AssetOps Toronto schedule

Setting the tone for a day of action-oriented discussion on data, automation and operational alpha in the Canadian buy-side. 

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.   

• Turning post-tariff and policy volatility into an operational advantage through adaptive infrastructure. 

• Balancing efficiency targets with the need for enterprise-wide resilience and data governance. 

• Lessons from firms that simplified their ops-data-tech ecosystems without sacrificing scale. 

• Where AI and automation are delivering real returns in Canadian operations (workflow orchestration, reconciliation, reporting). 

• Overcoming the execution gap: model governance, training data, and control frameworks in regulated environments. 

• Rethinking talent and change management for a hybrid human-AI workforce.

• From PDFs to platforms — how asset owners are automating valuation and performance processes in private assets. 

• Integrating private and public data streams for risk, liquidity and reporting consistency. 

• Technology partnerships and data standards needed to achieve public-market-style efficiency in alternatives.

• Navigating CSA total-cost reporting, Access Model and ESG anti-greenwashing rules through operational design. 

• Automating audit trails and records retention to satisfy emerging regulatory expectations. 

• Leveraging data transparency and disclosure automation to strengthen investor trust and brand credibility.

• Designing vendor-oversight frameworks that meet CSA/OSFI expectations without adding red tape. 

• Creating shared accountability for data security and service continuity in multi-provider environments. 

• Lessons from Canadian managers who turned outsourcing relationships into true partnerships.

• Rationalizing tech vendors and platforms without losing functionality or governance. 

• Managing change fatigue and aligning culture with technology strategy. 

• Embedding flexibility for new products and regulations into core systems architecture.

• Establishing a single source of truth for data across front, middle and back offices. 

• Integrating metadata, ontologies and controls to prepare for agentic AI workflows. 

• Case examples from firms achieving real-time reporting and auditability.

• Where operations leaders are focusing time and budget for the next 12 months. 

• Upskilling teams for data literacy, automation and AI enablement. 

• Peer exchange on priorities across public and private market operations.

• How COOs are balancing automation with empathy — managing fear, change fatigue, and reskilling across diverse teams. 

• Embedding a culture of experimentation, agility, and data literacy across the organisation. 

• What defines an effective operational leader in 2026: communication, adaptability, and psychological safety.

• Unpacking the "Data Trinity": How data models, metadata, and ontologies collectively shape enterprise AI readiness  

• The metadata gap: Why does incomplete or inconsistent metadata remain the single biggest blocker to scalable AI adoption? 

• Building the AI-ready data foundation: Key principles for aligning business context, governance, and automation  

• From static systems to agentic enterprises: Enabling dynamic decision-making through intelligent data frameworks and metadata-aware architecture 

• Redefining COO mandates around data-driven decisioning and client-centric innovation. 

• What the modern operations leader must own — resilience, tech, and culture. 

• Looking five years ahead: Canada’s role in a global investment community.

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.