Opening & Welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group

Practitioner Innovation Keynote: The evolution of AI and how to generate value from your investment

• What are the current trends in AI and where can Generative AI deliver results over traditional AI?
• How should firms balance the opportunities with the risks to get maximum value from AI?
• How should firms plan for workforce transformation and engagement?
• What are the key requirements (people, process, technology) for successful AI
• What role will agents play and how can they add value?
• What are the metrics to measure success?

Panel: From promise to value – How to move GenAI from POC to production

• What applications/use cases are mature enough to move into production for internal or external use?
• What are the key challenges of moving from a proof of concept (POC) into production and how can these be addressed?
• Embedding ML Ops: How should firms address the technical complexities and workflow integrations when moving into production?
• How should firms organize governance and teams to allow central control/oversight, whilst also allowing business units to initiate applications?
• Who is in the Generative AI team – what are the key skills and capabilities required?
• How can costs be optimized and what metrics should firms use to measure success?

Keynote: Delivering value with GenAI – Real world case study

Panel: Data as a differentiator – How to build a strong data foundation for AI

• How can firms improve data discoverability and accessibility for continuous data curation?
• How should firms address data inconsistencies and ensure clean and high quality data sets for AI model inputs. How can AI tools be applied to assist with this?
• What are the critical components of a robust data governance framework that can support reliable AI models?
• Expertise and skill sets are cited as a key concern for successful AI: What are the key skills and capabilities needed and how should these be sourced and retained?
• Working with 3rd parties: How should firms manage external (cloud) data vs. internal (on-prem) data with regard to data & model ownership, vendor contract compliance and risks around operational resilience?
• How can firms pull all of this together to ensure responsible use of AI and a positive approach to privacy and ethics?
• How should firms continuously manage and curate data sets to ensure high quality and accurate model results?

Case study: Extracting structured data from unstructured data

Panel: Adopting AI tools and models into existing infrastructures and workflows

• Build vs. buy AI apps: What are the advantages and limitations of each approach?
• How best can firms evaluate existing third-party tools and platforms in the marketplace?
• What are the challenges of integrating AI into existing technology infrastructures and workflows and how can these be addressed?
• What are the roles of cloud and open source technologies in adding AI to existing infrastructure and workflows?
• What are the concerns about the carbon emissions cost of adopting LLMs and GenAI apps and how can they be approached?
• How should firms concurrently balance the costs, ethical risks and environmental/carbon impacts of AI?

Keynote: A Regulator’s perspective on AI

• How does the current regulatory framework support AI and is any further clarification needed?
• How is the Regulator continuing to invest in data and technology to support digital markets and responsible AI?
• What are the regulatory expectations around data quality and data lineage for AI and how should firms prepare?
• What are the priorities and key messages for firms as they develop AI compliance strategies?

Panel: How to manage Shadow AI with proactive AI governance

• How do financial institutions define and find shadow AI in their organizations?
• What are the business, operational and compliance risks of Shadow AI?
• How can these risks be managed – ban shadow AI or implement controls as across the rest of the organization?
• How can firms develop AI policies and education that discourage or prevent the creation of shadow AI?
• Where shadow AI is allowed, what tools are needed to offer a safe and responsible user experience?
• Can shadow AI exist in a wider organization of AI and, if so, how can it be governed without limiting its creativity?
• Ultimately, what are the benefits of shadow AI and do these outweigh the risks

Presentation: Model risk management – how should firms test, validate and manage model performance for AI

• What are the guiding principles, regulations, and frameworks for managing model risk in AI?
• What challenges do these present and how can they be resolved?
• How can the explainability of AI models be ensured?
• What methods can be used to validate AI including machine learning and GenAI?
• How can the performance of a Gen AI application model be measured?

Hear real world applications of AI and how it is being applied for efficiencies and business value.

Closing Keynote – Big Tech: The future of AI

• What’s coming next? How will AI continue to shape capital markets?
• What can we learn from other industries?
• The intersection of AI and quantum

Networking Drinks Reception

Ends 

Agenda subject to change