The Competitive Advantage of AI Governance

The Competitive Advantage of AI Governance

The Gist

  • Governance is critical. Without proper AI governance, companies risk compliance failures and loss of customer trust.
  • Strategy first. A well-defined AI strategy and governance framework can be the difference between innovation and chaos.
  • Compliance drives trust. Strong AI governance not only meets regulatory requirements but also enhances customer trust and competitive advantage.

A recurring trend with most technologies is that they generally find their way into widespread adoption long before laws can be established to regulate them. Governance of technologies — especially those put in the hands of the masses — is critical for keeping people, companies and their data safe and secure from misuse.

AI appears to be no different. But is it a new dog with old tricks, or just an old dog with new tricks?

Though AI has been in existence for about 60 years, its numerous fits and starts have kept it pretty low-profile until recently. Since ChatGPT emerged in late 2022, many companies have begun developing AI models for a variety of applications.

Use of this technology can bring with it a number of risks, including exposing sensitive data, violating intellectual property laws, producing results that are patently wrong and running afoul of both existing regulations and those in development. AI must be applied carefully.

Why AI Governance Matters

Until recently, AI was used almost exclusively by data scientists and other data experts. Because the data was typically only used internally to improve products and services, or to analyze general customer behavior, there wasn’t a pressing need for data governance frameworks. With generative AI becoming ubiquitous in much of the workplace, that has now changed.

Nowadays, most companies recognize that in order to keep up with their peers, they need some form of generative AI or large language models (LLMs) in their products or services — and with that comes requisite governance.

One problem: AI governance programs are yet to be formed, let alone finalized. The underlying technology of AI is still evolving, making it challenging for legislative bodies to firmly settle on a compliance approach.

But companies can’t wait years to find out what the final frameworks look like. Instead, they must immediately begin to put in place a workable, defensible, scalable, and, ultimately, flexible framework for overseeing and managing AI use within their organizations. Before deploying an AI model, companies need to have a proper understanding of the lifecycle of those models within the context of their business, and how they work. And of course, all AI projects should go through the usual governance chain of review from the compliance, legal and info-sec teams.

Related Article: Generative AI in Marketing: Boost or Bust for Your Department?

Establishing an AI Governance Framework

Proper data governance addresses data quality, data security, ethical use and privacy protections, among other things. Depending on your business and the nature of the data you use, AI governance might be an even greater (and more resource-consuming) obligation than data governance.

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