Organisations are pushing ahead with artificial intelligence at pace, but most employees are not being equipped with the skills needed to use it effectively, leaving a growing gap between ambition and workforce readiness.
While senior leaders increasingly see AI as central to performance and competitiveness, training efforts are failing to reach the majority of staff. In many cases, capability is concentrated in small specialist groups, with limited opportunities for wider teams to develop or apply new skills.
The result is a disconnect between strategy and execution, where investment in technology outpaces the ability of the workforce to use it safely, effectively and at scale.
Research by Economist Impact, a research and analysis division of The Economist Group, found that although almost all organisations report having some form of AI skills strategy, formal training reached fewer than 10 percent of employees in nearly half of cases.
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Training gaps widen as AI adoption accelerates
The research shows that 99 percent of organisations say they are developing AI-related skills in some way, but most rely on informal methods such as mentoring or self-directed learning rather than structured programmes.
Only 16 percent offer formal internal training, while 21 percent partner with external providers. Even where training exists, it is often limited in reach, preventing organisations from building capability across the workforce.
Budget constraints appear to be a key factor. Just 38 percent of organisations report having dedicated funding for AI training, despite widespread agreement that skills development is critical.
The gap between ambition and delivery is reflected in outcomes. While 88 percent of executives view AI as a competitive advantage, only 4 percent say their organisation has achieved scalable value from its use.
Productivity focus limits long-term capability
Many organisations are prioritising short-term gains, particularly improvements in employee productivity, over longer-term workforce development.
The research found that 73 percent of leaders see productivity as the primary driver for AI investment, and 79 percent measure success using productivity metrics. Far fewer track indicators such as skills development, engagement or retention.
Charles Ross, Asia head of policy and insights at Economist Impact, said organisations were moving ahead without fully embedding the foundations needed for long-term success.
He said many were still building systems while trying to generate results, adding: “Driven by the desire for quick, tangible wins, organisations often equate productivity gains with ROI. Without clear strategy, skills, and governance, they risk missing the sustainable competitive advantage AI can deliver.”
This approach can limit the effectiveness of training. Without opportunities to apply new skills in day-to-day work, employees may struggle to retain knowledge or build confidence in using AI tools.
Skills shortages raise governance and risk concerns
The research also points to significant gaps in critical areas linked to responsible AI use.
Although 96 percent of executives say cybersecurity skills are essential, only 20 percent believe their workforce is proficient. Similar gaps exist in data privacy and bias detection, raising concerns about how organisations manage risk as AI becomes more embedded in operations.
These shortfalls are compounded by weak governance structures. While many organisations have discussed frameworks for responsible AI use, only a small proportion have fully implemented and enforced them.
Ross said effective adoption depends on both technical capability and oversight. He warned that risks often arise from internal practices, including poor data handling and weak controls, rather than the technology itself.
Leadership and culture barriers slow progress
Beyond technical skills, the research identifies gaps in areas such as critical thinking and creativity, which are seen as essential for working alongside AI systems.
Yet only around one third of organisations believe employees currently demonstrate these capabilities at a high level, limiting their ability to question outputs, adapt to change and drive innovation.
Responsibility for developing AI skills is also unclear in many organisations. Nearly half of executives say managers have limited responsibility for workforce development in this area, while some report no clear ownership at all.
Resistance to change is another barrier, particularly among middle management, where concerns about disruption or uncertainty can slow adoption.
Keisuke Koyama, senior general manager at Kyocera Document Solutions, a global provider of document management and business solutions, said organisations risked focusing too narrowly on immediate gains. He said prioritising productivity over long-term skills could limit outcomes, adding: “Organisations that prioritise short-term productivity over long-term skills development risk missing AI’s true potential.”
He said success depended on a broader approach to transformation. “This research highlights the non-technical factors — skills, governance, leadership — that determine whether AI ambition translates into sustainable business outcomes.”
Closing the gap between strategy and workforce readiness
The findings point to a widening divide between organisations that are able to scale AI effectively and those still struggling to move beyond early adoption.
Experts say companies at more advanced stages of AI maturity are growing faster than their peers, while those at earlier stages are falling behind, reinforcing the importance of workforce capability as a driver of performance.
Bridging the gap will require more than investment in technology, observers say. Expanding access to training, strengthening accountability and creating opportunities to apply skills in practice are likely to be critical if organisations are to translate AI ambition into lasting value.
