Study: Retail, consumer goods' AI adoption slow — but progressing
Retail and consumer goods (CG) companies are accelerating their adoption of artificial intelligence (AI) and analytics, however they still face challenges impacting their strategic use of the technology.
This was the message in the newly released “Progress Under Pressure: 2026 Retail and Consumer Goods Analytics Study,” from Consumer Goods Technology.
AI remains at the center of exploration, planned usage and investments across retail and CG companies, with more than half of respondents reporting that they have 10 or more employees dedicated to analytics. Both CGs and retailers reported a gradually larger representation of their IT budget dedicated to AI versus previous years.
Yet, many organizations are still operating with relatively small analytics budgets. More than one-third of retailers said 5% or less of their IT budget is currently dedicated to analytics. However, expansion is on the horizon: one-third of CGs expect to spend 25% or more of their IT budget on analytics in 2029, while about 40% of retailers expect to spend somewhere between 15% and 25%, according to the report.
Analytic and predictive AI remain top priorities for both industry segments, with 61% of companies currently using these solutions or planning to within 12 months. Generative AI is a close second with 59% of companies currently using the solution or planning to within 12 months. While interest in agentic AI is growing, it still lags behind as less than half (49%) of companies currently use agentic AI or plan to within 12 months, the study said.
[READ MORE: Visa: Consumers, businesses grow in acceptance of AI agents]
Business areas where agentic AI could help the most include inventory planning, pricing and allocation (for CGs), and consumer relationship management, consumer-facing service and social media (for retailers), according to the report.
The strongest impact of AI strategies stems from shared data models — and both retailers and CGs seem to be making progress toward achieving this goal. Currently, 64% of CGs reported they are moving in this direction compared to 55% last year. Meanwhile, 63% of retailers are on the right track versus only 43% last year.
The top AI application for both groups is assortment planning, but category management, social media analytics and digital commerce have gained ground over the past year. For retailers, the biggest gains in AI usage are across inventory management and category management, replacing transportation and replenishment as the most mature areas in last year’s study.
Digital shelf analytics, a new category in this year’s report, is considered to be one the least priorities for both groups (72% of CGs and 76% of retailers); however, it shows plenty of potential for future growth.
Where companies seems to be struggling is in their overall AI strategies and plans. For example, both retailers and CG’s said they lack a clearly articulated analytics strategy (26% and 44%, respectively). Both groups are also concerned about a lack of budget (34% for retailers, 46% for CGs), according to the study.
Both groups also lack AI-savvy leaders. Specifically, 37% of retailers and 36% of manufacturers are concerned that they don’t have the right staff in place to lead analytics strategy, the study revealed.
Another struggle is limited access to analytics toolsets and an inability to integrate data from multiple sources. For example, 28% of retailers feel they have a limited toolset compared to 45% of CGs. Meanwhile, 23% of retailers are unable to integrate data across sources compared to 39% of manufacturers, the study revealed.
Despite their struggles, both segments continue to move forward in their AI journeys, especially across software upgrades. For example, the biggest priority for retailers is updating generative AI platforms or copilots, as well as upgrading in-store analytics platforms (47% across both groups). Meanwhile, a majority of CGs (51%) plan to upgrade master data management this year ( this is a priority for 39% of retailers).
Other priorities include improving data quality (34% for retailers, 36% for manufacturers); upgrading to AI/machine learning (ML) platforms (37% of retailers and 38% for CGs), and improving data governance, privacy and security platforms (37% for retailers and 33% for manufacturers), the study said.
