While only 31% of manufacturers describe themselves as "transformational" in their AI adoption – the lowest share among sectors surveyed – they consistently report lower barriers to reaching that maturity, according to new benchmark data from S&P Global Market Intelligence, in collaboration with Vultr.
Key takeaways:
· The benchmark study categorizes AI maturity in three stages: Operational (early functional gains), Accelerated (AI across multiple functions), and Transformational (AI embedded into core operations).
· Currently, 25% of manufacturing respondents remain Operational compared to 19% across all industries, and only 31% have reached Transformational maturity. But across nearly every organizational barrier – skills shortages, data quality, security, cultural alignment, and more – manufacturers report materially lower severity.
· The skills shortage register stands at 46% for manufacturers, compared to 62% for all other respondents.
· 35% of manufacturers have built or are in the process of creating internal PaaS infrastructure. Within two years, the figure is projected to reach 45%, while reliance on hyperscaler-managed PaaS is expected to decline from 56% to 42%. This isn't a migration away from hyperscalers; manufacturers still run 30% of their training workloads and 28% of their inference on major public clouds, both figures well above the industry averages. But it does represent a fundamental redesign of how manufacturers govern and orchestrate AI.
· The average number of AI models in production among manufacturers has declined from 242 today to a projected 189 next year. In an environment where "more AI" is often synonymous with "better AI," manufacturers are moving in the opposite direction.
