As the power and possibilities of AI have evolved, so too has the potential for misuse and risk. To create responsible AI, it is critical for companies and governments developing, deploying or using AI to enact strong AI governance practices.

Strong AI governance requires a holistic approach that integrates both organizational and AI model governance, encompassing everything from foundational principles to regulatory compliance and everything in between. While there’s no question that such a robust approach is effective, it also requires an ongoing investment of both human and capital resources that might seem difficult to justify.

But the truth is that AI governance is one corner that cannot be cut. Beyond the obvious fines for noncompliance with regulatory requirements, there are several other potential costs of not implementing an AI governance program that makes AI governance a must-have for any organization looking to scale AI.

One of the major aims of AI governance is to provide assurance that AI systems are operating as the organization intends, as their stakeholders expect and as required by relevant regulations. “AI governance has typically been grounded in regulatory compliance. However, that’s only one important facet,” explains Lee Cox, Vice President for Integrated Governance and Market Readiness at IBM.

AI governance establishes accountability by defining organizational principles for responsible AI, assigning responsibility throughout the AI development lifecycle and operationalizing those principles into development and release cycles. This balances the business value promised by AI with the need for oversight and risk management.

When those accountability mechanisms are not in place, there is a greater risk that systems will not operate as intended or expected. This can lead to reduced adoption rates, compromised ability to operationalize and scale, decreased return on AI investments and increased risks and frequency of system failures. “But by establishing a comprehensive and integrated governance program, you can help determine acceptable operating parameters, including risk and policy alignment,” Cox clarifies.

71% of CEOs believe that establishing and maintaining customer trust will have a greater impact on their organization’s success than any specific product or service, and AI governance is a critical enabler of customer trust. When AI is ungoverned, it’s not only the success of individual AI projects that’s on the line: it’s the reputation of the business.

If an organization’s AI system does not operate as intended, the loss of trust can extend across the ecosystem of stakeholders. In addition to lost customers, there can also be lost talent: 69% of workers report being more willing to accept a job offer from an organization they consider to be socially responsible. Customer and employee attrition can be red flags for investors, leading to compromised shareholder confidence.

All of this can rapidly impact an organization’s public perception and competitive advantage, potentially resulting in downstream effects such as missed opportunities for growth, innovation and market leadership.

“There’s a saying that trust is earned in drops but lost in buckets. And it’s absolutely true,” says Christina Montgomery, Chief Privacy and Trust Officer. AI governance helps organizations to fortify their corporate character by systematically embedding ethics into technology lifecycles, managing regulatory compliance across jurisdictions and enabling continuous system monitoring, all of which help maintain trust.

As generative AI unlocks new use cases, it touches human lives more often and in new ways. This makes it even more critical that AI is built and used in alignment with human values and ethical expectations. To that end, AI governance establishes frameworks and guardrails that help ensure AI’s impacts on society are positive. In their absence, there can be inadvertent harm to people or to the planet. While these harms can be more general and more difficult to measure, they matter.

“Effective AI governance is a delicate balance of people, processes and technology,” reiterates Phaedra Boinodiris, Trustworthy AI Leader for IBM Consulting®. “It demands a symphony of organizational oversight, technical tools and educational initiatives, harmonizing societal needs with technological advancements to enable responsible AI adoption.”

AI governance helps to mitigate potential societal harms by establishing organizational baseline expectations for explainability, transparency and fairness, among others. It also facilitates the curation of a company culture that centers on trust. This includes creating build teams that are diverse, inclusive and multidisciplinary to more holistically consider and address layers of potential AI impact.

Ultimately, the business justifications for AI governance are not just about aligning with regulatory requirements. They are also about meeting stakeholders’ growing expectations for responsible development, deployment and use of technology. Those expectations have never been higher than they are today.

At IBM, we understand this imperative because we’ve been advancing along our own AI ethics journey for almost a decade. Our organizational governance mechanism, the IBM AI Ethics Board, works hand-in-hand with our AI model governance mechanism, the Integrated Governance Program (IGP), to enable holistic AI governance at IBM.

“By consolidating to a single and scalable framework, we not only reduce waste, minimize overhead and maximize return on investment (ROI), but also promote transparency, accountability and fairness,” explains Steven Eliuk, IBM AI Ethics Board member and Chief Technology Officer for Data, AI and Governance about IBM’s Integrated Governance Program. “It is a true testament to the power of AI at scale, grounded in AI ethics.”

Having both organizational and AI model governance mechanisms in place and integrating them so that they support and build on one another help enable organizations to deliver AI with speed and trust. In fact, rooting an AI governance implementation strategy in value generation can help organizations holistically measure the tangible and nontangible ROI of AI governance. From cost and risk mitigation to long-term value creation, it's increasingly clear that good governance is good business.

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