In this second of a three-part series on how digitisation is changing how we manage risk, Gareth Byatt, principal consultant at Risk Insight Consulting, considers how risk practitioners can help organisations to leverage the opportunities presented by artificial intelligence and the automation age.
The new digital age offers countless opportunities for people to manage risk – and create value – in new and innovative ways. Artificial intelligence (AI) and process automation are integral to this, and their adoption is growing as tangible benefits are being achieved.
Across many industries and sectors, robots and computers are performing a range of work more accurately, faster and at lower cost than people can (or used to) do. Operational risk is changing as a result. While automation is achieving a reduction in errors and improvements in productivity and safety, people retain a crucial role as decision makers. Decision-making with data and algorithms from AI and process automation is a new frontier for the risk profession. It can lead a step change in how we use risk management to create value.
What does this mean for the risk management profession? I believe that in coming years, we will see a rising demand for ‘risk intelligence and data analytics’ skills in advertised risk jobs. If you can demonstrate real-world use of AI and technology to create value by managing risk, you will gain a competitive advantage in the job market.
Risk practitioners should be helping teams, from executives to the frontline workforce, to use data from AI and process automation to spot previously unseen risks. They can then use established techniques such as risk appetite principles and scenario analysis to aid decision-making.
Consider the following examples from the retail and automotive industries of how risk practitioners can help create value by being embedded in business decision-making.
Retail is using AI and automation in various ways, in store and online (kiosks, customer buying behaviour, and so on.). Risk practitioners should liaise with analysts in retail operations teams to understand customers’ buying behaviour, supplies inventories, logistics and sales. Working together, they can develop leading edge ‘what if’ scenarios to determine new insights and achieve new levels of ability to take and manage risk, with new and innovative controls developed to achieve competitive advantage.
The automotive industry offers a high-profile example of using AI for self-driving road vehicles. Risk practitioners can be integral to an automotive manufacturer’s strategy. Their input can range from strategy risk profiling to manage the risk of introducing AI to its product range, to helping to design appropriate controls to manage data privacy and cyber risk. They can work with engineers to review ‘what if’ scenarios and risks to ensure the programming of AI is appropriate.
Risk practitioners can also help servicing and roadside assistance departments to use risk appetite thresholds for vehicle parts performance, to be alerted to potential faults and problems. At a broader transportation level, risk professionals can help the bodies that own and operate transport infrastructure to use data to reduce the risk of traffic congestion and to improve safety.
In my previous column, I stated that risk management remained a mostly qualitative management practice in many industries. This is changing. While qualitative risk reviews are still important, we need to get smarter at helping organisations to use data to manage risk.