Over the past three years, artificial intelligence (AI) has moved from an emerging innovation to a critical component of business strategy. However, as organizations deploy AI solutions, they must ensure that these initiatives are governed responsibly, ethically, and strategically. A structured AI steering committee is essential for bridging the governance gap in AI implementation.
We believe that organizations can leverage the cross-functional strength of their existing Information Governance (IG) steering committee model to create a well-rounded AI steering committee that balances technological advancement with compliance, risk mitigation, and long-term value creation. More importantly, IG plays a crucial role in ensuring that AI models, including large language models (LLMs), produce high-quality, reliable, and ethically sound outputs.
The Role of Information Governance in AI Steering Committee Success
An effective AI steering committee must be built on cross-functional collaboration. Organizations that have successfully implemented IG Steering Committees—typically composed of representatives from Legal, IT, Compliance, HR, Finance, Facilities, and other business units—understand the value of governance structures in managing complex data and technology initiatives.
Applying an AI governance model based on structured IG principles helps to promote an environment where AI initiatives align with business goals, remain compliant and ethical, and are built on high-quality data. Rather than operating in isolation, AI projects should support long-term objectives like process automation, competitive advantage, and scalable innovation—something a well-structured AI steering committee can help oversee. As regulations around AI and data privacy evolve, integrating Compliance and Privacy teams into governance efforts ensures adherence to legal standards, ethical AI use, and strong risk management.
Additionally, AI models, particularly LLMs, rely on high-quality training data, and without strong information governance (IG) practices, outputs risk being biased, inaccurate, or legally problematic. IG provides the necessary structure to curate, classify, and retain reliable data, ultimately strengthening AI model performance.
Using Information Governance to Improve AI Model Accuracy
Organizations that have adopted IG best practices understand that structured governance is critical for managing AI’s data-intensive nature.
AI models require large-scale data ingestion, continuous refinement, and structured oversight to produce reliable insights. Strong information governance (IG) practices play a critical role in ensuring AI systems operate effectively. For example, metadata management helps AI models access well-organized, labeled data, reducing errors like AI “hallucinations” and improving decision-making accuracy. According to recent industry studies, organizations with strong metadata practices see up to a 15% improvement in AI model accuracy. Notwithstanding these benefits, however, according to McKinsey, a strong majority of businesses struggle to scale AI due to poor data governance.
Beyond metadata, AI governance must incorporate data retention and privacy compliance to prevent unnecessary data accumulation and legal risks related to the proliferation of redundant, obsolete, and trivial data within LLM frameworks. Without clear retention policies, AI models risk training on outdated or legally protected data, leading to compliance violations under laws like GDPR and CPRA. A recent survey from IBM notes that robust retention policies help mitigate costly data breaches. Version control is another essential component, ensuring AI models are consistently tracked and refined to prevent bias drift and maintain transparency.
Finally, and perhaps most importantly, IG improves data quality. AI models must be trained on high-quality data to ensure reliable outputs — and poor data quality has been directly linked to AI failures and hallucinations. With the right IG practices, organizations can build AI systems that are accurate, scalable, and compliant.
AI Steering Committee Roles and Responsibilities
For an AI steering committee to be effective, organizations must define clear roles within the AI steering committee, similar to how IG steering committees are structured. Key participants should include:
Executive Sponsor: Ensures alignment with corporate strategy and secures necessary resources.
Legal & Compliance Representatives: Address ethical considerations, regulatory compliance, and intellectual property issues related to AI.
IT & Data Governance Leaders: Oversee technical implementation, security, and data governance policies.
Business Unit Leaders: Provide insights into AI use cases, ensuring AI solutions meet business needs.
Risk & Security Officers: Evaluate risks associated with AI models, including bias, security vulnerabilities, and misinformation.
HR & Learning Development Experts: Develop AI training initiatives to enhance workforce proficiency in AI applications.
Bridging the AI Governance Gap
Despite widespread AI adoption, fewer than 20% of organizations have an enterprise-wide AI governance body with decision-making authority. Many AI deployments still occur in silos, leading to misalignment with corporate strategy, regulatory non-compliance, and suboptimal outcomes.
By adapting the IG steering committee model to AI governance, organizations can:
Establish clear oversight mechanisms for AI projects
Improve cross-functional collaboration in AI decision-making
Mitigate risks associated with unregulated AI deployment
Enhance accountability and ethical AI usage
Ensure AI-generated outputs, especially from LLMs, are accurate, reliable, and legally compliant
The Role of IG Professionals in Creating Effective Steering Committees
IG professionals play a critical role in shaping an effective AI governance framework by ensuring that data integrity, compliance, and lifecycle management are at the core of AI initiatives. They bring expertise in metadata management, data classification, and retention policies—ensuring AI models are trained on high-quality, legally compliant data. IG professionals also help align AI governance with broader business objectives by integrating privacy, compliance, and risk management perspectives into the steering committee’s decision-making process.
Additionally, they support workforce training initiatives to enhance AI literacy across departments, ensuring that employees understand data governance principles and ethical AI usage. With their oversight, organizations can establish a structured, transparent AI governance framework that drives business value while mitigating risks.
Final Thoughts
For organizations looking to optimize AI governance, an IG-based approach delivers measurable benefits. Recent industry research has shown the potential for achieving a 20-30% ROI in AI projects through better decision-making and efficiency.
By integrating strategic information governance principles into AI governance frameworks, organizations can ensure their AI steering committees operate effectively, aligning AI initiatives with business objectives while upholding ethical and regulatory standards. As AI adoption accelerates, structured AI governance will become a competitive advantage, helping organizations navigate technological advancements responsibly and sustainably. And, not surprisingly, IG professionals can and should be at the vanguard of these efforts!