Bridging the AI value gap: Why European leadership must align with the frontline
Three out of four organizations across Europe are failing to secure transformative value from AI, according to a report from Eraneos. While tools are widely deployed and adoption dashboards continue to climb, organizational structures have largely failed to keep pace.
The report notes that AI has frequently been layered on top of existing processes without changing underlying workflows, decision-making methods, or lines of accountability. Though many organizations are getting benefits from AI, very few are seeing true transformation.
Leadership and the frontline workforce are describing entirely different realities within the same companies. C-level executives are on average 2.3 times more likely than frontline specialists to describe their organizations positively across every dimension tested.
This perception gap reaches 47 percentage points specifically regarding trust in AI actions. What that means is that the leaders who hold the power to implement fixes are often the least likely to recognize that a problem exists.
“In most organizations today, leadership and the workforce describe two different companies when it comes to AI,” said Anton Dahmen, senior manager strategy at Eraneos. “Strategy can only be as good as the reality it is built on. The work begins by reconciling those two views, before any further investment is made.”
Adoption as a misleading metric
The Eraneos study found that high adoption rates do not automatically translate into business value. Although AI touches 39% of daily operations on average and 92% of respondents report that their work would face disruption if these tools were removed, only 7% of organizations see significant impact across every dimension measured.
Financial services lead in adoption with 47% process coverage, yet its rate of major impact does not vastly surpass sectors with much lower adoption levels. The data indicates that success relies on what an organization builds around the technology rather than the sheer volume of AI it runs.
Trust deficits in daily operations
Trust issues heavily hinder progress during the final stages of implementation, with 74% of respondents discarding at least 40% of the AI recommendations they receive. This reluctance stems from a lack of clear guidance and defined accountability rather than technological inaccuracy.
The study suggests that this is not a problem with the technology itself, but rather a problem with the design of AI tools. This guidance issue is consistent across sectors, with the public sector respondents reporting they discard AI output and only 15% have guidance in place.
Building effective capabilities
The method used to develop AI capabilities heavily dictates employee confidence and success. Organizations relying on occasional standalone training report a frontline confidence rate of only 15%.
Conversely, when AI capability is fully embedded into daily workflows and professional development, frontline confidence climbs to 56%.
The report concludes that unlocking genuine value requires redesigning operational foundations, establishing clear governance, and fostering true alignment between leadership vision and frontline experiences. The takeaway for organizations is that simply investing more in the tools themselves will not solve the commonly seen value gap, rather leaders need to also invest in governance and trustworthiness.
“The critical question is no longer whether an organization uses AI. It is whether leadership is prepared to redesign how work, decisions, and accountability actually happen,” said Olaf Radant, principal at Eraneos. “Without that shift, AI adoption becomes noise – visible on dashboards, but invisible in performance.”
