Balancing AI Adoption with Trustworthy Risk Management Practices

Balancing AI Adoption with Trustworthy Risk Management Practices

According to a Risk Management Magazine article by Jon Knisley, enterprise AI adoption surged by 595% in 2024, marking a transformative moment for global industries. This exponential growth, however, has created a regulatory dilemma, with over 1,000 AI-related initiatives now under review across 69 countries, according to the OECD. 

For risk managers, Knisley says this evolving landscape demands a dual focus: supporting innovation while upholding security, fairness, and transparency. As organizations integrate AI into core business functions, they must navigate trust concerns and operational vulnerabilities, particularly cybersecurity, data privacy, and model accuracy.

A critical starting point is data quality. AI models, especially large language models (LLMs), depend on well-structured, relevant data. Poor or unvetted datasets introduce risks such as bias, misinformation, and potential data breaches. To mitigate these issues, companies should implement data audits and adopt retrieval augmented generation (RAG) strategies, which enhance LLM performance by enriching them with secure, contextual organizational data.

Equally vital is the implementation of explainable and transparent AI systems. Obvious algorithms can result in bias and discrimination, so organizations should deploy diverse teams and oversight structures to ensure ethical development. External audits, such as those offered by ForHumanity, and purpose-built models like small language models (SLMs) offer additional safeguards by prioritizing accuracy and accountability.

Human oversight also remains indispensable. A “human-in-the-loop” approach strengthens AI reliability across training, testing, and decision-making, ensuring alignment with ethical standards. Educating employees on AI governance further reinforces responsible deployment.

Finally, continuous evaluation using AI tools like process intelligence enables real-time regulatory monitoring and risk detection. For risk managers, this creates an opportunity to automate compliance while reducing operational vulnerabilities.

By integrating data governance, transparency, human oversight, and continuous monitoring, Knisley says organizations can balance innovation with risk mitigation and establish a foundation of trust for sustainable AI adoption.