Expert: Craig Barraclough, Director, Finovation Consulting Facilitator: Brod Whiting, JoynedUp
Headlines:
- The current state of AI tools, their adoption in consumer and business spaces, potential use cases, challenges, and considerations around implementing AI solutions
- The proliferation of generative AI tools like ChatGPT in the last 12-18 months, primarily in the consumer space, but with growing interest in business applications
- The need for a balanced approach, leveraging AI's capabilities while preserving human expertise and client interactions
Discussion points:
Exploring AI Adoption and Use Cases
The p proliferation of AI tools, particularly in the consumer space, and explore potential use cases in the financial services industry.
The potential for productivity gains and cost savings through AI-enabled processes like meeting transcription, report generation, and compliance checks.
Concerns about the risks, biases, and regulatory implications of AI adoption.
Balancing AI and Human Expertise
Finding the right balance between leveraging AI's capabilities and preserving human expertise and client interactions.
The importance of understanding AI's limitations and appropriate use cases, while maintaining human oversight and decision-making in critical areas.
Vendor Selection and Implementation Challenges
The challenges of selecting the right AI tools, managing vendor relationships, and ensuring the longevity and reliability of solutions.
Concerns about the potential for vendor lock-in, rapid obsolescence of tools, and the need for robust risk assessment and assurance processes.
Data Privacy, Bias, and Ethical Considerations
The ethical implications of AI deployment, including data privacy concerns, potential biases in AI models, and the need for responsible and transparent implementation.
The importance of addressing these issues proactively to maintain public trust and ensure compliance with regulations.
Environmental Impact and Energy Consumption
The environmental impact and energy consumption associated with AI models and their training, including the significant energy requirements of AI-related activities and the need for sustainable solutions.
Key takeaways:
- Explore potential use cases for AI in financial advice workflows, such as meeting transcription, report generation, client analysis, and compliance checks
- Conduct a thorough risk assessment and develop robust assurance processes for any AI solutions being considered for implementation
- Establish guidelines and protocols to address data privacy concerns, potential biases, and ethical implications of AI deployment
- Investigate sustainable solutions and strategies to mitigate the environmental impact and energy consumption associated with AI models and their training
- Develop a framework for evaluating and selecting AI tools, considering factors such as vendor reliability, longevity, and integration with existing systems
- Provide training and resources to help employees understand AI's capabilities, limitations, and appropriate use cases, fostering a balanced approach that leverages AI while preserving human expertise and client interactions
