AI governance frameworks could help organizations learn, govern, monitor, and mature AI adoption and scale. While there is no one-size-fits-all approach, organizations can consider adopting processes to mitigate risk. This session will explore:
- What an effective AI governance and risk management framework looks like in practice
- The core principles that can be operationalized
- Implementation of a functional framework irrespective of available resources and organization size
- The most vital aspects of a framework and how to tailor them based on need
- Generating maximum additional value as a result
Gurleen Virk
Ken Archer
Daniel Wu
Daniel Wu is an accomplished technical leader with over 20 years of expertise in software engineering, AI/ML, and team development. With a diverse career spanning technology, education, finance, and healthcare, he is credited for establishing high-performing AI teams, pioneering point-of-care expert systems, co-founding a successful online personal finance marketplace, and leading the development of an innovative online real estate brokerage platform. Passionate about technology democratization and ethical AI practices, Daniel actively promotes these principles through involvement in computer science and AI/ML education programs. A sought-after speaker, he shares insights and experiences at international conferences and corporate events. Daniel holds a computer science degree from Stanford University.