Enterprise AI Summit Canada schedule

What it takes to pilot AI to full scale implementation across organization
How do increase the adaption rate in business and measure the direct impact of AI
How do understand the ethical concerns and build the AI in a control and responsible way
How do we make adjustment with new emerging AI technologies such as Generative AI

 

•    How does your company identify which applications utilize artificial intelligence?
•    How does the company evaluate if these applications make sense from an economic perspective or if they are just hype?
•    Identifying the risks and benefits related to the large-scale use of artificial intelligence applications

•    How to overcome the challenges of aligning business and engineering on the AI journey
•    Unlocking the power of process optimization to drive sustainable profitability
•    Strategies for building a bridge between process and delivery in ai finance
•    Best practices for implementing ai solutions that deliver real-world results 

 

 

•    What is the next chapter in the usage of Data in AI?    
•    What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
•    How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency?

 

Panelists:
Maurice Leon, Director Service Governance, CIBC Mellon 
Huzaifa Noman, Global Head of Data & AI, Colliers 
Nitesh Soni, Global AI Solutions Leader, Sanofi 
Parisa Lak, Senior Data Scientist | Group Advanced Analytics, Manulife

Moderator: Brett Harris, Vice President, AI Strategy & Enablement, SGK

•    Evaluating end-user AI use cases and the value proposition that informs ROI
•    Prioritizing investments within a structured farmwork, taking into consideration, data, technical, legal, and resource impacts
•    Validating results to drive continuous improvement and increased business investments

•    An overview of the AI regulatory and legal landscape affecting the Banking sector
•    The importance of having an AI Governance program to manage AI-related risks
•    Key components of an AI Governance program and a practical approach to assess and mitigate AI-related risks

 

•    Leveraging AI to create highly personalized customer experiences and targeted marketing campaigns
•    Using AI to analyze vast amounts of data, uncover insights, and make more informed marketing decisions 
•    Gen AI use cases



 

 

 

•    Objective of the use of AI - Productivity or doing things differently with AI/ How is GenAI different compared to traditional AI/ML and how can it be used in the business context? 
•    What would you recommend companies do to build AI successfully from the ground up? Is data still as critical in the new context of GenAI? 
•    Resistance to change, & elements to a successful AI Adoption? Harsh Singh, Manager, Data 
•    How are you using AI in your personal life?

Panelists:
Javier Medel, Senior Machine Learning Engineer, WM
Harsh Singh, Manager, Data Science | Advance Analytics AI/ML Product Delivery, Sobeys 
Saad Rais, Senior Manager, Health Data Science, Ontario Ministry of Health

Moderator:
Ramzi Abdelmoula, Head of AI Strategy, General Motors Canada

•    Building a financial sector-specific AI strategy
•    Integrating AI with existing business processes and systems
•    Establishing key performance indicators (KPIs) for AI adoption

Panelists:
Aneta Osmola, Data and AI Risk Director, Global Risk Management, Scotiabank
Amit Satpathy, Director of Artificial Intelligence, Munich Re Canada
Martin Bernier, Senior Director, Quantitative Strategies and Data Science, Caisse de Dépôt et Placement du Québec
Priya Alagarsamy, Senior Manager, Deposits and Investments, Pricing & Insights, Scotiabank 

Moderator: Awais Sher Bajwa, Head of Data & AI Banking, Bank of America

• Explore key skills needed to make a successful, impactful data leader
• Hear about some of the greatest data leaders of our time, how they ended up where they are today, and what we can learn from their journey
• Get on the path to developing effective data leaders 

Join us for an exploration of the technological frontier in the life and health insurance industry. This session will delve into the world of Advanced Analytics and Generative AI on product design, pricing, underwriting, and distribution channels. Mike will share examples of tools in production, as well as what his team is working on. Discover how to derive tangible value from emerging technology, leveraging the latest tech trends for meeting your goals. Learn from real examples to harness these innovations for competitive advantage and enhanced customer experience. Don’t miss this opportunity to shape the future of insurance in the digital age.

•    What is Enterprise AI and how do you define an enterprise AI assets? 
•    How do you think enterprise AI assets should help organizations D&A team to deliver business value faster?
•    What are your thoughts on standardization of tools and automation when it comes to deliver value faster in AI and Gen-AI domain? 
•    What do you think about Governance at enterprise level end to end. Starting from intake process, ETL, engineering, ML engineering and AI development?
•    Do you think AI industry doing enough to make it visible that data preparation or engineering is foundational part of overall success? . What do you think as data professionals we should address?
•    Security and Privacy. Today data scientists can generate content which could be private in nature very accurately. Do we think AI industry is taking enough steps?
•    Do you think the link is clear between Platform Engineering – Data Engineering – ML Governance

Panelists:
Shenson Joseph, Data Scientist, JPMorgan Chase & Co
Steffen Klaere, Data Scientist, Definity
Maria Abrar, Data Scientist, Instagram-Meta

Moderator: Moinul Islam, Data Transformation Office, Leader, BELL

•    Creating a Change Management with AI
•    How do you run the change?
•    Should I create new roles in my organization?
•    What are the economic benefits of AI?

•    Developing cost-effective strategies for hiring and retaining top AI and Data talent
•    Exploring how to maximize the potential of free and low-cost resources to build a robust data infrastructure and accelerate development
•    Promoting a culture of knowledge sharing and collaboration within the team to enhance skillsets and innovation

•    Pre ChatGPT: Natural Language Understanding (NLU) driven architectures and why they were successful
•    ChatGPT: Advent of large language models and their impact on conversational AI design, development, and deployment. Do we abandon the old in pursuit of the new?
•    Post ChatGPT: How do enterprise conversational AI systems evolve to meet existing and future challenges. What are some good design principles to keep in mind while building for the future?

•    Providing an overview of how Generative AI is currently being used in production within the banking sector?
•    What are the key benefits that Generative AI offers to banks in terms of production efficiency and customer experience?
•    What are some of the challenges that banks face when implementing Generative AI in production?
•    How can these challenges be overcome, and what are some best practices for successful implementation?

Panelists:
Yannick Lallement, Chief Artificial Intelligence Officer, Scotiabank 
Chris Patterson, Head of Enterprise AI Platforms and Solutions, CIBC
Jeff Kurys, Director, Artificial Intelligence, BELL
Lin Liu, Senior Manager, Data Science & Engineering, Wealthsimple

Moderator: Yuri Levin, Professor of Management Analytics, Smith School of Business, Queen's University

•    Does artificial intelligence increase productivity as expected?
•    What are the biggest challenges financial institutions face when implementing AI solutions?
•    How can human expertise be best combined with AI to create a more robust financial ecosystem?
•    Implementing a Data-Driven approach

•    Identifying the current challenges facing by leaders, such as rising competition, security threats, and customer experience demands. 
•    The emergence of Artificial Intelligence as a game-changer in this environment 
•    Guiding leaders on evaluating their current processes to identify areas ripe for AI integration 
•    Encouraging leaders to embrace AI as a strategic tool for driving innovation and growth 

Panelists:
Amit Satpathy, Director of Artificial Intelligence, Munich Re Canada
Amin Atashi, Senior Machine Learning Engineer, The Globe and Mail
Justin Javorek, Digital Experience Design Leader, BELL
Roma Kojima, Senior Director, Enterprise Audience Data, CBC/Radio-Canada

Moderator: Paul Beaton, Sr. Manager, Data Science & Analytics, Rogers Communications

•    Evolution of Generative AI in the Workplace - From Personalization to Impersonation: Exploring the shift from AI enhancing personalized experiences to AI performing tasks that mimic human behavior, with cross-industry examples showcasing AI's diverse applications.
•    Human-AI Collaboration - Enhancing Capabilities and Ethical Considerations: How AI complements human work to increase productivity and efficiency, while addressing ethical concerns about AI impersonation and maintaining human interaction.
•    Future of Work- Adapting to and Predicting Change: Strategies for adapting to the evolving AI landscape and potential future scenarios, from utopian collaboration to dystopian job loss.

•    AI-Powered Hotel Review Summarization: Using Generative AI to simplify hotel reviews and improve customer decision-making with concise and sentiment-rich summaries.
•    Automated Descriptions and Information Extraction: Leveraging AI to generate hotel brand and neighborhood descriptions, and extract pet policies, enhancing the customer’s booking experience.
•    Personalized Travel Recommendations with GenAI: How AI can help travelers find the best neighborhoods and experiences tailored to their preferences in large cities

•    IAMDWarrior is MunichRe's upgraded machine learning platform is based on open- source stack and applies Gitops principles to machine learning in production. 
•    At MunichRe we use Gitops for most of our work, and this has many benefits including traceability, consistency, enhanced productivity, and provides a single source of truth. 
•    We will take a deeper look at the process, architecture, and how IAMDWarrior is helping us speed up our ML work and how Gitops principles fit into this setup.

•    Introduction to Reinforcement Learning (RL): An overview of RL and how its interaction-based learning differs from traditional machine learning methods, enabling complex decision-making. RL is a key technology behind advancements like ChatGPT, where it helps models improve through feedback and interaction as opposed to labels and model retraining. 
•    RL in Ad Recommendations: Explore how RL excels in measuring long-term effects in advertising, crucial for optimizing user interactions in large-scale recommender systems.
•    Evolution and Future Directions: Trace the evolution of RL in recommendation systems from the 2000s to today, highlighting advancements by companies like Google, Microsoft, and TikTok, along with a discussion of current gaps and future trends in the field.