Agenda Day 1

Gain insight into today’s sessions so you can get the most out of your conference experience.

AI experimentation is now widespread, yet real financial returns remain elusive for many organizations. Too often, pilots multiply without clear ownership, agents are deployed without operational readiness, and promising solutions fail to scale because the organization cannot absorb the change. Monetizing AI requires far more than technical capability—it demands discipline across execution, people, and operating models. Turn AI investment into sustained business value with practical approaches to:

• Identify AI use cases with clear economic value and realistic paths to production, avoiding experimentation without return.
• Design AI solutions that are operationally ready and governable, aligned with existing workflows, controls, and decision-making structures.
• Build the right mix of talent, roles, and operating models to support AI across its full lifecycle—from experimentation through scale.
• Manage organizational change deliberately, ensuring AI adoption improves performance rather than creating confusion or resistance.

Drive durable returns by moving beyond AI experimentation and into disciplined execution that delivers measurable business impact.

Financial services leaders face mounting pressure to transform AI enthusiasm into measurable business value — all while satisfying stringent governance, risk, and compliance expectations. This keynote will set the tone for the summit by addressing the critical path from experimentation to enterprise adoption. Develop a blueprint to:

• Establish a clear AI vision aligned with regulatory requirements and corporate strategy.
• Balance innovation speed with robust oversight, ethics, and customer trust.
• Measure and communicate the tangible business outcomes of AI investments.

Achieve a practical framework for embedding AI into the operational fabric of your organization to minimize risk and maximize long-term value.

Agentic AI is rapidly changing how work gets done. Systems that can reason, plan, and act autonomously are increasing individual productivity at unprecedented speed, yet many enterprises are struggling to translate that autonomy into scalable business value. As AI agents proliferate, organizations are running into a familiar tension: centralization provides trust, governance, and control but slows innovation, while decentralization unlocks speed and creativity but fragments accountability, data quality, and outcomes. Agentic AI exposes the limits of both approaches. Resolve the autonomy–control tension with practical approaches to: 

• Design a core operating model that centralizes trust, governance, and data foundations while enabling autonomy at the edge where agents operate. 

• Establish clear control mechanisms for agentic systems, including context inheritance, permissions, and accountability, without constraining innovation. 

• Align data, platforms, and operating models so agentic AI can scale consistently across teams, use cases, and markets. 

Enable agentic AI to deliver enterprise-scale impact by pairing autonomy with strong foundations that provide trust, context, and control where it matters most. 

• Explore the latest data analytics technology and strategies catered to financial services with our industry-leading sponsors.
• Share your challenges with the biggest innovators in the business.
• Schedule one-to-one private meetings for personalized advice.

As open banking frameworks mature and AI adoption accelerates, financial institutions face growing pressure to balance innovation with regulatory scrutiny. Too often, teams waste time hunting for usable data, manually documenting compliance steps, and reconciling inconsistent definitions across business units. In a landscape shaped by OSFI E-23, open banking mandates, and increasing model oversight, treating data as an unmanaged asset slows AI initiatives and heightens risk. Shift from fragmented data management to certified data products with practical approaches to: 

• Establish a data product marketplace where curated, certified datasets are discoverable, governed, and ready for analytics and AI use. 

• Embed regulatory requirements such as OSFI E-23 and open banking controls directly into data product design, automating documentation and compliance workflows. 

• Provide end-to-end visibility across data, models, and AI agents to ensure traceability, accountability, and audit readiness at scale. 

Move from manual compliance and data hunting to trusted, model-ready data products that accelerate AI ROI while keeping regulators confident and innovation sustainable. 

Financial institutions sit on a wealth of data, but too often, siloed systems and legacy technology prevent them from unlocking its full potential. Map how leading banks, insurers, and fintechs are modernizing data ecosystems to accelerate value realization. Adopt best practices to: 

• Break down silos between transactional, market, customer, and operational datasets. 
• Leverage cloud-native data platforms to achieve real-time analytics at scale. 
• Embed self-service data access while maintaining security and compliance. 
• Measure and communicate the business value of modern data architectures. 

 Optimize how your organization extracts value from data to drive insights, efficiency, and growth.

As banks move beyond GenAI experiments toward autonomous, agent-driven use cases, one challenge keeps showing up: trust. AI agents cannot personalize experiences, detect fraud, or act autonomously if data is stale, fragmented, or poorly governed. Build a trusted data foundation for Agentic AI by:

• Delivering live, real-time data access across core banking, ERP, and fintech systems, without replication
• Establishing a shared semantic layer so humans and AI agents reason from the same business context
• Embedding governance and security by design, allowing AI to act safely within business and regulatory guardrails
• Turning trusted data into outcomes, including faster insights, reduced fraud risk, and quicker rollout of AI initiatives

Attend this session to see how banks are moving from AI ambition to trusted, production-ready Agentic AI, and why the data layer is now the deciding factor.

Wealthsimple has reshaped Canada’s financial landscape by challenging long-held assumptions about banking, investing, and customer experience. Competing against entrenched incumbents has required more than sleek design or lower fees—it has demanded a deeply data-driven product strategy, capable of understanding customer behavior across investing, savings, payments, and emerging asset classes, while operating at scale in real time. Embrace the journey to:

• Use customer behavior and product analytics to identify unmet needs, prioritize features, and continuously refine low-friction digital financial services.
• Build scalable data infrastructure and analytical models that support real-time trading, aggressive pricing strategies, and expansion into new asset classes while managing risk.
• Integrate advanced analytics, machine learning, and natural language processing directly into the customer experience to deliver personalized insights and decision support.

See how a data-led approach enables Wealthsimple to move faster than incumbents—driving innovation, managing risk, and redefining what modern financial services can be.

As financial services organizations accelerate AI adoption, many are discovering that tomorrow’s models depend on today’s data discipline. Regulatory expectations, model risk scrutiny, and increasing reliance on automation mean that weak data foundations can quickly translate into compliance gaps, reputational risk, and failed AI initiatives. Preparing for an AI-driven future requires a structured, enterprise-wide approach to strengthening data quality, governance, and accountability now. Build AI-ready foundations with practical approaches to: 

• Implement a systematic data improvement plan that strengthens quality, lineage, ownership, and controls across critical data domains. 

• Align data governance, risk management, and analytics teams to ensure regulatory expectations are embedded into AI development from the outset. 

• Prioritize high-impact data domains that directly support future AI use cases, reducing risk while accelerating value delivery. 

Protect your brand by creating a disciplined data program that enables safe, accurate, and regulator-ready AI—positioning the organization for confident innovation at scale.

Dashboards are quietly becoming relics of the past. In their place, natural language search and conversational analytics are emerging as the new front door to enterprise data. How will this shift fundamentally change the shape, skills, and strategy of your data team over the next three years? This isn’t just about technology, it’s about how human interaction with data is evolving, and what that means for hiring, workflows, and organizational design. Adopt best practices to:

• Redefine the skillset of your data team, moving from dashboard builders to data product managers, conversational model trainers, and semantic layer architects.
• Architect your data stack for speed and flexibility, making search-powered analytics as secure and governed as traditional BI tools.
• Build trust in AI-powered analytics, ensuring that search results are transparent, explainable, and aligned with governance standards.
• Anticipate the impact on data culture, empowering non-technical stakeholders to directly interrogate the data while freeing data teams to focus on higher-value innovation.

Transform your data team into a strategic enabler of real-time decision-making to accelerate insight, improve agility, and drive business impact.

Review the key solutions and takeaways from today’s sessions. Source a summary of action points to implement in your work. Discuss tomorrow’s highlights!