A study by K2Tech involving 150 executives from large industrial firms reveals that 79% of companies face a gap between management expectations for AI and their actual readiness. Only 5% of respondents reported full alignment between expectations and capabilities, while 30% noted a significant discrepancy due to unprepared data and infrastructure.
Data quality remains a primary hurdle, with only 5% of companies considering their data fully ready. Meanwhile, 37% of firms do not allocate specific budgets for AI and data management, handling these tasks within existing IT budgets. Only 14% of companies reported a budget increase of more than 50% for these initiatives.
Governance and security processes are also lagging. 19% of companies lack formal responsibility for generative AI risks, and 75% of firms developing internal AI solutions do not adhere to MLSecOps standards. Furthermore, 27% of respondents identified a lack of computing power as a major constraint, with many relying on a mix of internal clusters and cloud rentals.
Compliance with AI regulations is a challenge for critical information infrastructure (CII) entities. Only 12% of respondents consider themselves fully prepared for new regulatory requirements, while 48% report partial readiness. Another 21% anticipate a significant transformation of their data landscape is necessary to meet these standards.
