While the rise in adoption of AI has resulted in undeniable business value addition, it could also lead to adverse -- and sometimes dire-- side-effects in the absence of continuous risk assessment. The entire scope of AI-induced risks and their impact on individuals, organizations, or society is still speculative at best. The cost of erroneous decisions taken by AI can range from unrecognized revenue to loss of reputation and public trust. Many organizations are unreceptive of AI due to the fear of regulatory backlash. The HOLMES Advisory Board met in March 2020 to share their diverse experiences and offer best practices for governance, risk and compliance in the context of AI productionization at scale.
The industrialization of AI is set to grow exponentially in the coming years. Enterprises must treat risk management as a critical requirement supporting AI innovation and should invest in controls (process and technology tools) ahead of time. Continuous monitoring and metering of AI/ML models is one such control to avoid incorrect decisions taken by AI. Organizations investing in maintaining robust AI systems should consider the risk appetite and the business value associated with the strategic objective.
A comprehensive risk management framework facilitates innovation and guides investments through informed decision-making. While it is true that AI poses real, albeit relatively unknown challenges to its adopting enterprises, the need of the hour is to collaboratively find a solution that enhances governance, lessens risk, and ensures compliance.