A Supply & Demand Chain Executive report shows 72% of manufacturers have adopted AI amid reshoring pressures and labor shortages — but only 10% have scaled it enterprise-wide. Reshoring activity surged to 70% (from 33% in 2024), generative AI adoption hit 65% in 2024 (up from 48%), and quality control is the top AI use case at 50%. Key barriers include high implementation costs (40%), data security concerns (39%), and integration challenges (38%). Leadership fears hesitation more than overreach: 60% worry about moving too slowly on AI.
According to sdcexec.com, nearly 72% of manufacturers have adopted artificial intelligence to respond to reshoring mandates, labor shortages, and rising operational costs — yet only 10% have implemented AI at scale across their networks.
Reshoring Surge and AI Adoption Acceleration
70% of manufacturers have completed or are actively reshoring operations — a dramatic increase from 33% in 2024. This shift is driven primarily by workforce constraints and cost pressures, with 46% citing increased operational complexity, 44% flagging higher labor costs, and another 44% identifying supply chain logistics adjustments as top challenges.
Generative AI adoption rose to 65% in 2024, up from 48% the prior year. Meanwhile, overall AI adoption climbed to 72%, up from 53% in 2024 — though 22% report implementations still underway and the vast majority remain below scale.
Top Use Cases and Strategic Priorities
Quality control leads AI application areas at 50%, followed by IT operations (46%) and supply chain management (45%). Supporting capabilities include AI/ML-enabled decision support (54%), IIoT/Edge devices (50%), and predictive maintenance tools (50%).
Supply chain resilience has improved markedly: 71% of manufacturers now describe their supply chains as resilient — up from 50% in 2024. Similarly, 66% feel prepared to address current issues, up from 58% in 2024. Yet 70% prioritized cost-cutting over resiliency in the past 12 months — underscoring persistent financial pressure.
Barriers and Workforce Realities
High implementation costs hinder broader deployment for 40% of respondents; data security concerns affect 39%; and integration difficulties with legacy systems challenge 38%. Nearly half (45%) cite internal skill gaps as a major obstacle when integrating new technologies.
The most difficult roles to fill include IT/tech specialists (60%), quality assurance staff (49%), and management positions (39%). A striking 84% agree that manufacturing’s outdated reputation as a labor-intensive, low-skill industry impedes recruitment of top talent.
Risk Perception and Strategic Mindset
Leaders express greater concern about moving too slowly on AI than moving too fast: 60% fear being overly hesitant, versus 40% worried about excessive aggression. As Eddy Azad, executive chairman at Parsec Automation Corp., states:
“Manufacturers are feeling pressure from reshoring imperatives, supply chain volatility, workforce shortages, cost pressure, and accelerating technological change. Operational excellence has shifted from a competitive differentiator to a prerequisite for survival.”
Just 37% of manufacturers currently maintain a unified, data-driven strategy — though 60% are either planning or implementing one, up from 40% in 2024. More than two-thirds (69%) operate hybrid environments blending legacy and modern equipment — reflecting the sector’s transitional technology stack.
- Top supply chain challenges: complexity and lack of visibility (34%), raw material shortages and volatility (33%), transportation disruptions (31%)
- Top mitigation strategies: implementing new technology (58%), strengthening supplier relationships (58%), increasing inventory levels (53%)
- AI replacement outlook: 53% believe AI could replace at least half the roles in certain departments; 47% disagree
Source: sdcexec.com
Compiled from international media by the SCI.AI editorial team.
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