Artificial intelligence has moved from experimental use cases to systems that shape access to healthcare, financial products, and employment. As the impact widens, regulators and standard setters have intensified their demands for governance. The European Union’s AI Act introduces binding obligations, including mandatory documentation, risk classification, and post-market monitoring. ISO/IEC 42001:2023 establishes a management system standard for AI governance, drawing parallels to how ISO 27001 reshaped cybersecurity practices. In the United States, the National Institute of Standards and Technology (NIST) has advanced the AI Risk Management Framework as a guide for organizations to map, measure, and manage AI risks.

The regulatory trajectory is unmistakable. AuditBoard’s 2024 Blueprint to Reality survey found that while awareness of regulatory expectations is high, only 25 percent of organizations report fully implemented governance programs (AuditBoard, 2024). PwC’s 2024 Responsible AI Survey reached similar conclusions, noting that a majority of companies have conducted preliminary risk assessments, but few have integrated oversight into enterprise-wide operations (PwC, 2024). This gap between awareness and readiness is not a minor vulnerability. It means that when regulators demand evidence, many organizations will struggle to produce defensible documentation.

Market analysis reflects this urgency. Future Market Insights projects that enterprise AI governance and compliance spending will grow from 2.2 billion USD in 2025 to 9.5 billion USD by 2035 (Future Market Insights, 2024). Grand View Research similarly highlights accelerating demand for AI governance solutions, noting that drivers include not only regulation but also the rising need to manage reputational risks and preserve consumer trust (Grand View Research, 2024). The market growth is not speculative. It signals that organizations are already reallocating resources toward governance as a license-to-operate requirement.

Yet governance is complicated by the fragmented regulatory environment. The EU AI Act requires risk tiering and Annex IV technical documentation. ISO/IEC 42001 emphasizes organizational structures and continuous monitoring. The NIST AI RMF encourages voluntary adoption of risk management functions. Multinational companies must reconcile these frameworks, ensuring that evidence produced for one regulator can satisfy others. Research by the OECD on trustworthy AI has underlined the costs of fragmentation, warning that lack of alignment increases compliance burdens and slows adoption of best practices (OECD, 2023).

Firms also face a shift in what regulators demand. Policies and principles are no longer sufficient. Supervisory bodies increasingly expect proof that governance is embedded in practice. The Financial Reporting Council in the United Kingdom observed in its 2023 monitoring that firms adopting AI tools for audit were not adequately measuring the impact on audit quality, underscoring the need for evidence beyond declarations (Financial Reporting Council, 2023). Regulators are converging on the expectation that controls must be accompanied by verifiable artifacts such as system logs, incident reports, and bias test results.

Mitigation requires a layered strategy. Horizon scanning should be institutionalized so that organizations can anticipate obligations before they become enforceable. Evidence libraries should be developed to centralize artifacts such as model cards, bias assessments, and escalation records. Cross-mapping frameworks is essential; each artifact should be linked to overlapping requirements across the EU AI Act, ISO/IEC 42001, and the NIST AI RMF to reduce duplication and ensure efficiency. Finally, organizations should conduct internal rehearsals, simulating regulator queries and escalation drills. Readiness is not demonstrated by having policies on paper but by the ability to deliver evidence under pressure.

The growth of the AI governance market is therefore not a trend but a reflection of necessity. Regulators are moving quickly because the stakes for citizens are real. Biased credit models, insecure health applications, and opaque hiring systems erode trust and create material harm. Organizations that treat governance as an external burden will remain reactive. Those that embed evidence-driven governance as continuous practice will be prepared not only for compliance but also for the broader responsibility of managing AI’s role in society.

References

AuditBoard. (2024). From blueprint to reality: The state of AI governance programs. AuditBoard. https://auditboard.com/blog/new-research-finds-only-25-percent-of-organizations-report-a-fully-implemented-ai-governance-program

Financial Reporting Council. (2023). The use of technology in the audit of financial statements. FRC. https://www.frc.org.uk

Future Market Insights. (2024). Enterprise AI governance and compliance market analysis. Future Market Insights. https://www.futuremarketinsights.com/reports/enterprise-ai-governance-and-compliance-market

Grand View Research. (2024). AI governance market size, share & trends report. Grand View Research. https://www.grandviewresearch.com/industry-analysis/ai-governance-market-report

Organisation for Economic Co-operation and Development. (2023). OECD framework for the classification of AI systems. OECD Publishing. https://oecd.ai

PwC. (2024). Responsible AI survey: How US companies are managing risk in an era of generative AI. PwC. https://www.pwc.com/us/en/tech-effect/ai-analytics/responsible-ai-survey.html