Key takeaways
Margin pressure
AI security
Supply chain resilience
AI readiness
Workforce capacity
Key challenges in smart manufacturing
Factories are becoming more intelligent, but the infrastructure, resilience, margins, and skills needed to support that transformation are under growing pressure. Rising AI security risks, higher input costs, supply-chain uncertainty, data-readiness gaps, and accelerating technician demand are forcing manufacturers to strengthen both their technology foundations and operating capabilities.
AI security gaps threaten factory technology
AI deployment is accelerating, but limited visibility and weak infrastructure are creating significant technology risks for manufacturers. The 2026 Thales Data Threat Report found that 67% of manufacturing respondents identified rapid changes in AI ecosystems as their leading AI security concern, while only 31% had complete visibility into where their data was stored. Honeywell’s September 2026 report found that just 21% of surveyed industrial organisations had a complete inventory of their operational technology assets, while major cyber incidents caused an average of 16.2 hours of downtime. These gaps highlight the need for stronger data visibility, comprehensive OT asset management, and cybersecurity foundations capable of supporting secure AI integration.
Rising input costs squeeze manufacturing margins
Profitability is coming under pressure as manufacturers expand production while facing accelerating input costs. In September 2026, U.S. manufacturing recorded a 54.5 PMI, indicating continued expansion, but its input-price index rose to 77.9 from 71.1 in August, signalling sharper raw-material cost pressure. The National Association of Manufacturers’ Q3 2026 outlook found that firms expect raw-material and other input costs to increase by 5% over the next 12 months, with 77.3% citing freight rates and 74.1% fuel costs as business challenges. German manufacturing also saw input-price inflation reach its highest level since June in September, even as production and new orders grew, highlighting the risk that rising costs can erode margins during periods of expansion.
Supply chain visibility falls behind rising risk
Supply-chain resilience is becoming harder to maintain as geopolitical uncertainty, transport disruptions, and material shortages threaten production continuity. In September 2026, 82% of manufacturing professionals were concerned about supply-chain disruptions over the following 12 months, with 62% identifying tariffs and trade policy as the greatest anticipated risk, followed by transportation and logistics at 54% and material shortages at 42%. A September 2026 global survey of more than 250 supply-chain executives found that only 31% of organisations had full visibility across their supply networks, while just 20% conducted monthly supply-chain risk assessments. Limited visibility and infrequent monitoring can leave manufacturers less prepared to anticipate supplier disruptions, respond quickly, and maintain reliable production schedules.
Data foundations define AI transformation readiness
Scaling AI beyond individual use cases is exposing gaps in the data foundations needed for enterprise-wide transformation. In September 2026, Cloudera’s manufacturing research found that 82% of manufacturers had visibility into where their data resided, but only 58% said all or nearly all of their data was fully governed, limiting their ability to scale AI securely. A September 2026 Seagate report covering more than 2,700 enterprise technology decision-makers found that 99% expected AI to increase storage requirements over the next three years, yet only 38% considered themselves fully prepared. These gaps make stronger data governance and infrastructure readiness critical to turning AI adoption into scalable transformation.
Technician demand outpaces available skills
Workforce requirements are shifting rapidly as automation increases demand for technically skilled manufacturing roles. Between 2025 and 2030, U.S. manufacturing technician employment could grow six times faster than production occupations, while employers may need to fill 2.3 million technician job openings across manufacturing and related industries. Skills shortages could increase downtime and constrain production capacity, making recruitment and workforce development increasingly important, while UK manufacturing and engineering job advertisements mentioning AI skills rose from 1.3% in 2025 to 2.02% in 2026.
Next steps
Manufacturers should strengthen cybersecurity, OT asset visibility, data governance, and supply-chain monitoring while aligning technology investment with the cost and infrastructure requirements of scalable AI adoption. Targeted technician recruitment, workforce development, and AI-focused skills programs will also be essential to protect production capacity and convert automation investment into sustainable productivity gains.
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Smart manufacturing should deliver measurable business value, not just new technology.
