**Global:** A new survey by NTT DATA reveals 95% of manufacturers see efficiency gains from GenAI, but 92% cite legacy systems and skill shortages as major barriers, urging urgent action on infrastructure upgrades and governance to avoid stalling advancements.
A recent global survey conducted by NTT DATA has highlighted a significant shift in the manufacturing sector towards the integration of generative AI (GenAI) into essential operations, specifically targeting supply chain and inventory management as primary focus areas. The study, which surveyed over 500 decision-makers across 34 countries, reveals that 95% of manufacturing leaders report measurable improvements in efficiency and profitability due to the adoption of GenAI technologies.
Among the respondents, a striking 91% believe that merging GenAI with digital twins will not only enhance asset performance but also bolster supply chain resilience. Key applications identified in the survey include supply chain planning, inventory optimisation, knowledge management, and quality control—demonstrating that GenAI is becoming a crucial tool for addressing market volatility and enhancing demand forecasting for supply chain directors.
Prasoon Saxena, Co-Lead of Products Industries at NTT DATA, stated, “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.”
Adding to this, a separate survey from The Access Group found that 57% of manufacturing employees in the UK are currently utilising AI tools, such as ChatGPT, in their day-to-day tasks. An impressive 93% attribute positive effects to these tools, mentioning factors such as reduced workloads and enhanced productivity. This grassroots adoption reflects a growing momentum in the sector, yet it highlights a concerning gap between the enthusiasm shown by frontline workers and the pace at which senior management is aligning with this technological shift.
Despite these evident benefits, the report also underscored several critical challenges that manufacturers must address to ensure the sustainable integration of GenAI. Notably, a significant 92% of participants acknowledged that legacy systems are hindering progress in leveraging GenAI. However, less than half of the organisations have conducted assessments to gauge their infrastructure’s readiness for such technological transitions. This disconnect raises the risk that without necessary upgrades to data systems and cloud environments, initiatives involving GenAI may stagnate at preliminary stages without further advancement.
The survey highlights talent as another major hurdle. Two-thirds of manufacturing leaders expressed concerns that their workforce lacks the necessary skills for effective GenAI utilisation, which could restrict both the adoption rate and the breadth of its implementation. Furthermore, only 41% of respondents strongly affirmed having adequate storage and computational resources to support the demands of AI workloads, pointing to broader hurdles concerning scalability.
Governance also remains a pressing issue. Less than half of the manufacturing leaders reported adherence to a robust ethical framework for managing AI utilisation. This lack of governance could expose companies to various risks, including compliance, privacy issues, and potential reputational harm as AI becomes increasingly integrated into critical decision-making processes.
As Saxena concluded, “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.”
Overall, while the advantages of GenAI adoption in manufacturing are becoming increasingly evident, the findings from NTT DATA serve as a cautionary indication that initial enthusiasm might not necessarily translate into long-term success. Manufacturers now face the critical task of moving from experimentation to a structured approach, necessitating thorough evaluations of their infrastructure, identification of skills gaps, and the establishment of proactive governance frameworks to navigate the complexities of AI integration effectively.
Source: Noah Wire Services
