**Global:** A new NTT DATA survey finds 95% of manufacturing leaders cite improved efficiency with GenAI, yet 92% flag legacy systems as a barrier. Skills shortages and weak governance frameworks also threaten wider implementation despite growing employee use of AI tools.
A recent global survey led by NTT DATA has highlighted the accelerating integration of generative AI (GenAI) within the manufacturing sector, particularly focusing on supply chain and inventory management. The report reveals that an impressive 95% of manufacturing leaders report measurable improvements in operational efficiency and profitability as a result of adopting GenAI technologies. Furthermore, 91% of respondents believe that combining GenAI with digital twins will enhance asset performance and bolster supply chain resilience.
Conducted among over 500 decision-makers from 34 countries, the survey identifies several key applications of GenAI, including supply chain planning, inventory optimisation, knowledge management, and quality control. These areas have become increasingly vital for addressing volatility, honing demand planning, and enhancing throughput in manufacturing operations.
Prasoon Saxena, Co-Lead of Products Industries at NTT DATA, remarked, “AI is streamlining processes and redefining what’s possible. GenAI helps organizations achieve flexibility in fast-changing business environments, especially in the face of uncertain tariff policies worldwide.”
The findings are further corroborated by a separate survey conducted by The Access Group, which indicates that 57% of manufacturing employees in the UK are utilising AI tools such as ChatGPT in their day-to-day tasks. An overwhelming 93% of these employees report positive impacts, including reduced workloads and increased productivity. This organic adoption reflects a notable momentum in the industry; however, it also emphasizes the necessity for senior leaders to better understand the capabilities and applications of AI in practice.
Despite the evident benefits of GenAI, the report uncovers significant structural and strategic challenges that manufacturers must navigate. Notably, 92% of respondents acknowledged that legacy systems pose barriers to GenAI implementation, yet fewer than half have conducted assessments to gauge their infrastructure readiness. This disconnect raises concerns that without fundamental upgrades to data systems and cloud environments, AI initiatives may remain stagnant or at the pilot stage.
Talent scarcity is another pressing issue. Approximately two-thirds of the survey participants indicated that their workforce lacks the necessary skills to utilise GenAI effectively, potentially hindering both the breadth and pace of adoption. Furthermore, only 41% expressed strong confidence in their organisation’s storage and computing capacities to support AI workloads, highlighting deeper issues surrounding scalability.
In addition to technological and talent-related challenges, governance remains another area of concern. Fewer than half of the manufacturing leaders reported that their organisations adhere to a robust ethical framework for managing AI usage. This lack of governance raises potential risks related to compliance, privacy, and reputation as AI tools become increasingly integral to decision-making processes.
Saxena cautioned, “The most successful manufacturing organizations have already integrated GenAI into essential operations. Companies failing to plan, deploy, and govern GenAI strategically will not only have a problem—they may be planning to fail.”
As the manufacturing industry increasingly embraces GenAI, the focus now shifts towards converting experimentation into structured implementation. This involves carefully evaluating infrastructure capacities, identifying skills gaps, and establishing governance frameworks that are both practical and forward-looking. While GenAI has the potential to accelerate numerous processes, its successful integration requires a steady and informed approach.
Source: Noah Wire Services
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