Enterprise teams already run dozens of AI tools in their daily work. Uses range from code generation and analysis to customer support drafting and internal investigations. Oversight remains uneven across roles, functions, and industries. Laridin’s new survey of business leaders puts measurement and governance at the center of this operating environment.
Executives often express confidence in their understanding of AI activities across their organizations. Directors and managers close to day-to-day operations describe a different situation. Confidence decreases as implementation approaches, creating a 16-point gap between executives and directors’ views on AI visibility. This gap exists across industries and company sizes.
The use of Shadow AI contributes to this disconnect. Despite most of the same group having high confidence in visibility, more than one-fifth of leaders identify employees’ use of personal or unapproved AI tools as a barrier to success. While procuring tools provides insight into purchased licenses, there is limited insight into day-to-day usage patterns at the desktop and browser level.
Russ Fradin, CEO of Larridin, said, “While executives believe AI is measurable, valuable and under control, adoption is lagging behind measurement and governance is inconsistent. Until companies can organize their efforts around real-time data, AI can become both a strategic asset and a strategic liability.”
There is confidence at the top, but there are blind spots on the ground
Most companies rely on multiple AI products. Organizations reporting higher revenues use an average of 2.7 tools, while low-performing organizations use 1.1 tools. Dedicated tools support individual workflows such as software development, automation, analysis, and content generation. A centralized platform accounts for only a portion of your daily activities.
This diversification creates redundancy. Some leaders believe that duplication of tools is a source of budget waste. Embedded AI capabilities within SaaS platforms also add to that number. The average large enterprise currently operates 23 AI tools, with 45% of adoption taking place outside of formal IT procurement channels.
Only 38% of organizations maintain a comprehensive inventory of the AI applications in use. Inventory gaps complicate governance, budgeting and risk management, especially as regulatory frameworks such as ISO 42001 require continuous awareness of the systems in place.
More tools mean less visibility
Investment returns vary widely by sector. Retail, software, manufacturing, and communications organizations report that they are likely to realize ROI within six months. Hospitality, restaurants, and healthcare report lower expectations.
Many of the differences are explained in the workflow structure. Sectors that break down knowledge work into discrete automatable tasks will achieve faster results. Industries that rely on physical operations and highly regulated processes report slower progress. Healthcare stands out as having the lowest ROI expectations and high executive trust in visibility, reflecting governance frictions and compliance constraints.
Industry context shapes AI returns
Results also vary by job. IT teams report the strongest outcomes and highest confidence in both visibility and ROI. These teams use AI to generate code, automate infrastructure, accelerate delivery, and produce measurable outcomes such as deployment frequency and system uptime.
They report that customer support and logistics are unreliable. The use of AI in these functions focuses on drafting, summarizing, and coordinating tasks that provide incremental benefits. Measurement remains limited and attribution of value has proven difficult. Customer support roles report the lowest ROI confidence across all functions, despite significant investments in chatbots and agent assistance tools.
Why IT helps you stay ahead of the curve
Most employees report small time savings from AI. More than 85 percent still save less than 10 hours per month. A small group of power users, representing approximately 6% of the workforce, report savings of more than 20 hours per month. These users utilize multiple tools and advanced features.
Training has a strong correlation with proficiency. Organizations that implement formal AI training programs report higher skill levels, satisfaction, and increased productivity. Utilization metrics alone cannot capture this difference. Login counts and license adoption provide limited insight into effectiveness and value creation.
Productivity gap within the workforce
Structural issues limit the measurement of companies. 30% of respondents cited accountability gaps around AI measurement. Fragmented ownership between teams follows closely. Technical limitations are ranked lower.
Governance policies exist in most organizations, but their implementation varies. 69 percent report having an AI risk and compliance policy, and more than 80 percent are satisfied with their guardrails. At the same time, many companies lack visibility into employee adoption rates, risk exposure, and value metrics. Organizations with formal governance have a higher potential for ROI, reflecting alignment between leadership, security, and operations teams.
Tracked metrics value ease of collection. Money saved, percentage of users, and hours saved per week top the list. Few organizations track investments per tool, maturity per feature, or improvements in delivery velocity. These gaps limit the ability to link the use of AI to business outcomes.
