Since the release of ChatGPT, a flood of AI tools has entered the enterprise market, which continues to change business processes and procedures. The hype of being an “AI-Company” has subsided, yet AI remains an imperative for many organizations. The rapid evolution of technology and the impacted business functions have made AI governance a critical component for organizations. Whether it is to harness the transformative power of artificial intelligence or to navigate complex ethical and regulatory challenges, boards cannot ignore the issue. Establishing and future-proofing a robust AI governance framework will remain challenging for many boards. Yet, it is a challenge we cannot ignore.
The Growing Imperative for AI Governance
Adopting AI across departments and business units has created a pressing need for comprehensive governance structures. As an evolving technology, AI can produce unreliable outcomes, especially when employees get complacent, and proper oversight is lacking. The ad-hock adoption of many AI projects is compounding the issue. These failures can cascade into reputation damage, financial losses, and increased regulatory scrutiny.
One of the first AI discrimination cases in the US should be a warning to all companies. The EEOC accused the iTutor Group of having their AI-powered recruiting software unlawfully reject female applicants over 55 and male applicants over 60. The company settled the lawsuit for $365,000 and made multiple commitments to improve its systems.
A better-known case involves Air Canada. The company’s virtual AI assistant provided incorrect information about bereavement discounts. The AI’s training data contained the policies from United Airlines. Ignoring the erroneous information, the airline denied a customer’s request for a price adjustment. A Canadian judge subsequently ruled against the airline, leaving them with the costs and the reputational damage resulting from the lawsuit.
Both examples show how AI errors can have significant legal and financial consequences. Without proper oversight, the risk manifolds.
Beyond Compliance: The Strategic Value of Good Governance
Regulatory compliance remains a fundamental driver for AI governance. However, forward-thinking organizations recognize that effective governance delivers substantial strategic advantages. A well-structured governance framework enhances the reliability of AI outcomes by establishing transparency, accountability, and quality controls throughout the AI lifecycle. This approach ensures AI systems operate on robust, unbiased data, leading to more trustworthy results.
Perhaps most importantly, AI governance creates a foundation of trust among stakeholders. Organizations can address the trust deficit surrounding AI technologies by implementing visible controls and processes that ensure data reliability and ethical AI use. This trust enables faster adoption, more efficient scaling of AI programs, and ultimately, greater competitive advantage.
Challenges to AI Governance
Despite its apparent benefits, implementing effective AI governance presents significant challenges for many organizations. According to recent benchmark data by the AI Governance provider ModelOP, 58% of companies struggle with integrating fragmented systems when adopting AI governance platforms. Additionally, 55% face difficulties replacing or scaling manual processes, while 53% encounter internal procurement and administrative hurdles.
The regulatory landscape is complex, further complicating governance efforts. Organizations must navigate an increasingly complex compliance environment with regulations like the EU AI Act, NIST AI RMF, and various state-level requirements emerging globally. This regulatory patchwork requires specialized expertise that many organizations lack internally, with 36% citing a shortage of skilled personnel as a significant obstacle to effective governance.
A Structural And Cultural Question
Many providers offer solutions for data warehousing and AI management. Yet, the issues of AI governance run deeper than the technological questions. Successful AI governance requires a well-defined organizational structure with clear leadership and responsibilities. It also requires a culture that balances trust with responsive management. The rise and fall of shadow IT in cloud computing can serve as an exemplary case study.
Organizations should establish specific roles focused on managing and overseeing AI technologies. This structured approach ensures accountability and consistent decision-making across the enterprise. Developing repeatable AI policies also helps establish service-level standards, particularly in large organizations with diverse AI applications. Finally, transparent decision-making processes help departments follow best practices when aiming to implement AI within their area of expertise. They also ensure that the IT and requesting departments divide the evaluation work along sensible lines.
A Controlled Tomorrow
As AI turns from a hype topic into an everyday tool, we must recognize new technology’s unique challenges. Between changing regulatory actions, data security questions, and technological developments, many of us face a never-ending stream of information and questions around AI. A comprehensive AI governance framework allows companies to realize the transformative potential of artificial intelligence while mitigating its inherent risks. Organizations will build trust, ensure compliance, and ultimately drive greater value from their AI investments by establishing proper guardrails and procedures for AI adoption.