In a space defined by data-driven decision-making, AI seems like the natural companion for the modern Risk manager. Regulated firms stand to gain a great deal from embracing the transformative power of AI-enabled tools, but persistent barriers are preventing widespread adoption.

What do these barriers look like? What’s next in tech? What does AI mean for Risk Management in the future? Too many questions? There’s a lot to think about.

Barriers to Adoption

AI is no stranger to regulatory turmoil, partly because the tech itself is always changing, largely because there’s a lack of concision regarding how it should be regulated in the first place. The UK’s ‘Pro-Innovation’ approach was met with some concern, notably from the Guardian, who highlighted that the initial white paper might already be out of date.

Other major barriers include cost, reliability, a lack of specialised and relevant skillsets to operate the AI, public backlash, or a lack of access to data sets, to name a select few.

AI has made some big promises (we see them in action every day), but UK businesses are yet to fully capitalise, even in the midst of a productivity crisis.

Ethical Considerations

From Healthcare and FinTech to Logistics and Marketing, AI is reshaping the world’s most impactful industries by the minute. Whether it’s the power to enhance disease detection, combat cybercrime or optimise logistics operations, an AI-enabled workforce represents a shift in the way we work for the best, provided we can conquer the ethical hurdles.

Currently, mitigating bias is one of the major stumbling blocks. How exactly do we ensure bias AI doesn’t perpetuate inequitable outcomes?

AI is only as bias as the humans who build the data sets that it’s based on. Even if there’s no explicit bias on show, unrepresentative data sets can lead to biased outcomes – take facial recognition systems for example, its infamous misclassification of minority individuals has grave consequences in the legal and healthcare spaces, and it’s not a deliberate move by sinister data engineers.

The lack of diversity in the teams (and the data sets they’re working with) building the AI is a major source of the problem. How can a team with no experience with diversity build AI that benefits a diverse population?

Through bias training, greater representation, and bias-detection tools, Risk Management has the opportunity to improve the use case for AI, realigning its scope to account for the needs of a broader population.

The Changing Role of Risk Management

Risk Managers are naturally future-facing, it’s the only way to pre-emptively combat threats. In today’s world, they face a volatile tech future. Those with an understanding of the tech space and a versatile, adaptable, skillset are in high demand these days – businesses are more complex than ever, and the number of career opportunities for Risk Managers is rising.

If you’re interested in stepping into a high-growth, tech-enabled Risk Management Job, the team at Broadgate are here to help. Our specialist consultants have a wealth of experience in connecting incredible individuals with environments in which they can flourish. Reach out today to learn more about our diversity-led hiring methodology, our insights network, our people, passion and expertise.