The rise of artificial intelligence (AI) in enterprise operations has introduced a new layer of complexity and urgency to business management. As organizations increasingly integrate AI tools into their daily workflows, a pressing need emerges to measure their effectiveness and oversee their deployment. A recent survey by Larridin highlights that while many executives feel confident about their organization’s engagement with AI, the reality, as perceived by operational teams, tells a more nuanced story.

Executives often express assurance in their awareness of AI activities, but the perspective shifts dramatically for directors and managers responsible for the day-to-day work. This disparity in perception reveals a concerning 16-point gap in confidence regarding AI visibility. This inconsistency, observable across various sectors and company sizes, has deep implications for how organizations strategize their AI use.

One of the most significant contributors to this gap is the phenomenon known as Shadow AI—where employees employ personal or unsanctioned AI tools. More than one-fifth of leaders view this misuse as a barrier to successful AI integration. Interestingly, despite these concerns, many leaders maintain a high confidence level in their oversight of AI usage. Tool procurement might show which licenses are purchased, but it fails to deliver insights into how these tools are utilized on a daily basis.

Russ Fradin, CEO of Larridin, encapsulates the dilemma succinctly: “The C-suite believes AI is visible, valuable, and under control, while adoption is racing ahead of measurement and governance is inconsistent. Until enterprises can organize their efforts around real-time data, AI could be a strategic liability as well as a strategic asset.”

This stark division in confidence levels—robust at the executive level but shaky in operations—calls for more structured AI governance and measurement strategies. The survey indicates that enterprises leveraging multiple AI products tend to perform better, with an average of 2.7 tools yielding noticeable returns compared to just 1.1 tools for those underperforming. This data points to the advantages of utilizing diverse, specialized tools tailored to specific workflows.

However, this diversification does not come without challenges. Too many overlapping tools can lead to budget inefficiencies, and as various embedded AI features within SaaS platforms continue to proliferate, the average large enterprise now finds itself managing around 23 AI tools. Alarmingly, nearly 45 percent of these tools are adopted outside the formal IT procurement channels, complicating oversight.

Moreover, only 38 percent of organizations maintain a comprehensive inventory of AI applications in use. These inventory gaps pose significant hurdles for governance and budgeting, particularly in light of evolving regulatory frameworks such as ISO 42001, which mandates continuous awareness of deployed systems. Without a reliable inventory, organizations risk inadvertently exposing themselves to liability and missed opportunities for optimal AI utilization.

The variation in return on investment (ROI) across different sectors further complicates the AI landscape. Sectors such as retail, software, manufacturing, and telecommunications report a notably high likelihood of realizing ROI within a six-month window. In contrast, industries such as hospitality, restaurants, and healthcare have lower expectations for return on their AI investments. This disparity often stems from workflow structures that either facilitate or hinder automation and efficiency.

In knowledge work sectors that can break down tasks into discrete, automatable components, rapid progress is evident. However, industries anchored in physical operations or tightly controlled processes often experience slower advancements in AI integration. Healthcare, for example, presents a dichotomy—executives exhibit a high degree of confidence in AI visibility and control, while operational realities may indicate otherwise.

This evolving landscape underscores the mounting pressure on business leaders to establish robust measurement practices around their AI tools. As reliance on these technologies deepens, organizations must be vigilant in monitoring their AI investments, ensuring they align with overarching goals while adhering to compliance and governance standards. In an era where AI is a critical driver for success, understanding its usage and effectiveness will be paramount for enterprises aiming to capitalize on its transformative potential.