Find the original article published by Food Logistics here.
The Generative AI (GenAI) market is growing at 31.1% CAGR and will reach $235 billion in 2028, however, significant technology challenges prevent many companies from realizing the true business value of artificial intelligence (AI) implementations, according to a new report released from Coresight Research in partnership with Digital Wave Technology.
“The transformative potential of artificial intelligence for the retail and consumer sectors is indisputable, and it will affect every facet of business connected to both internal productivity and external consumer interactions in the near future,” says Deborah Weinswig, CEO, Coresight Research. "Nevertheless, it is imperative that organizations empower themselves with the right tools and technologies to dismantle data silos and ensure a seamless, integrated approach across all operational functions. The scale of potential impact is substantial, and those enterprises that do not adeptly navigate the transition to AI risk falling behind. We believe that companies are worrying about FOBO, the fear of being obsolete, if they do not get started with an AI roadmap.”
“The lack of high quality and unified data has been a huge barrier to successful AI adoption, but Digital Wave Technology has changed that paradigm with our ONE Platform,” says Lori Schafer, CEO, Digital Wave Technology. “Many of the world’s largest consumer-facing companies rely on the ONE Platform as their enterprise AI solution for enhancing customer satisfaction, profitability, improving product availability and driving greater efficiency.”
Key takeaways:
Only 41% of U.S.-based retailers have price planning “fully integrated” with complementary business functions including assortment planning, allocations, promotional planning and markdown optimization.
52% of U.S. retailers surveyed cannot execute at least 10% of their promotional campaigns properly in any given selling period.
On average, retailers misprice 10% of their products in any given sales period.
The lack of clean, high-quality data and analytical capabilities are critical barriers to effective AI deployment.
To address these challenges, a new generation of AI-native platforms is emerging, with master data management at the core.