Generative and agentic AI continue to evolve rapidly inside enterprise applications, from CRM platforms to digital workplace suites. As vendors push new capabilities into tools like Microsoft 365, Salesforce, and ERP systems, the challenge for leadership is no longer whether to adopt AI, but how to govern it safely and consistently.
According to Gartner’s How to Mature Generative and Agentic AI Governance for Enterprise Applications, “70% of organizations say they have a centralized AI strategy, but only 34% can apply it consistently to their enterprise applications.” Many organizations are not prepared for the scale of change underway.
This gap between strategy and execution exposes enterprises to risk, slows adoption, and limits the value AI can deliver.
Why Governance Must Modernize
As AI becomes embedded across critical business applications, governance can no longer rely on policies alone. Vendor-driven AI features are being activated faster than IT teams can evaluate them. Meanwhile, frontline employees are increasingly experimenting with AI capabilities without adequate guardrails, training, or oversight.
Gartner warns that this approach hinders value. Many organizations restrict AI to small groups of “trusted users” because they lack confidence in their governance model. This leads to slower rollout, inconsistent usage, and a limited understanding of AI’s true business impact.
To help leaders close this governance gap, Gartner recommends a structured model for modern AI oversight, designed to keep pace with rapidly evolving tools.
Gartner’s Three-Part Model for Enterprise AI Governance
- A Central AI Governance Committee
Gartner recommends establishing a cross-functional committee to define strategy, policies, and investment priorities. This group should include senior leaders from IT, security, legal, risk, and critical enterprise application owners. Their role is to align top-down strategies with the realities of ERP, CRM, and digital workplace environments.
- Operational Governance at the Application Level
This is where governance often breaks down. Each major system, ERP, CRM, and workplace apps, needs its own product-style governance team responsible for applying AI policies, monitoring vendor updates, and managing embedded AI capabilities and agents.
This ensures governance is not theoretical but embedded directly into the systems employees interact with every day.
- Enforceable AI Trust, Risk & Security Management (TRiSM) Controls
Traditional security tools cannot address AI-specific risks like prompt injection, agent sprawl, or unintentional data exposure. According to Gartner, “organizations that invest in third-party AI governance products are almost two times more likely to report higher levels of value from their AI tools.”
TRiSM capabilities, such as runtime monitoring, anomaly detection, and sensitive data protection, provide the enforcement layer needed to keep AI safe and trustworthy.
Why Leaders Must Act Now
AI is quickly becoming a primary interface for enterprise workflows. Users will increasingly rely on AI to summarize information, automate processes, and initiate system actions. Without updated governance, organizations risk losing visibility into how AI is interacting with critical data and systems.
Forward-looking governance is not about slowing AI down; it’s about creating the structure needed to scale AI confidently and safely.
The Bottom Line
The future of enterprise AI belongs to the organizations that can govern it well. C-suite leaders should focus on three priorities:
- Empower a centralized governance committee with cross-functional leadership.
- Apply governance directly inside core business systems, not just at the policy level.
- Adopt TRiSM controls that enforce guardrails and manage AI-related risks.
Modern AI governance is no longer optional; it is foundational to realize AI’s full value while protecting the enterprise.
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