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.

Across industries, organizations are investing heavily in analytics and AI—but many still struggle with fragmented data estates, inconsistent definitions, and slow, manual integration processes. The result is delayed insights, duplicated reporting efforts, limited trust in dashboards, and AI initiatives that stall before reaching operational scale. Take away practical approaches to:

• Integrate data across diverse sources and platforms to create a trusted, enterprise-wide analytics foundation.
• Automate data pipelines and transformation workflows to reduce manual engineering effort and improve reliability.
• Enable governed self-service analytics that empower business teams while maintaining consistency and control.

Move from fragmented reporting to continuous intelligence by building analytics foundations that scale with your data, your teams, and your ambitions.

Financial services organizations are scaling analytics and AI faster than ever, yet many still struggle with unclear data ownership, inconsistent definitions, and governance processes that exist on paper but not in practice. The result is duplicated reports, regulatory risk, slow approvals, and limited trust in analytics outputs. As data becomes a regulated asset and AI adoption accelerates, governance must move from a compliance exercise to an operational capability embedded directly into how data is created, shared, and used. Build trust in data and analytics with practical approaches to:

• Establish clear data ownership, stewardship, and accountability across decentralized data domains and lines of business.
• Create a shared enterprise data language through metadata management, business glossaries, and end-to-end lineage.
• Operationalize governance by embedding policies, controls, and workflows directly into analytics, reporting, and AI delivery processes.

Turn governance into a business enabler by creating trusted, auditable data foundations that support faster decision-making, regulatory confidence, and scalable AI.

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.

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!