A 2024 survey by CSIRO and Alphinity revealed that only 40% of company boards had a director with expertise in AI ethics, and few had public AI policies. This highlights a growing gap between fast-moving AI technologies and corporate governance structures.
“A recent report by CSIRO and Alphinity indicates a significant gap in AI ethics expertise among company boards, with only 40% having a director knowledgeable in AI ethics and few companies having public AI policies.”
Gap analysis in AI governance is the process of evaluating current practices against ideal frameworks or regulatory requirements. It helps identify missing policies, incomplete processes, or misaligned values across AI lifecycles.
Why gap analysis matters in AI governance
Gap analysis provides a structured way to uncover risks, compliance failures, and weak ethical safeguards. As AI deployments scale, governance teams must ensure that controls around data, models, and decision-making align with regulatory, ethical, and operational expectations. Without this step, companies risk legal exposure and reputational damage.
Key components of AI governance gap analysis
Each gap analysis should examine AI governance from a policy, technical, and operational perspective. Common components include:
-
Baseline assessment: Reviewing existing AI policies, tools, roles, and procedures.
-
Standard alignment: Comparing the baseline with known frameworks such as ISO/IEC 42001.
-
Gap identification: Highlighting areas of non-compliance or incomplete practice.
-
Action planning: Designing interventions, such as policy updates or tool integration, to close the gaps.
Practical examples and applications
Companies in finance often run governance gap analyses when introducing new risk-scoring models. If the model lacks transparency or bias controls, a gap analysis would flag the absence of documentation or fairness audits. Another example is a healthtech startup building a diagnostic AI that doesn’t yet have a risk classification or external review process. Identifying this early avoids problems when seeking certification or entering partnerships.
Best practices for AI gap analysis
Conducting a useful gap analysis is as much about collaboration as it is about technical knowledge. These tips can help:
-
Involve diverse roles: Governance needs input from data scientists, product managers, legal, and operations.
-
Use live systems, not just policy docs: Sometimes systems operate differently than they’re written to.
-
Prioritize gaps: Some issues may be technical debts while others are compliance-critical.
-
Follow up: Assign owners and track whether improvements are actually implemented.
FAQ
What is the main benefit of gap analysis in AI governance?
It helps organizations identify where their current AI practices fall short of expected norms or standards, enabling early corrections before issues grow.
How often should gap analysis be done?
At minimum, once a year. But it’s also essential after major AI deployments, regulatory changes, or incidents involving model failure or bias.
Can startups use gap analysis effectively?
Yes. It doesn’t need to be formal or expensive. Even a checklist or internal audit can help early-stage companies understand what they’re missing.
Which standards are most relevant for gap analysis?
In addition to ISO/IEC 42001, consider frameworks from NIST, OECD, and sector-specific ethics guidelines like High-Level Expert Group on AI’s assessment list.
Summary
Gap analysis offers AI governance teams a proactive way to identify weaknesses before they become liabilities. Whether it’s missing documentation, unclear responsibilities, or unassessed bias, closing these gaps improves trust, safety, and regulatory readiness. It’s not a one-time fix but a recurring habit that helps future-proof AI systems
