Many companies across the EMEA region have made significant strides with AI over the past year and a half, moving beyond just testing to real deployments. However, progress has slowed down, with many projects stuck in the pilot phase or scaled back altogether. CIOs now face the challenge of figuring out how to reignite these initiatives and turn them into tangible business results.

Understanding the Current AI Landscape in EMEA

Research shows that while companies invested heavily in large language models and machine learning, many are now pulling back. Boards are demanding clearer proof of financial benefits before committing more resources. This shift isn’t about losing interest in technology but about ensuring investments deliver measurable value. Only a small fraction of organizations, around nine percent, have achieved concrete business outcomes from their AI projects in recent years.

Most AI initiatives tend to lose momentum because they don’t produce immediate results or impact broader organizational goals. They often remain in the pilot stage, with teams hesitant to scale due to uncertain ROI or integration challenges. As a result, many promising projects aren’t able to move into full production, leaving potential benefits unrealized and investments underutilized.

Rethinking How AI Value Is Measured and Funded

Traditional procurement methods focus mainly on direct costs, like software licenses and headcount reductions. But with AI, the true value often comes from indirect benefits, such as creating new revenue streams, improving efficiency, or reducing risk. For example, a predictive maintenance tool might not cut the engineering team’s size but can prevent costly equipment failures that would disrupt production.

Since organizations lack a standardized way to measure these intangible benefits, many AI pilots are judged on narrow metrics that don’t reflect their true impact. This can lead to valuable projects losing support before they reach full deployment. To overcome this, CIOs need to develop new ways to calculate ROI—ones that account for increased revenue, risk mitigation, and operational improvements—so AI initiatives can be justified as strategic investments rather than just cost centers.

Overcoming Infrastructure and Data Challenges

Moving AI projects from sandbox environments into full-scale operations requires significant ongoing investment. It’s not enough to test models in the cloud; companies need robust infrastructure, continuous data pipelines, and reliable maintenance. Many organizations face difficulties integrating modern AI tools with older legacy systems, such as on-premise Oracle or SAP databases.

For example, deploying retrieval-augmented generation architectures—where large language models access structured data—requires clean, well-organized information. Disorganized data storage can lead to poor results and “hallucinations,” or false outputs, which undermine trust in AI systems. Fixing this structural gap often means costly data cleanup and restructuring efforts before AI can truly deliver its promised benefits.

Addressing these technical hurdles is crucial for AI to move beyond pilots and become a core part of business operations. CIOs must prioritize building scalable, integrated infrastructure that supports ongoing AI development and deployment, ensuring models are accurate, reliable, and aligned with company goals.

In summary, EMEA CIOs have a clear path forward. They need to assess their current systems critically, redefine how they measure AI value, and invest in the infrastructure necessary for sustainable growth. Only then can AI start delivering on its full potential to transform businesses across the region.

Inspired by

• https://www.artificialintelligence-news.com/news/idc-how-emea-cios-can-jumpstart-ai-rollouts/

Sources

• idc.com