A new methodology for assessing AI maturity in industrial enterprises has been introduced, utilizing a 30-question audit framework. The assessment evaluates two dimensions: technical and organizational, with the final maturity level determined by the lower of the two scores, adhering to a 'bottleneck' principle.
The audit process requires evidence for each response and incorporates 20 specific gates to validate progress. The final output provides a maturity level, identifies organizational gaps, highlights bottlenecks, evaluates scaling barriers, and establishes a 12-month development plan.
Data from MIT CISR indicates that 62% of companies in the first two stages of AI maturity underperform financially compared to their industry peers. Meanwhile, Deloitte reports that only 6% of companies achieve a return on investment in under a year, with most requiring 24–48 months.
Large organizations often struggle to transition AI pilots into production compared to mid-sized firms. According to Gartner, at least 30% of generative AI projects are expected to be discontinued after the pilot phase by the end of 2025 due to data quality, cost, and risk management issues.
The methodology aims to replace subjective self-assessments with objective measurements. It mandates that a project must be fully operational in a production environment to be considered successful, rather than relying on the number of launched pilots or estimated economic effects.
