Businesses across industries are racing to deploy generative AI tools and frameworks. However, the governance frameworks to empower these deployments for full potential lack the very necessary contextual posture. There’s a need to consider the uses, challenges, and improvements required across business units for GenAI adoption. Without such discussion, the Gen AI governance might as well be training wheels on a stationary bike.  

While organizations are quick to cite security and compliance concerns for slower Gen AI deployments, there’s little admission on their part regarding a lack of change management. If the risk teams are forced to build compliance and security blueprints for a moving target, the governance posture is likely to collapse inevitably. 

So, let us have a look at how change management is the missing piece in the Gen AI governance puzzle and what businesses can do about it. 

The Real Test of Gen AI Governance 

When organizations think of GenAI governance, their minds often jump to security, regulatory alignment, and compliance. What’s frequently overlooked is that governance only becomes real when GenAI is actually adopted at scale. Without that, policies remain theoretical and frameworks sit idle. Here are some of the challenges that ignorance of mindful change management can cause for Gen AI governance: 

• Unclear ROI Value: Enterprises often struggle to identify exactly what GenAI is there to help them with. Such half-hearted adoption and limited executive buy-in reflect in the misalignment of governance frameworks and business vision. 
• Limited Governance: With the Gen AI governance policies being built mostly around security and regulatory concerns, there’s a lack of more contextual operational frameworks and cross-functional support. This leads to a one-size-fits-all framework that limits GenAI usage rather than empowering it. 
• Low GenAI Literacy: The current governance policies also fail to instill confidence in employees around safe and effective GenAI usage. This creates a vacuum where policies exist but aren’t applied consistently or meaningfully. 
• Fragmented Deployment: Pilots may be successful in isolated teams, but broader rollouts stumble when they fail to account for the diverse needs, roles, and work styles across the organization. 

As Gartner® put it, “Too often, GenAI tools are brought into the business as a solution looking for a problem.” *  Thoughtful change management directives, therefore, appear to be a necessary catalyst in formulating a governance framework around such a sophisticated technology. 

Embedding Change Management in Governance 

Governance must be built with the workforce as it evolves. Instead of relying on static policies and blanket trainings, Gen AI policies need to be more role-specific and culture-driven. Being co-authored with change managers and business stakeholders, the governance strategy would help affect real behaviors and evolve more practical use cases and department-specific needs.  

• Co-Creation Over Top-Down Control: Change management encourages collaboration with business departments, who understand where GenAI can add value. Their input ensures that governance policies are grounded in actual workflows, not abstract assumptions. 
• Persona-Driven Enablement: Most GenAI training today is generic. But employees across different roles need different types of guardrails and guidance. Change management surfaces these differences and feeds them back into governance design. 
• Local Champions: Governance cannot be enforced through documents alone. Change management helps create local influencers among the champions who can demonstrate responsible GenAI use. These champions can mentor peers and reinforce behavioral standards where it matters most. 
• Reward Systems: Change programs often include merit systems or “time hack” showcases to reward smart GenAI use. These create clear, observable examples of what responsible use looks like, effectively operationalizing policy. 
• Vendor-Agnostic Roadmapping: Instead of following a vendor’s roadmap blindly, change-led governance allows organizations to build their own phased GenAI strategy—driven by internal learning, employee feedback, and business outcomes. 

Why GenAI Still Hasn’t Landed 

Organizations keen to adopt GenAI are failing to familiarize the workforce and, therefore, their entire culture with the actual requisites that demand such a technology. As a result, Gen AI tools and frameworks sit underused or misused, making the governance strategies, at best, inconsequential and, at worst, ill-equipped. Until the governance frameworks are built upon department-level insight and evolving behavior, Gen AI will continue to remain a glorified logo on our slide decks. 

*4 Key Ingredients for a Successful Generative AI Strategy in Enterprise Applications, 15 July 2025 

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.