leaders evaluate the financial trade-offs between building proprietary AI capabilities and adopting third-party solutions?
- What role can AI FinOps and improved cost transparency play in supporting sustainable enterprise-wide AI adoption?
4:30

Closing RoundTable: The 2030 Reality Check: What Will Break First in Canada's Data & AI Enterprise?
- Are current enterprise data architectures, governance frameworks and infrastructure investments equipped to support the next generation of AI capabilities?
- What assumptions about today's AI technologies, operating models and enterprise strategies are most likely to prove wrong by 2030?
- How can Canadian organisations prepare for emerging challenges involving autonomous systems, data sovereignty, escalating costs and technology dependencies?
- What should enterprise data and AI leaders prioritize over the next three years to build more adaptable, resilient and future-ready organisations?
Track B - Data & AI in Financial Services
4:00

Track Presentation: Breaking the Legacy Barrier: Modernizing Canada's Financial Data Infrastructure for AI
- How can Canadian financial institutions modernize fragmented legacy systems to support real-time analytics and enterprise AI adoption?
- What architectural approaches can help integrate cloud, on-premises and hybrid data environments while maintaining security and regulatory compliance?
- How can banks and insurers improve data quality, interoperability and accessibility across complex business lines?
- What lessons can financial institutions learn from successful modernization programmes to accelerate delivery while minimizing operational disruption?
4:30

Closing Roundtable: The Future of Canadian Finance: Can Banks Innovate Fast Enough for the AI Economy?
- How are AI-native fintechs, changing customer expectations and emerging technologies challenging traditional Canadian banking and insurance models?
- What opportunities do agentic AI, real-time analytics and intelligent automation create for transforming financial products, services and operations?
- How can financial institutions balance innovation with the demands of regulatory compliance, cybersecurity, and legacy system modernization?
- What investments and strategic priorities will differentiate Canada's financial institutions in an increasingly AI-driven economy?
5:00

Networking Reception & Chances to Win
6:00

End of Conference Day One

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08:20

Registration & Light Breakfast
8:55

Chairperson's Opening Remarks
9:00

Opening Keynote: From AI Innovation to National Impact: Powering Canada's Next Industrial Transformation
- How is AI reshaping Canada's traditional economic strengths, from energy and natural resources to manufacturing, transport and infrastructure?
- What opportunities exist for Canadian enterprises to apply AI and advanced analytics to improve industrial productivity, operational efficiency and competitiveness?
- How can organisations overcome challenges involving legacy operational systems, fragmented data, and the integration of AI into physical environments?
- What role can data and AI leaders play in strengthening Canada's industrial innovation ecosystem and delivering measurable economic value?
9:30

Panel Discussion: Responsible AI Under Pressure: Who Is Accountable When Things Go Wrong?
- As AI becomes embedded in high-impact enterprise decisions, how are Canadian organizations establishing clear accountability when systems produce unintended or harmful outcomes?
- What governance approaches are proving effective in identifying bias, managing model risk and ensuring AI decisions remain transparent and explainable?
- How should executives respond when AI systems make mistakes that affect customers, employees, or critical business operations?
- How can Canadian enterprises build practical, enforceable AI safeguards without creating barriers to innovation and adoption?
10:00

Keynote Presentation: From Prediction to Prevention: The Next Frontier of AI in Canadian Healthcare
- How are Canadian healthcare organisations using predictive analytics and AI to improve patient outcomes, clinical workflows and resource allocation?
- What challenges must healthcare leaders overcome when integrating AI into fragmented clinical systems and sensitive patient data environments?
- How can organisations improve health data interoperability while maintaining privacy, security, and appropriate patient consent?
- What lessons from real-world deployments can help healthcare providers move from promising research to safe, sustainable AI adoption?
10:30

Keynote Presentation: The Data Quality Crisis: Why AI Can't Fix What It Can't Trust
- How are poor data quality, inconsistent definitions and fragmented ownership limiting the performance and reliability of enterprise AI systems?
- What approaches can organisations take to embed continuous data quality monitoring, observability and automated issue detection into enterprise data pipelines?
- How can data leaders establish clear ownership, accountability and standards for trusted data across complex organisations?
- What practical lessons demonstrate how stronger data quality directly improves AI performance, business decisions and operational outcomes?
11:00

Morning Break & Networking
11:30

Keynote Presentation: AI in the Real World: Keeping Enterprise Models Reliable After Deployment
- What challenges emerge when AI models move from controlled testing environments into complex, real-world enterprise operations?
- How can organisations detect model drift, performance degradation, unexpected behavior, and emerging risks before they affect business outcomes?
- What monitoring, testing, and observability capabilities are needed to maintain reliable AI systems throughout their operational lifecycle?
- How can organisations establish effective processes for model updates, incident response and continuous improvement as AI adoption scales?
12:00

Panel Discussion: Canada's Data Talent Challenge: Building the Next Generation of AI Leaders
- How are Canadian enterprises responding to growing demand for specialized data, AI engineering and analytics capabilities?
- What strategies can organisations use to attract, develop and retain high-performing data and AI professionals in an increasingly competitive talent market?
- How can businesses strengthen collaboration between universities, research institutions and industry to develop future-ready AI talent?
- What skills and leadership capabilities will become most valuable as AI reshapes technical roles, business functions and organisational structures
12:30

Keynote Presentation: The Customer Intelligence Revolution: From Personalization to Predictive Experiences
- How are Canadian enterprises combining customer data, AI and advanced analytics to deliver more relevant and personalized experiences?
- What opportunities do predictive intelligence and real-time behavioral insights create for anticipating customer needs and improving engagement?
- How can organisations unify fragmented customer data across digital and physical channels to create a more consistent customer experience?
- How can businesses balance deeper personalization with privacy expectations, customer trust and responsible data use?
1:00

Lunch Break & Networking
Track A - Enterprise Data & AI Transformation
2:00

Track presentation: How AI Is Transforming Canada's Energy, Manufacturing and Supply Chains
- How are Canadian organizations leveraging AI and advanced analytics to optimize industrial operations, asset performance and supply-chain efficiency?
- What opportunities do predictive maintenance, computer vision and real-time operational intelligence create for reducing downtime and improving productivity?
- How can enterprises connect operational technology and enterprise data environments to enable more intelligent decision-making?
- What lessons from real-world industrial AI deployments can help organizations overcome implementation challenges and achieve scalable outcomes
2:30

Track Panel Discussion: The Data Product Revolution: Turning Enterprise Data into Something the Business Actually Uses
- How are Canadian enterprises moving away from centralized data delivery towards data products that directly address business needs?
- What organisational and architectural changes are required to establish effective data product ownership, discoverability and interoperability?
- How can organisations encourage business teams to adopt self-service data capabilities without compromising governance or consistency?
- What metrics should data leaders use to evaluate whether their data products are improving decision-making, productivity, and business performance?
3:00

Track Presentation: The Digital Twin Advantage: Turning Real-Time Data into Smarter Operations
- How are Canadian enterprises using digital twins to create intelligent representations of physical assets, operations and infrastructure?
- What opportunities do real-time data, simulation and predictive analytics create for improving asset performance, maintenance and operational planning?
- How can organisations integrate IoT, sensor data, and AI models to support more accurate operational insights and decision-making?
- What practical challenges and lessons should enterprises consider when moving digital twin technologies from proof of concept into operational use?
Track B - Data & AI in Financial Services
2:00

Track Presentation: Beyond the Transaction: Using AI to Transform Customer Experience in Canadian Banking
- How are Canadian banks and financial institutions using AI to create more personalized, proactive and accessible customer experiences?
- What opportunities do predictive analytics and customer intelligence create for anticipating financial needs and improving engagement?
- How can financial institutions connect fragmented customer data across digital banking, contact centers and traditional channels?
- What approaches can banks take to measure customer experience improvements while protecting privacy and maintaining trust?
2:30

Track Panel Discussion: Open Banking, Open Possibilities: Canada's Next Chapter in Financial Data Innovation
- How could Canada's evolving consumer-driven banking framework reshape financial data sharing, innovation and competition?
- What opportunities could secure, permission-based data access create for more personalized financial products and customer experiences?
- How should banks, fintechs and financial institutions prepare their data infrastructure, APIs and governance frameworks for greater interoperability?
- What challenges must the Canadian financial ecosystem address around consent management, liability, cybersecurity and consumer trust?
3:00

Track Presentation: From Risk to Resilience: How AI Is Reinventing Canadian Banking and Insurance
- How are Canadian banks and insurers leveraging AI and advanced analytics to transform underwriting, claims management, credit risk assessment and financial decision-making?
- What opportunities do predictive analytics and real-time intelligence create for identifying emerging risks, improving operational resilience, and delivering better customer outcomes?
- How can financial institutions overcome fragmented data environments and legacy systems to enable more accurate, efficient and scalable AI-driven processes?
- What strategies can organizations adopt to balance AI innovation with regulatory compliance, model explainability, fairness and customer trust?
3:30

Afternoon Break & Networking
Track A - Enterprise Data & AI Transformation
4:00

Discussion Group: Build, Buy or Partner? Making the Right AI Technology Decisions for the Enterprise
- When should Canadian enterprises build their own AI capabilities rather than purchase existing solutions or collaborate with external providers?
- What factors should data leaders consider when evaluating technology ownership, vendor dependency, flexibility, and long-term scalability?
- How can organisations assess the risks of integrating multiple AI technologies into existing data and enterprise systems?
- What experiences and lessons can executives share about technology investments that exceeded expectations or failed to deliver?
4:30

Discussion Group: The Analytics Blind Spot: What Are Canadian Enterprises Still Not Measuring?
- Where do Canadian organisations continue to face gaps in data visibility, measurement and understanding of business performance?
- How can data leaders identify high-value information that is currently overlooked, inaccessible or underutilized across their organisations?
- What approaches can organisations take to connect operational, financial, and customer data to develop a more complete picture of enterprise performance?
- If executives could redesign their analytics strategy from scratch, which metrics, capabilities and decision-making processes would they prioritize?
Track B - Data & AI in Financial Services
4:00

Discussion Group: The Financial Crime Arms Race: Can AI Keep Up with AI-Powered Threats?
- How are generative AI, deepfakes and increasingly sophisticated cyber-enabled fraud changing the financial crime landscape for Canadian institutions?
- What role can advanced analytics, behavioral intelligence and AI-driven detection play in identifying emerging fraud patterns?
- How can banks and insurers improve collaboration, information sharing, and detection capabilities while protecting sensitive financial data?
- What practical measures should organisations prioritize to build resilience against increasingly automated and adaptive financial threats?
4:30

Discussion Group: Banking in 2030: What Will the Next Generation of Canadian Financial Services Look Like?
- How could AI-native financial services, intelligent automation and emerging technologies reshape the traditional Canadian banking and insurance landscape?
- What customer behaviors, competitive pressures and technological developments are most likely to influence the future of financial services?
- How can established financial institutions remain adaptable while competing with digital-first challengers and emerging fintech business models?
- What strategic decisions should Canadian financial leaders make today to prepare their organisations for the next generation of financial services?
5:10

End of Conference

CDAO Canada schedule

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