AI governance has become a business imperative as companies rush to deploy generative and agent-driven AI tools that carry risks leaders struggle to quantify. According to The AI Journal November 14, organizations are now confronting the uncomfortable truth that even well-tested AI systems can misfire in ways that threaten reputation, compliance, and customer trust.
Business leaders initially embraced generative AI based on consumer experiences with tools like ChatGPT, the authors noted. However, this confidence quickly collided with the reality of hallucinations and opaque decision making. Teams deploying generative AI into products and services faced urgent questions with no guaranteed answers: How accurate is the model? Is bias present? Can outputs be explained? One leader cited in the article delayed a customer service AI agent launch and limited its scope once executives realized that occasional hallucinations could not be fully ruled out.
AI agents heighten the stakes even further. Their ability to take autonomous actions promises expanded value but increases the risk of failures cascading across interconnected systems.
What and How to Govern
The authors described a unified risk landscape synthesized from global frameworks including NIST, OECD, the EU AI Act, UK guidance, and Singapore’s FEAT principles. They identified nine core risk categories:
- accuracy and reliability
- fairness and bias
- explainability and transparency
- accountability
- privacy
- security
- intellectual property and confidentiality
- workforce impact
- environmental sustainability.
Effective AI governance requires visible executive oversight because it spans data science, legal, compliance, security, HR, and business units. Companies should anchor governance in clear principles tied to organizational values, then translate them into actionable policies and standards, the authors said. Accountability structures must align with existing risk programs so controls are neither duplicated nor overlooked.
A core operational challenge is maintaining a complete AI inventory as use rapidly expands, often through third-party tools. Governance must include checkpoints in procurement and development processes to flag AI use early. High-risk applications then undergo deeper assessment, balancing risk and benefit at appropriate approval levels.
To enforce guardrails, the authors emphasized, organizations need both control requirements and control routines, supported by quantitative testing tools that evaluate fairness, explainability, and other risk factors. Increasingly, companies are adopting specialized AI governance platforms or adapting workflow systems such as ServiceNow to manage approvals and documentation.
The authors concluded that governance does not restrict innovation. Instead, it creates the trust necessary for AI adoption at scale.
What this Means for ERP Insiders
AI governance should be treated as a foundational requirement rather than an afterthought. The article makes it clear that even when teams test extensively, they cannot guarantee that generative AI or autonomous AI agents will avoid accuracy issues or harmful behavior. For ERP end users, this means AI deployments in finance, HR, or supply chain must include upfront guardrails that define who is accountable, how decisions are monitored, and what level of residual risk the business is willing to accept.
AI risk evaluation needs to be engaged directly instead of assuming data science teams have it handled. The authors noted that governance requires coordination across legal, security, HR, compliance, and business units. In an ERP context, that translates to embedding checkpoints within procurement, development, and workflow approvals that ask whether new modules or third-party extensions include AI and what type of risk review they require before go-live.
Leaders evaluating ERP enhancements should scrutinize vendor governance capabilities as closely as product features. The article explains that AI is increasingly embedded in third-party applications and that specialist governance platforms are emerging because traditional GRC software has struggled to manage AI’s unique risks. When selecting ERP add-ons or upgrades with AI embedded, buyers should look for vendors that can demonstrate transparency, fairness testing, explainability metrics, and documented controls for privacy, security, and IP protection. Vendors that cannot show how they manage bias, hallucinations, or propagation of errors across connected agents are pushing risk back onto the customer.
