Artificial Intelligence Leadership Chasm: Methods for Business Heads to Bridge It
In a revealing 2025 AI Governance Survey, it has been found that while 82% of enterprises are using AI across various functions, only 25% have fully implemented AI governance programs [1]. This significant gap between AI adoption and governance maturity is a cause for concern, particularly for smaller firms that face unique challenges and heightened risks.
The survey and expert sources emphasize that core AI governance principles—accountability, transparency, risk management, and ethical use—apply equally to small and large firms [2]. However, smaller organizations often grapple with a lack of clear ownership, insufficient internal expertise, and limited resources [1]. Over 60% of AI incidents in the past year involved small and medium-sized enterprises (SMEs), underscoring the urgent need for tailored governance frameworks for these entities [2].
One of the key obstacles identified in the survey is the rapid pace of AI adoption outpacing risk management efforts, partly due to fragmented regulations and cultural barriers within organizations [1]. Mature AI governance, on the other hand, correlates with sustained AI initiatives and higher trust in AI applications [4].
To address these challenges, the survey provides several recommendations for enterprises, especially smaller firms. These include conducting honest self-assessments of current AI use and risks, aligning governance efforts with proven frameworks such as the AIGN Framework, ISO/IEC 42001, or OECD AI Principles, and using self-assessment tools to benchmark maturity and prioritize improvements [2].
Other recommendations include recognizing the growing importance of formal AI governance certifications as a signal of responsible AI leadership, going beyond mere compliance by embedding governance into daily operations and culture, and integrating monitoring tools for model drift, hallucination, and injection attacks directly into deployment pipelines [1].
The survey also highlights the importance of incident response plans tailored to AI-specific risks, such as bias, misuse, data exposure, and adversarial attacks. It is concerning that among small firms, 9% don't monitor their AI systems for accuracy, drift, or misuse, and only 36% have governance leads [1].
To ensure responsible AI leadership, organizations should mandate AI training for the entire workforce, including understanding of key frameworks like NIST AI RMF, ISO 42001, and applicable local and industry-specific regulations. Only 60% of companies have defined response playbooks for AI governance, and only 30% of organizations have deployed generative AI in production [1].
Industry-wide collaboration, tools, and templates can help minimize issues in AI governance. Larger enterprises are five times more likely than smaller firms to deploy generative AI in production, yet the absence of structured oversight often leads to preventable failures that can stall projects, erode stakeholder trust, and attract regulatory scrutiny [1].
The greatest barrier to stronger AI governance is the pressure to move quickly, with nearly 45% of all respondents citing this as the primary governance obstacle [1]. To overcome this, enterprises must prioritize AI governance as a performance enabler, weaving monitoring, risk evaluation, and incident management into engineering workflows.
In conclusion, the 2025 AI Governance Survey underscores the need for enterprises, particularly smaller firms, to prioritize AI governance. Successful AI governance requires clear ownership, adequate expertise, structured frameworks, and embedding ethical practices throughout business operations [1][2][4]. By following the recommendations outlined in the survey, enterprises can build a foundation for responsible AI leadership and create a safer, more trustworthy AI ecosystem.
[1] 2025 AI Governance Survey Report [2] OECD AI Policy Observatory [3] AIGN Framework [4] ISO/IEC 42001:2021 - Guidance on ethics for AI [4] NIST AI Risk Management Framework (RMF)
- Smaller organizations, which often struggle with clear ownership and sufficient internal expertise in data governance, should prioritize aligning their AI governance efforts with established frameworks like the AIGN Framework, ISO/IEC 42001, or OECD AI Principles to ensure accountability and transparency.
- To create a safer and more trustworthy AI ecosystem, smaller firms must embrace formal AI governance certifications as a sign of responsible AI leadership, and embed governance principles into their daily operations, culture, and engineering workflows, while monitoring the risks of misuse, data exposure, and adversarial attacks.
