Agenda Day 1

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

Innovative technologies are transforming businesses, but at their core, success depends on people. Build breakthrough technologies while reshaping recruitment, employee engagement, leadership, and learning and development processes. As organizations adopt new workflows, understanding the evolving relationship between humans and these technologies is crucial. Take back to your office strategies to:

• Define the role of humans in AI-driven decision-making and operations.
• Implement effective change management strategies for breakthrough technologies.
• Cultivate a culture of continuous learning and upskilling to align with new advancements.

Adapt your workforce to thrive and embrace new technologies by placing people at the centre of transformation.

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 can only deliver real business value when it operates on a unified data and governance foundation. Without it, AI agents risk providing inconsistent answers, exposing sensitive data, and driving poor decisions. Organizations relying on fragmented silos or ad hoc integrations inevitably face compliance risks and a loss of trust. Leave with a blueprint to:

• Ensure every AI-driven decision aligns with security, privacy, and regulatory mandates.
• Eliminate data silos and deliver consistent, explainable outcomes across business functions.
• Scale agentic AI initiatives from pilots to enterprise-grade deployments that accelerate automation and decision-making.

Operationalize AI to responsibly drive measurable outcomes.

(Please select one)

Break into smaller groups of approximately 25 industry peers to work through “how-tos” and apply them to your business scenario.

WORKSHOP A: GENAI SECURITY

Ditch ChatGPT: Master the Art of Securing Your Enterprise Retrieval-Augmented Generation (RAG)

Off-the-shelf generative AI tools like ChatGPT can be powerful — but in financial services, they pose significant compliance, security, and privacy risks. That’s why leading institutions are ditching public LLMs in favour of secure, enterprise-grade RAG systems that integrate directly with vetted internal datasets. Build, govern, and scale an AI platform that serves as a trusted decision-support engine for regulated environments. Take away specific solutions to:

• Design a secure RAG architecture that separates model capabilities from sensitive data, ensuring no confidential information leaks into public AI training sets.
• Implement strong governance frameworks to monitor and audit AI outputs for accuracy, bias, and regulatory compliance.
• Integrate RAG into existing analytics platforms so users can access AI-powered insights without abandoning approved workflows or tools.
• Develop a talent strategy for AI stewardship, upskilling teams to prompt effectively, validate responses, and maintain institutional knowledge integrity.

Excel in deploying a domain-specific RAG environment that delivers secure, compliant, and creative AI insights.

Simon Sulyma, AVP, Information Risk, Manulife

OR

WORKSHOP B: TEAM PERFORMANCE IN THE AI ERA

Designing for Neurodiversity: Building Teams and AI that Learn from Every Mind

Most organizations have a neurodiversity policy on paper. Far fewer have created working environments and technologies that truly embrace the many ways people perceive, process, and apply new knowledge. Cognitive diversity is not just a talent issue, it’s a competitive advantage for human performance and for training better AI systems. Develop a blueprint to:

• Reimagine work practices to accommodate a wide range of learning and processing styles, from communication norms to meeting structures.
• Design digital interactions and apps that account for different ways people engage with technology, ensuring inclusivity is built into every user experience.
• Train AI models on diverse datasets, understanding that models built from a narrow slice of data will miss the richness of human cognition and context.
• Build governance frameworks that ensure both people and AI systems can adapt, self-correct, and thrive in complex environments.

Bolster human and AI capabilities by creating inclusive environments where diverse minds thrive to drive innovation and performance.

Sumeet Bhatia, Senior Director, Head of Data Strategy & Enablement, Sun Life 

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.

Many financial institutions are stuck in “proof-of-concept purgatory” — running promising predictive analytics and automation pilots that never make it to production. The result? Wasted investment, frustrated teams, and missed competitive advantage. Take back to your office strategies to:

• Diagnose the POC trap to mitigate initiative stall and the organizational patterns that cause it.
• Execute from prototype to production using frameworks for transitioning models and workflows into operational systems.
• Expedite data readiness and infrastructure scaling, ensuring quality, accessibility, and integration across the enterprise.
• Build trust across stakeholders communicating model transparency, fairness, and compliance to business leaders and regulators.

Reduce barriers to scale, aligning technology, governance, and talent to ensure predictive analytics and automation deliver real results.

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!