Ensuring GenAI’s Success: The Case for Information Governance

According to Jim Merrifield of Robinson+Cole, the success of generative AI (GenAI) in the upcoming year will depend on robust information governance frameworks that promote ethical AI development, compliance, and accountability. Without these protections, GenAI’s potential could be diminished by risks such as data misuse and regulatory challenges. As organizations increasingly integrate AI into their operations, information governance will play a crucial role in shaping its responsible development.

One of the primary ways information governance will influence AI in 2025 is by mitigating algorithmic bias and ensuring AI models align with societal values. Ethical AI will require organizations to implement fairness audits, transparency measures, and accountability protocols. As AI continues to evolve, companies must not only adopt these safeguards but also recognize information governance as a foundation for building long-term trust and sustainability.

Beyond addressing bias, strong information governance strategies will be essential for maintaining data integrity and minimizing risks such as data breaches, misuse, and regulatory non-compliance. By ensuring that data is collected, stored, and used correctly, governance frameworks will help organizations uphold privacy and security standards. In 2025, practices such as proper data labeling, privacy-preserving techniques, and retention policies will become standard to reinforce responsible AI management.

As AI adoption expands, so will the complexity of regulatory requirements. In response, organizations will increasingly turn to regulatory technologies (RegTech) to streamline governance processes. These technologies will help automate compliance and monitor AI-driven decisions, enable companies to track real-time data usage, and ensure adherence to global data protection laws.

To effectively implement these governance measures, organizations must foster cross-disciplinary collaboration among AI developers, legal teams, and compliance experts. This shared responsibility will necessitate new training initiatives and role definitions to align ethical principles with technological advancements. Additionally, open-source initiatives and standardization efforts across industries will promote greater transparency and accountability in AI systems.

Ultimately, organizations prioritizing strong information governance frameworks will position themselves as leaders in the AI-driven future. Proactively implementing governance measures can safeguard sensitive information, build trust with stakeholders, and ensure compliance with evolving legal and ethical standards.