Organizations can view upcoming AI regulations as a cost to be endured, or as an opportunity to gain competitive advantage. We’ve seen this movie before.
In the 2000s, governments around the world put in place regulations for environmental and social responsibility, such as the EU Restriction of Hazardous Substances (RoHS) directive or, in the US, rules in the Dodd-Frank Act around reporting on components containing so-called conflict minerals.
The new regulations caused a great deal of turmoil as companies worked to remove hazardous or unethical materials from their products and supply chains. Over time, we saw many organizations adopt a “check the box” approach to doing the minimum necessary to meet new regulatory requirements. They filled out forms with information that quickly became stale, then repeated the same process over and over again, with no lasting business value, only ongoing compliance costs.
On the other hand, we also saw forward-thinking organizations adopt full material disclosure (FMD) programs that provided transparent reporting to regulators and consumers. These programs ensured regulatory compliance and gave consumers confidence in the safety and ethical sourcing of the products they were buying. This reduced legal risks and enhanced brand reputation and customer loyalty.
But FMD programs also delivered operational benefits. With better understanding of the composition and environmental impact of the materials in their products, companies could identify risks and opportunities for improvement, reduce waste and emissions, comply with regulatory requirements, and respond to customer demands for more sustainable and ethical products. Full material disclosure also encouraged higher levels of collaboration with suppliers and other stakeholders, leading to more efficient and resilient supply chains.
History Repeats
History is now repeating itself. In place of environmental and social responsibility, substitute in safe and responsible AI. Companies that put in place the people, processes and technologies necessary to meet new regulatory requirements around AI also have an opportunity to improve the business.
For example, a robust model catalog that makes it easy to produce an inventory of models in production can also facilitate reuse of model assets and collaboration among different stakeholders. Proactive compliance efforts also provide a solid foundation for exploring new use cases, partnerships and collaborations that foster innovation while respecting legal and ethical boundaries.
The journey to compliance with AI regulations involves understanding your current capabilities and how they map to regulatory requirements, identifying gaps and building consensus among all the stakeholders to fill in those gaps. But the first step in that journey is recognizing that while compliance is inevitable, embracing it proactively and realizing operational efficiencies is a choice.
Learn more about how Verta helps organizations comply with AI regulations.
