AI adoption is no longer the challenge. AI control is.
Tool sprawl
AI tools are being introduced faster than they can be evaluated or governed. Teams experiment independently, leading to overlapping capabilities, rising costs, and a fragmented technology landscape that is difficult to manage or secure.
Shadow AI
Employees are already using AI outside approved systems to get work done. While often well intentioned, this creates unmanaged data exposure, inconsistent outputs, and risk that sits outside formal IT and security oversight.
Governance Anxiety
Leaders know AI needs controls, but struggle to define what “good governance” looks like in practice. Unclear ownership, evolving regulation, and ethical concerns often result in stalled decisions or overly restrictive policies that slow progress.
Low adoption after pilots
Many AI initiatives show promise in pilot form but fail to translate into sustained usage. Without workflow integration, enablement, and clear accountability, pilots remain isolated experiments rather than becoming part of everyday work.
