The Critical Need for Advanced AI Evaluation & Safety

The AI Revolution & Escalating Risks

Artificial intelligence systems are demonstrating remarkable advancements, achieving high performance on complex benchmarks and becoming increasingly integrated into critical business operations and daily life. However, this rapid proliferation is accompanied by a dramatic and concerning rise in AI-related risk incidents. According to the OECD AI Incidents Monitor (AIM), the total number of risk incidents in 2024 surged significantly compared to previous years, with a large percentage directly related to AI safety issues.

The Stanford AI Index Report 2025 further highlights this dichotomy: while AI performance continues to improve, standardized Responsible AI (RAI) evaluations remain uncommon. The sheer volume of AI incidents points towards a potential underestimation of cumulative risks, underscoring the necessity for continuous evaluation to maintain AI trustworthiness.

The AI Evaluation Market: A Multi-Billion Dollar Global Imperative

The market for AI and its associated evaluation services is substantial and rapidly expanding. Key segments include:

• AI Trust, Risk, and Security Management (TRiSM): Projected to reach USD 7.44 billion by 2030.
• AI Model Risk Management: Anticipated to hit USD 10.5 billion by 2029.
• Generative AI Market: Forecast to exceed USD 176 billion by 2030.
• India's AI Market: Expected to expand significantly, with the Generative AI segment within India projected to reach USD 5.40 Billion by 2033.

This growth is driven by the need for ethical AI, governance, explainability, compliance, and the imperative to unlock AI's full potential safely. Effective evaluation builds trust, a fundamental prerequisite for broader AI adoption and innovation.

The Evaluation Gap: Why Current Methods Fall Short

Despite the proliferation of AI, current evaluation methodologies face significant challenges, particularly when assessing frontier AI models. Standardized benchmarks often test isolated capabilities and can lead to models "overfitting" without genuine, generalizable intelligence. They struggle to assess long-horizon planning, creative problem-solving in dynamic scenarios, and emergent capabilities.

There is a growing demand for evaluation frameworks that extend beyond mere functional accuracy to encompass context-specific performance, user experience, security, safety, and ethical considerations. Continuous and Human-in-the-Loop (HITL) evaluations are becoming indispensable.