Enterprises are discovering the power of AI to transform their operations. At the same time, they’re taking measures to put governance into place to protect their people, processes, and brand reputation.
AI transformation has moved out of the experimental stages and become more of a norm in most businesses. Indeed, recent research suggests that at least three-quarters of all organizations have adopted AI in some way across their operations. However, companies haven’t quite caught up when it comes to developing AI governance policies, including those at the enterprise level.
Why have enterprises lagged behind? For one, AI governance is difficult to define. As one media outlet reported, 44% of businesses admit to having policies covering AI, but 76% of them say their policies don’t address data quality.
Another roadblock to developing AI governance policies is that people have different recommendations when it comes to what a solid policy would look like. Even experts across industries had differing opinions on AI and its governance when they spoke at a global event on the topic.
In other words, AI governance is changing and adapting along with AI itself. Consequently, it’s challenging for enterprises to understand how to approach governing the AI that’s becoming part of their operational infrastructure. However, it’s also necessary.
AI transformation without the guardrails of a solid layer of governance can open the door to preventable risks. In addition to exposing an enterprise’s private and proprietary data to the outside world, ungoverned AI tools and systems may make decisions that do not line up with the organization’s mission or values. In addition, AI can hallucinate and show bias, which can lead to unfair business practices.
For these reasons, some enterprises are building their AI governance alongside their AI transformation. And they’re gaining benefits because the governance is being created in the moment rather than after the fact.
What strategies are enterprises using to make certain their AI governance policies are intentional and appropriate?
They’re replicating best practices from other enterprises.
One way to fast-track AI governance policies is to replicate the basic frameworks being used by other enterprises. For example, plenty of organizations are working with consulting firms that have expertise in the area of AI governance creation.
The value of working with AI governance consultants such as EY is that the consultants can pull from countless experiences. That way, they can share their knowledge and insights. Plus, long-time consultants understand what the key components of an AI governance framework look like. This gives their clientele the ability to customize scalable AI governance policies and procedures that are more likely to improve performance while maintaining strict governance.
They’re staying on top of the AI in the organization.
It’s not possible to draft governance-related documentation without having a deep understanding of the AI that’s being used operationally within an organization. Accordingly, enterprises are making an effort to gain visibility to all AI products being used by their teams.
This isn’t a small task. For instance, employees may be using AI chat tools to help them complete their work. Since they’re using the applications without direction, they may be exposing the company’s data without realizing it. But by finding out which AI products are entering into an enterprise’s workflows, the enterprise can develop frameworks that are more pertinent to the needs of its team members and departments.
They’re making changes based on other enterprises’ problems.
An AI governance policy needs to be strict, but it can’t be too unbendable in terms of updates. In the quickly changing world of AI, governance needs to be able to adapt rapidly. Therefore, policies must be structured but regularly updated, frequently based on news from AI transformation issues faced by other companies.
Of course, this poses another issue, which is how to successfully update employees about changes in an AI policy. Ideally, this can be solved with constant communication to all team members, including those who might not be using much AI. By spreading knowledge about policy changes right away, enterprises can avoid hearing excuses from teams that they didn’t know about an important update.
They’re turning over governance to one area.
One of the most important elements to any AI policy is that it is clearly owned by a specific team or individual. In fact, accountability is a huge asset because it assigns responsibility. And when responsibility is established, it can then be tracked.
Without this type of oversight, an AI governance policy could easily be put aside and neglected. By making it clear that AI policies belong to a certain group, enterprises can avoid their policies becoming outdated and count on those policies protecting them and their stakeholders
AI transformation is happening at enterprises, and it’s happening fast. With the right type of governance, large organizations can keep pace without losing control or competitiveness along the way.
