Growing AI Review Industry: Strategies for Business Heads to Maintain Competitive Edge
In today's rapidly evolving digital landscape, the importance of AI guardrails and quality assurance in AI systems cannot be overstated. With the potential market for AI audit or assurance services expected to surpass £6.5 billion by 2035, and 63% of senior leaders expressing a higher trust in AI tools validated by external organizations, it's clear that robust AI governance is essential for safe, secure, and responsible use.
The international standards body, ISO, has been at the forefront of this movement. The world's first AI management system standard (ISO/IEC 42001) was published in 2024, outlining requirements for AI system providers. The ISO/IEC 42006:2025 standard, meanwhile, provides requirements for certification bodies when auditing AI management systems. The ISO/IEC 38507:2022 standard offers guidance for organizational governing bodies to enable and govern the use of AI.
Mark Thirlwell, Global Digital Director at BSI, underscores the significance of these standards, stating, "Monitoring is more than a technical safeguard—it's a governance imperative."
Continuous monitoring is crucial to manage issues like bias and ensure long-term trust in AI systems. Without it, issues may go undetected, leading to unintended outcomes, performance drops, or compliance failures. Being able to show how and why decisions were made is just as important as making the right decisions in the first place.
In the realm of AI, domain specificity is key. Ensuring the AI system is developed and optimized for specialized fields relevant to business needs, such as healthcare, automotive, or other agentic systems that handle natural language requests, is essential. Organizations must also assess and improve their governance frameworks before implementing AI solutions, focusing on strong oversight to avoid the AI going off track.
Leading accountancy firms, including the Big Four, are launching AI auditing programs. Structured documentation is crucial for audit readiness, regulatory compliance, and building internal accountability.
Business leaders should establish clear measures of success such as response time, latency, tokens per second, and accuracy, especially when deploying smaller or specialized AI models in specific domains. Ensuring high-quality and representative training data is also crucial to avoid bias and ensure reliability and inclusiveness in AI outputs. Implementing robust testing frameworks to detect and mitigate bias and improve the overall quality and safety of AI applications is equally important.
The confusion of the corporate sustainability landscape offers a cautionary tale about unchecked providers. The EU's AI Act is a starting point for regulation, but faces challenges in practical roll-out. As the use of AI continues to grow, it's clear that a proactive approach to AI governance, including quality assurance, is not just a best practice—it's a necessity.
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[1] Ours, J. (2022). The Forbes Technology Council Explores The Importance Of Quality Assurance In AI Systems. Forbes. [Link not provided]
[2] Ours, J. (2021). The Forbes Technology Council Explores The Importance Of Ensuring High-Quality And Representative Training Data In AI Systems. Forbes. [Link not provided]
Mark Thirlwell, Global Digital Director at BSI, emphasizes the importance of technology in AI governance, stating, "The role of technology is vital in ensuring robust AI governance, from continuous monitoring to detect biases and maintain trust, to the development and optimization of AI systems for specialized fields."
With leading accountancy firms launching AI auditing programs and the EU's AI Act setting a starting point for regulation, technology also plays a crucial role in quality assurance, as it helps in structuring documentation for audit readiness, enforcing strong oversight, and implementing robust testing frameworks to improve the overall quality and safety of AI applications.
