While respondents reported already using generative AI numerous ways in their organizations, less than half (46 per cent) have applied the technology within their supply chains, with 34 per cent of respondents planning to implement it in the future.
Of respondents using or planning to use generative AI in their supply chain, four in 10 (43 per cent) said their primary reason is to unlock prescriptive analytic capabilities for customer or sales order fulfillment, such as tapping in-house and external data to identify SKUs and make recommendations to category managers to adjust pricing, promotions, assortment, and delivery and provide mitigation options. Other major drivers include: the ability to analyze information across disparate systems (35 per cent); generating accurate sales predictions based on historical data, trends, seasonality (34 per cent); and inventory optimization (34 per cent).
“Generative AI has the potential to revolutionize supply chain management, logistics and procurement, but only if it’s underpinned by reliable, quality data – that’s where many organizations face challenges. Their data is not managed and organized in an optimal way,” says Mr. Polyakov.
Indeed, two-thirds of respondents said one of the main challenges to implementing AI is having non-validated, inaccurate data inputs, which, if used to train the large language models that underpin generative AI platforms, could potentially lead to “hallucinations” or inaccurate or misleading outputs.
Seven in 10 (71 per cent) of respondents said their inability to access or leverage data is also a challenge in implementing generative AI.
“Retailers have access to enormous amounts of data – including customer data, sales data and supplier data to name a few – and that data can be leveraged for using generative AI. But to make that data useful for a generative AI system, retailers must make sure it’s clean, organized and structured. That’s a crucial part of any successful generative AI implementation,” Mr. Polyakov adds.