To say that the risk industry looks completely different today than it did a decade ago would be a dramatic understatement. Even prior to the pandemic, risk professionals were already having to cope with an ever-increasing plate of rapidly evolving priorities and responsibilities as the world has become more interconnected. Add in the enforced digital transformation efforts, supply chain bottlenecks and other problematic ripples that have stemmed from the pandemic era and the already-chaotic risk space has become even more untenable.
Because of the complexity of the risk management space, it has become extremely challenging for risk professionals to detect immediate threats—let alone avoid, mitigate and preempt those on the horizon. Moreover, while some risk teams have begun to become more data savvy, the deluge of unstructured data that is now flooding their way means that manual data analysis and processing just is not feasible. This is why more and more risk industry insiders are beginning to eye artificial intelligence and automation as a potential solution for their risk intelligence and reputation management pain points.
The following are three key steps risk management teams can take to ease their AI adoption and drive immediate success.
1. Shifting Mindsets and Prioritizing Clarity
Risk management has operated in very deliberate ways for decades. While this has resulted in established success, with the speed and expansion of the global business landscape, risk management has become even more of a 24/7 proposition than ever before. In addition, with data now a staple in many other business areas, leaders expect to see the same intelligence, agility and “scientific” results being produced by departments throughout their organizations. This is heaping pressure on risk professionals.
Adopting new technology is easier said than done, particularly when operations frameworks and workflows have remained largely unchanged for decades. And while onboarding new technology can come with growing pains and headaches, the biggest barrier teams face when undertaking digital transformation and especially AI adoption is in changing mindsets. Fortunately, these can be overcome but requires incredibly clear guidance, goals and procedures to get right.
For example, it can be natural for human professionals to feel as if their insights are being replaced by technology. This simply is not the case. For risk management to be successful, it is imperative that human intuition and input be the guiding force when it comes to decision making. Technology is simply the vehicle that allows for increased visibility and agility. Therefore, to overcome this hurdle, organizations need to lay bare this fact and reinforce it throughout onboarding so that they can overcome any cynicism and drive optimal buy-in throughout the workforce.
2. Look for Transparency and Explainability
Two of the biggest issues that teams have when onboarding AI technology is when it comes to transparency and explainability. Because of how rapidly AI can make decisions, transparency and explainability are essential both in terms of internal success as well as making sure that no regulatory or compliance issues crop up.
The insights that you get from AI are only as good as the frameworks that it adheres to. And while companies put a huge amount of effort at the outset into ensuring that their technology does not go awry as it learns and evolves, decisions and insights can become skewed. Therefore, organizations need to have tools in place that provide a “glassbox” that allows for easy understanding of how an insight was arrived at. From there, tools need to allow for easy tweaking should bias have occurred in the decision-making process.
The benefits of this are two-fold in that it allows internal professionals to get a better grasp of how decisions are being made, while also allowing companies to actively avoid falling afoul of ethical standards. Additionally, it allows business to confront the historic fears that surround AI adoption in relation to trust.
3. Be Open to Experimentation
To drive the maximum returns from AI technology, it is imperative that organizations be open to experimentation. AI is constantly evolving and shifting. This can be unsettling for “traditionalists” that prefer to have a consistent, hardened plan on how workflows are handled and how AI is used.
Through AI, businesses can track emerging risk with far greater clarity and visibility than they ever have before, thus allowing risk teams to finally uncover “unknown unknowns.” Thus, risk professionals should not be afraid to dig as deep as possible for potential insights. AI technology allows risk professionals to examine suppliers and other risk sources that they never would have had effective access to before.
Granted, not every investigation and analysis will ultimately result in a treasure trove of insights. However, what this experimentation will allow companies to do is to figure out with certainty that no issues exist, rather than relying on guesswork and hoping that nothing crops up down the road.