The AI Sentiment Divide: Why Executives See Success While Employees Face Friction

The integration of artificial intelligence into the modern workplace has sparked a fundamental disconnect between executive suites and the front-line workforce. According to fresh research, corporate leaders believe artificial intelligence is driving success far more effectively than the employees who interact with the technology on a daily basis. This perceptual divide grows even more pronounced within larger enterprises, highlighting a critical friction point as organizations race to scale automated tools without always ensuring their people are brought along for the journey.

The findings come from the AI Execution Index 2026, published by Asana, which surveyed 1,000 respondents comprising 300 executive decision-makers and 700 knowledge workers. The research evaluated how well organizations put artificial intelligence to work and convert it into tangible results. Overall, executives scored their organizations an impressive 75.2 out of 100 for AI execution. In stark contrast, knowledge workers gave a much more modest score of 58.2, leaving a substantial gap of 17.1 points between leadership optimism and employee reality.

This divergence is not uniform across all businesses. In smaller organizations with 10 to 999 employees, the sentiment gap between leadership and staff sat at a relatively modest 11.6 points. However, inside businesses with 1,000 or more employees, that divide nearly doubled, widening significantly to 22.5 points. The survey, which was administered through Dynata’s research panel, points to deep structural challenges in how large companies roll out enterprise-wide technology compared to their more agile, smaller counterparts.

Beyond numerical scoring, the two groups displayed sharply conflicting views regarding basic operational oversight. Sixty-one per cent of surveyed executives claimed that leadership maintained full visibility into day-to-day artificial intelligence usage across the enterprise. Conversely, only 36 per cent of workers shared this assessment, revealing a blind spot in how management monitors actual workplace adoption.

Jo Gaines, general manager for the Asia-Pacific region at Asana, emphasized the significance of this disparity. Leaders naturally focus on overarching capital investments and high-level strategic roadmaps, while workers focus entirely on whether the software actually helps them complete their daily responsibilities. Gaines noted that accelerating individual tasks does not automatically translate to broader organizational effectiveness. When management and staff hold such disparate views of technological progress, organizations risk declaring premature victories while ignoring underlying operational friction on the ground.

Why Leaders and Workers Measure AI Success Differently

The root of this disconnect often traces back to how performance and technology outcomes are measured within modern corporations. James Tidey, artificial intelligence go-to-market lead at Asana, noted that when individual contributors are asked what concrete, quantifiable outcomes their daily digital workflows actually achieve, many struggle to provide a clear answer. This points to a deeper misalignment within corporate strategy.

According to Tidey, executives and everyday employees are typically evaluated against completely different sets of metrics, and these individual incentives inevitably shape workplace behavior. If executive-level metrics and top-tier organizational outcomes are not properly translated down to the individual contributor level, the resulting disconnect becomes inevitable.

Tidey draws a sharp distinction between what he terms "blue money" outcomes and "green money" outcomes. "Blue money" refers to basic time savings, such as utilizing a generative chat assistant to draft a routine email in a matter of seconds. While valuable for personal efficiency, these single-agent use cases often fail to drive broader financial performance. "Green money" outcomes, by contrast, directly grow revenue, slash operational costs, or mitigate enterprise risk. Too many businesses focus narrowly on isolated productivity hacks rather than fundamentally redesigning end-to-end workflows to secure measurable business results.

The empirical data gathered in the study reinforces this observation. While 91 per cent of all respondents reported experiencing at least one measurable improvement from artificial intelligence over the preceding twelve months, the average organization achieved success in just 3.1 out of 10 potential outcome areas. Furthermore, executives were 19 percentage points more likely than general workers to report tangible revenue growth stemming from their artificial intelligence investments.

Bigger Businesses Report More AI Adoption, Weaker Execution

Paradoxically, the research demonstrates that larger businesses boast higher rates of deployment while suffering from weaker execution scores. Half of all enterprise respondents described artificial intelligence as deeply embedded or organization-wide within their operations, compared to just 42 per cent of respondents in small and mid-sized organizations.

Yet, when evaluated on performance, enterprise respondents averaged a score of 61.7 on the execution index, trailing smaller organizations which averaged 64.9. In fact, large enterprises scored lower across all 10 evaluated dimensions. The most pronounced gaps appeared in complex operational areas, specifically workflow integration, where enterprises scored 58.8 against 63.5 for smaller firms, and cross-functional coordination, where enterprises scored 61.3 compared to 65.7.

Tidey suggested that large corporations often fall into the trap of purchasing and deploying an excess of disconnected software solutions. Organizations continuously introduce new applications to solve specific operational hurdles, but frequently fail to integrate those tools cohesively, leaving employees struggling to navigate fragmented digital environments. In contrast, smaller organizations benefit from physical and operational proximity. Employees in smaller firms often sit near their colleagues, making it much easier to coordinate workflows and understand how technology fits into shared daily tasks.

This environment of disconnected tool deployment has also fueled the rapid expansion of shadow AI—the unauthorized use of artificial intelligence tools that have not been formally vetted or approved by an employer. Approximately 72 per cent of executives and roughly half of all workers acknowledged that employees routinely rely on unapproved applications to manage their workloads.

Training emerged as a vital differentiator in bridging this gap. Among organizations classified by the index as "AI Executors"—those combining advanced adoption with measurable improvements across at least four distinct outcome areas—59 per cent of workers reported receiving comprehensive training in practical and responsible artificial intelligence use. In contrast, only 29 per cent of workers in organizations labeled merely as "AI Adopters" reported similar training levels. Across the broader business landscape, only 22 per cent of survey respondents qualified for the "AI Executor" designation, underscoring how widespread adoption continues to outpace proper capability building.

What Should HR Leaders Do About AI Anxiety?

The breakneck pace of technological transformation is also taking a psychological toll on workforces. Tidey observed that rapid change is wearing down employees who never asked to transition into artificial intelligence-driven roles, admitting that even technology leaders experience fatigue. Anxiety surrounding job security must be met with genuine empathy from management, as leaders run the distinct risk of mistaking forced compliance for authentic workplace uptake.

In a market defined by widespread ambiguity, organizations have a clear operational priority—and a fundamental corporate responsibility—to bring clarity to their people. Tidey advocates for radical transparency regarding corporate motivations. Employers should openly articulate why they are investing in artificial intelligence, what strategic goals they hope to achieve, and what those changes mean practically for individual workers and their daily routines. Cultivating organizational transparency should be treated as a core operational value.

To bridge the sentiment divide, the index report recommends that leadership teams routinely survey both executives and employees using identical metrics covering readiness, workflow integration, visibility, training, ownership, and perceived value. By examining precisely where the answers diverge, organizations can identify operational bottlenecks before minor friction points devolve into systemic failure.

As Gaines concluded, unlocking the true, measurable impact of enterprise technology requires leaders to ask their workforce the exact same questions they ask themselves regarding progress, and to investigate honestly where those perspectives differ.