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Notably, a higher percentage of businesses (63%) that identify as high-growth – defined by revenue growth of 20% or more over the past three years – have already integrated generative AI into their respective supply chain operations to manage cost and operational challenges.
Nucleus Research surveyed more than 1,700 supply chain management leaders worldwide to understand how they are leveraging powerful technologies like artificial intelligence and machine learning to thrive while navigating challenges like supply chain disruptions, escalating costs, and skilled labour gaps. The study also uncovered anticipated future investments in these technologies.
“When workers are empowered to spend more time innovating—what humans do best—that’s where the real value creation happens. That is agility,” said Vaibhav Vohra, Chief Product and Technology Officer at Epicor. “Our 2024 Agility Index underscores the growing adoption of AI and other automation technologies as an essential factor in enabling supply chain businesses to better thrive and compete. These cognitive capabilities are coming together to empower workers and their businesses to more readily adapt to shifting market conditions and better serve their customers.”
Survey respondents indicated they are integrating generative AI into digital supply chain operations across various functions such as product descriptions, customer service chatbots, natural language querying, reporting, and in-application assistance. Specifically, the adoption of generative AI in customer service chatbots, noted by 72% of organisations, is highlighted as the most prevalent use case. This widespread implementation is attributed to the technology's ability to streamline customer interactions across various sectors.
Similarly, 67% of organisations currently employ generative AI for crafting product descriptions, leveraging the technology's capacity to analyse customer sentiment and forecast market demand. This enables a more informed approach to product design and feature development.
Businesses are also implementing machine learning most frequently in inventory optimisation (45%) and demand forecasting (40%), underlining the critical role of these technologies in managing inventory levels and accurately predicting future demand.
According to survey respondents, the greatest hope for the impact of automation technologies lies in increased efficiency and productivity (32%), cost savings (26%), and improved supply chain automation (23%). This reflects a strong belief in the potential of these technologies to drive significant improvements in supply chain management.
