In an earlier post, I discussed the importance of governance in Artificial Intelligence (AI) and how, arguably, aside from the initial hurdle of getting started, governance is one of the most significant barriers to adoption, particularly in large enterprises. Concerns such as liability, intellectual property and the risk of introducing incorrect or biased information into AI models are often cited as the biggest impediments to AI integration at scale.

My previous advice encouraged experimentation, emphasizing the importance of gaining momentum, learning from efforts and celebrating small wins. However, as promised, this follow-up aims to define what governance in AI really means. The first paragraph above provides some context, but let’s dive deeper.

Governance in AI refers to the set of practices, principles and processes that an organization establishes to develop, deploy and manage its Artificial Intelligence systems. In practice, this encompasses all systems that provide data to the AI, all outputs and outcomes generated by the AI and all stakeholders – individuals, teams, departments – whose jobs, roles and successes are influenced by AI. Since AI is fundamentally built on data, this broad reach underscores the technology’s far-reaching impact.

AI is still relatively new in the wider society and not fully understood. It is imperative that the governance framework adopted by an organization is designed with a clear end-goal in mind, and is implemented transparently, with widespread knowledge across the organization. This approach helps the AI initiatives align with organizational acceptance.

This does not imply that organizations should become paralyzed by over-analysis, as failing to implement AI would likely mean falling behind in today’s competitive landscape. The key to success lies in balancing careful governance with agile action. Trust is a vital component of AI adoption, and proper governance fosters trust by ensuring transparency and accountability.

Additionally, AI systems must be regularly monitored and evaluated to ensure they continue to function as intended, without introducing unforeseen risks or biases. This ongoing governance is essential for maintaining the public’s trust in AI technologies, as well as ensuring compliance with evolving regulations.

AI governance is multifaceted, but definitely possible and practical. Keeping a human in the loop is a check against an unintended consequence. Diverse stakeholders need to focus on long-term goals and organizations must engage to harness the full potential of AI while minimizing risks and fostering trust.

Things That Need To Go Away: The ‘AI Can Wait’ Attitude