‘A Doom Loop’
More is not always more in generative AI investments — a realization that is dawning on many companies as early excitement around the technology settles into expectations for tangible results.
“In their pursuit of efficiency and time savings, CSCOs may be inadvertently creating a productivity ‘doom loop,’ whereby they continuously pilot new GenAI tools, increasing employee anxiety, which leads to lower levels of productivity,” said Sam Berndt, senior director in Gartner’s supply chain practice. In this sense, Bendy said supply chain leaders need to examine their overall strategy rather than bring more and more generative AI tools into the workplace.
All is not lost, however. In terms of tracking the ROI of generative AI investments, Gartner recommends supply chain leaders instead rethink their approach, shifting focus from pure efficiency gains to broader organizational productivity — a shift they said will require greater integration of frontline workers, addressing employee anxieties, and prioritizing applications that drive creativity and innovation.
To start this shift, Gartner said CSCOs should focus on refining their generative AI strategies across three areas: use cases, talent, and management. Instead of just automating for speed, supply chain teams should tap into AI’s creative potential, aiming to unlock smarter workflows, better decision-making, and opportunities beyond basic time savings.
“[U]se-cases encourage employees to utilize GenAI tools to approach their strategic tasks more thoughtfully, with an emphasis on those that incorporate interactions with direct peers and colleagues,” the report outlined.
Supply Chain & AI
Despite mixed results on productivity, supply chain leaders at many leading CPGs have shown enthusiasm for the technology and started the process of deployment of generative AI in their operations, with applications ranging from demand forecasting to procurement.
General Mills is just one example of a CPG strategically deploying AI in the supply chain, using the technology to shift its supply chain to an “always-on” model. By enhancing procurement data, it identifies cost gaps in ingredients and packaging, reducing waste by over 30%.
Similarly, P&G is using data and machine learning algorithms to optimize truck scheduling to minimize driver idle time, as well as tapping AI tools to optimize fill rates and for dynamic routing and sourcing optimization.
More broadly, many CPGs are focused on transforming the way they think about supply chain in their business. Gallo, for example, recently outlined how it is planning to elevate the function in their organization, creating a Center of Excellence built around four key pillars.
