BearingPoint released a study on October 1 showing that companies are generating measurable value from AI but struggling to scale initiatives across their operations, according to a Reuters report. 

The global study surveyed 1,050 C-suite executives and senior leaders across 13 countries in Europe, the United States and China, with only 13% of organizations scaling their AI initiatives fully in line with their original business case.

The gap is being driven by regulatory requirements and legacy technology, while organizations also face challenges connecting AI to trusted data, governance and existing business systems. 

About 40% of respondents identified legal regulations as a main barrier to scaling AI, while 34% cited integration with existing IT systems. 

BearingPoint, a European independent management and technology consultancy, said organizations that successfully scale AI are connecting it to financial accountability, trusted data, governance, architecture and workforce decisions from the start.

AI Delivers Value, But Scaling Remains Difficult

Nearly three-quarters of organizations that have implemented AI reported measurable top-line or bottom-line impact, yet almost three-quarters either changed the original scope of their AI initiatives or achieved less scale than anticipated.

The study also found that AI is becoming more deeply embedded in business operations. The share of organizations reporting deep AI integration rose from 7% in 2025 to 11% in 2026. However, more than one-third of organizations remain in the exploration or experimentation stages, showing that many companies have yet to move beyond pilots and early deployments.

Data and Governance Become Scaling Requirements

For chief data officers (CDOs), the findings put data foundations and governance directly into the AI scaling equation.

BearingPoint found that 54% of executives identify high-quality, trusted data as critical to scaling AI, followed by connected data across systems, clear AI governance and ownership, and data accessibility.

That makes AI scaling partly a data management problem. CDOs need to ensure AI systems can access reliable and connected data while maintaining clear ownership, permissions and governance as deployments expand across business units.

This becomes more important as organizations move from individual AI use cases toward broader AI usage and increasingly autonomous systems.