When employees use unapproved AI tools, it is rarely because they want to cause harm. More often, they are trying to do their jobs better. Shadow AI is a signal that something is broken in your current setup. Maybe the tools are too complex. Training may be missing. Or approvals may take too long. Whatever the case, people are reaching outside the system to meet a need they cannot fulfill internally.
“Shadow AI is an indication of the necessity of incorporating these AI and generative AI tools into your organization,” says Merav Yuravlivker, Chief Learning Officer at Data Society Group. “The efficiency that happens is just so tremendous. It is detrimental not to use this in your day-to-day.”
For Chief Data Officers and Chief Learning Officers, the presence of Shadow AI is a cue to look deeper. It doesn’t signal disobedience; it signals unmet needs.
Where Your System May Be Falling Short
Most shadow AI use is not about cutting corners. It is about avoiding bottlenecks. If it is faster to summarize meeting notes in an outside tool than to request help from a sanctioned platform, people will default to speed. That behavior reveals pain points in the system, pain points leaders should address head-on.
“These are not fringe cases,” Yuravlivker adds. “These are daily tasks.”
Shadow AI surfaces patterns of inefficiency, not just one-off mistakes. It gives learning and data leaders a diagnostic tool, if they are willing to listen.
Governance That Supports, Not Restricts
AI governance is not about locking down every use case. It is about creating clarity and structure so employees can make smart decisions within acceptable limits. When organizations define approved tools, clarify what types of data can be used, and communicate why certain guardrails are in place, Shadow AI use decreases without killing innovation.
“At Data Society, we train people on the systems they already have,” Yuravlivker explains. “Then we showcase the power of those systems through hands-on projects that let people solve real problems.”
CDOs can define governance strategy, but adoption depends on CLOs helping employees navigate and trust that structure.
MUST READ: Why It’s Time to Drop Shadow AI Without Dropping Innovation
Making Compliance Practical and Personal
Compliance works best when it is woven into daily work. That means offering support at the moment of need, using relevant examples, and reinforcing learning with hands-on practice. When employees understand the purpose behind governance and feel equipped to operate within it, compliance becomes a habit, not a hurdle.
“When people get that hands-on practice, it increases the likelihood that they will continue to use those tools in the future,” says Yuravlivker.
For CLOs and CDOs, the key is to treat compliance not as a barrier, but as a behavioral shift supported by trust, training, and access.
Shadow AI Is a Mirror, Not the Enemy
Shadow AI is not the problem. It is the symptom of a larger issue. It points to where workflows break down, where guidance is unclear, and where your teams are outpacing your systems. That’s not a failure, it’s an opportunity.
When CLOs and CDOs work together to embed AI governance into training, reinforce it with real-time examples, and design tools employees actually want to use, Shadow AI loses its appeal.
Data Society helps organizations close the gap between what employees need and what systems currently offer. Through hands-on, role-specific training and governance-aligned content, we support responsible AI use at scale.
If you’re ready to transform Shadow AI from a liability into a leadership opportunity, we can help.
Reach out to Data Society to build a learning and governance strategy that turns hidden risk into everyday readiness.
Q&A: Shadow AI
It reveals gaps in tools, communication, or training. It means employees are trying to solve problems that your systems are not addressing.
