These are the 4 skills to look for when building an AI governance team

The question of AI governance has arrived in the C-suite. Top executives, along with technology, data privacy, risk, legal and security professionals are seeking insight into how other companies are tackling the thorny question of governing AI and determining which model is best for them.

It’s becoming clear that sweeping change is needed — and urgently. AI laws coming into force don’t offer an end-to-end blueprint to aid compliance that address all risks; they add to an existing list of established compliance obligations. What is different is that risk assessment and mitigation cannot be implemented inside traditional silos.

At the core of this is data, and we all know there is no AI without data. While the collection, use, processing, storage and disposal of personal data is already a highly regulated space, organizations need to stretch further to address how AI upends the traditional data risk landscape.

One of the factors complicating AI governance is that compliance specialisms typically sit in multiple parts of the business. As a result, their existing governance mechanisms and processes may be quite siloed, focused on identifying and mitigating specific risks without assessing the bigger picture outside of their narrow remit.

For example, data protection professionals are not equipped to identify potential intellectual property infringement during their privacy review. Equally, intellectual property lawyers are not best placed to opine on risks associated with personal data. But it is precisely these functions — data privacy, risk, legal and security — that must work together to address risks associated with AI.

Collaboration is essential. The best way to tackle this is to assign ownership of AI governance to the function that has the skill set to pull together a seamless and integrated approach and wrap around an appropriate operate model to ensure accountability.

It’s better to start early. Don’t wait until AI laws are passed in your jurisdictions to start the internal conversations you need to have about AI risks. The current challenges around intellectual property, data protection and product liability repeatedly appear in the press, and there are still a lot of grey areas. New governance models will take time, as will careful negotiation of organizational culture and politics, including associated funding to operationalize and scale a new governance model in line with strategic ambitions for AI.

Taking a skills-based assessment to who holds the reins of AI governance can offer clarity and inform an organization’s decision. These four skills are essential for an effective AI governance function:

AI will undoubtedly disrupt existing industries and roles will evolve across the business, requiring a reskilling of the workforce. It is essential to view AI governance as more than a tick-box exercise — it’s a chance to leverage AI to do things differently while providing strong oversight for people to trust the outcomes. As technology continues to evolve at pace, and as laws and regulations continue to move albeit at a slower pace, creating a multidisciplinary and agile team that is ready to enable the business will set you apart from the competition.

Wherever AI governance lands in your organization, mindset is everything. The more you recognize the skills needed and understand it’s a team sport, the more likely it will be that this new shape of governance is embedded in your organization, providing the right guardrails to accelerate innovation – which is what everyone really wants to achieve.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.