European companies face AI execution gap as 79% of initiatives miss their goals

There is a growing AI execution gap in European companies: A staggering 79% of AI initiatives fail either during or after the pilot stage, according to a new report from digital strategy consultancy OMMAX, conducted in collaboration with Statista+, Ibexa, and Make.

When it comes to AI adoption, embracing innovations does not automatically translate to a successful strategy. Many companies race to adopt the technology but remain trapped between experimentation and actual deployment, according to the study, which surveyed 250 senior decision-makers across Germany, France, Italy, the Netherlands, and the United Kingdom.

While 58% of organizations boast a clearly defined AI strategy, fewer than half, at 44%, have fully implemented the operating models required to scale these programs successfully. The lack of a credible strategy can ultimately ruin AI initiatives that otherwise have the right goals in mind.

This notable divide leaves a majority of enterprises with ambitious plans but no functional machinery to deliver on them. Experts note that a defined strategy without an implemented operating model functions like a plan without an engine, stalling before it can achieve a meaningful corporate impact.

According to the OMMAX co-authored report, around 66% of organizations in the financial services sector have already successfully implemented end-to-end AI capabilities. This leading adoption rate is followed by the consumer goods sector and business services.

Internal efficiency gains

The research highlights that corporate returns from AI investments are heavily skewed toward optimization of internal processes rather than generating new value. A total of 84% of organizations report experiencing operational efficiency gains from their technology implementations.

In addition to that, 55% of the surveyed companies achieved internal efficiency improvements of at least 10%, whereas only 36% could say the same for their revenue growth.

This imbalance suggests that companies are successfully using technology to optimize how they work internally, but they are struggling to translate those gains into broader business transformation or commercial expansion. True maturity will require businesses to shift their focus from cost-savings to actively driving growth.

Financial cost of AI initiatives

The journey from a successful test to full corporate deployment is proving to be financially risky. Out of the high number of failed initiatives, 35% of projects fall apart during the pilot stage itself, and 44% fail right during the critical transition from pilot to production.

Budget overruns complicate the situation further, with 65% of completed AI projects exceeding their original financial plans. Although a typical concept takes between three and six months to deploy for 49% of organizations, the combination of high failure rates and unexpected delivery costs raises serious questions about the net returns on these investments.

System and ownership bottlenecks

Two main roadblocks are holding businesses back from achieving a stronger operational impact: Technical complexity and restrictive organizational structures.

Integration complexity with existing systems is cited as the top barrier by 40% of decision-makers, followed closely by a lack of internal skills at 32%, unclear return on investment at 32%, and fragmented data foundations at 30%. While front-office customer relationship tools trend toward full integration, foundational data warehouses and data lakes remain a bottleneck, with 53% only partly integrated.

Compounding these technical hurdles is a distinct lack of diverse corporate ownership. The report reveals that 48% of execution responsibility is concentrated inside technical departments like IT and engineering, while a meager 7% sits within business units. Because technical teams naturally optimize for infrastructure rather than commercial outcomes, experts conclude that ownership must be redistributed across commercial and customer-facing functions to unlock genuine corporate value.

The findings from the report seem to indicate that the challenge is no longer access to AI, but rather execution across systems, data, and organizational structures. Leaders have managed to differentiate themselves by aligning their operating model to their strategy, distributing ownership beyond IT teams, and fixing data quality and integration complexity before scaling.

“AI has moved beyond experimentation. The real challenge is execution,” said Toni Stork, CEO and founding partner at OMMAX. “Companies don’t need more pilots; they need the operating models, governance and organizational capabilities that turn AI into measurable business value. The organizations that close this execution gap will define the next generation of market leaders.”