The lack of defined entry points means that AI is often used in isolated, low-impact ways. (Image source: 123RF)
A new field report finds a widening gap between organizational expectations.[–>artificial intelligence ([–>AI) and the actual results delivered by the companies using it.
This global report, published by technology executives and analysts Christian Cavasi and Blank Collar, shows that while AI adoption is now spreading across the enterprise, most organizations are still in the early stages of capability development.
The State of AI at Work report states that 87% of the world’s workforce uses AI at an entry-level level, and only an estimated 13% use AI for meaningful work, highlighting a structural imbalance between deployment size and productive applications.
Rather than indicating a lack of access to tools, this finding points to a deeper implementation gap.
“While companies are deploying AI systems across departments, the majority of employees are still unable to translate access into consistent, high-value workflows. As a result, there is a growing mismatch between executive expectations for change and the incremental, often experimental realities of day-to-day use,” the report said.
This field report was conducted through observational analysis of patterns of AI usage in organizations, practitioner and executive insights, and behavioral interpretations of how AI is being implemented in real workplaces.
expectations and reality
At the heart of the report is a strong warning that organizations are overestimating the direct impact of AI while underestimating the actions and organizational changes needed to unlock value.
This gap is structural rather than marginal, and is rooted in the way we currently design work and introduce AI into existing systems.
“Your company acquired AI and no one changed. We’ve seen this to some degree with every major technology change over the years, but in the case of AI, these results are very disappointing.
“This technology has tremendous potential, but in my view, it is not being leveraged in the right way. Organizations often believe that once they deploy a tool, transformation will automatically follow, but that is not the case. There is a gap between availability and meaningful integration, and that gap is holding back results.”
The report suggests that many companies are treating AI as a “plug-in productivity layer” rather than a functional shift that requires rethinking workflows, decision-making structures, and task design.
This has led to a situation where implementation is ahead of implementation maturity and experimentation is not consistently translated into operational value.
A central concept introduced in the report is what Kabashi describes as the “use case desert,” or the difficulty employees face in identifying clear, structured AI applications in their daily work.
Kabashi further elaborates on this challenge by emphasizing that the limitations are primarily due to cognitive frameworks and organizational clarity, rather than technical skills. “What hinders mastery is lack of encouragement. People can learn in an afternoon, start experimenting, and quickly become proficient with a tool. But before they get there, they get stuck with a blank question: ‘What does this even mean?’
“They open a tool, summarize an email or draft a short reply, and then stop because their real work isn’t pre-labeled or structured in a way that tells them what to delegate to AI. The problem isn’t access or capabilities, it’s that clearly defined use cases aren’t built into their daily work.”
According to the report, this lack of defined entry points means that AI is often used in isolated, low-impact ways, rather than being incorporated into workflows that deliver measurable business outcomes.
The report argues that this is one of the key reasons why, despite rapid adoption, organizations are not seeing a commensurate return on investment.
Beyond individual usage patterns, the report identifies leadership as a significant constraint to AI effectiveness. They argue that senior executives are still developing a clear understanding of where AI provides tangible value and how to operationalize it at scale across teams and departments.
While Kabashi acknowledges the novelty of this change, he warns of a slow pace for organizations to learn and adapt. “I’m not trying to point out or suggest that leaders are acting in bad faith.
“This is all very new, and it’s not surprising that senior executives have yet to figure out what actually works in practice. But the scope for learning through experimentation is narrowing.
“Everyone had better get busy now, because the companies that understand this – those that can move beyond 13% meaningful AI utilization – will have a strong competitive advantage in their respective industries. Those that delay this or treat it as a surface-level tool rollout will increasingly be overtaken by more adaptive organizations.”
This means that competitive advantage will increasingly depend on the depth of organizational collaboration and ability to execute, rather than access to AI.
State of AI at Work concludes that the central challenge facing organizations is no longer whether to deploy AI, but how to translate widespread experimentation into consistent and meaningful capabilities.
