Consumer firms targeting AI spending in wrong places, finds Blue Ridge Partners study
Consumer companies are spending their AI budgets on initiatives that are easier to present to a board rather than ones that drive the highest ROI, according to a recent survey from Blue Ridge Partners. The McLean, VA-based growth consultancy polled more than 300 consumer industry leaders in the US.
According to the Consumer Industry Commercial AI Study, a small group of firms are capturing outsized gains while the majority invest in lower-return areas.
Just 8% of surveyed consumer companies are funding AI use cases with the highest payoffs – commercial planning, revenue growth management, and AI-driven innovation – which drive revenue growth improvements of 16% to 40%. Instead, most budgets are funneled to marketing content development and ad creative optimization, which the survey found deliver comparatively modest returns.
The most under-used opportunity is AI-powered customer onboarding, which the study says can drive a 41% improvement in customer lifetime value. Ninety-one percent of consumer companies, however, aren’t investing in the area.
Counterintuitively, the firms deriving the strongest revenue growth from AI investments are spending less on it than their lower-performing counterparts. High-impact companies spend an average of $15 million on commercial tech and AI versus $20 million among companies pursuing lower-impact use cases.
“Consumer companies are sitting on significant untapped revenue growth,” said Carrie Shea, managing director and head of the consumer practice at Blue Ridge Partners and lead author of the study. “Our research shows the path to capturing it is not spending more on AI. It is spending it on the right commercial use cases.”
The Blue Ridge study found a pattern of a “AI theater,” wherein companies pilot AI projects based on how easy they are to present to a board. Surveyed leaders highlighted lower perceived risk, simpler data requirements, and polished vendor demos as reasons for choosing these initiatives.
The study identified four traits shared by high-performing companies: they are likelier to be using AI agents; expect AI to grow their workforce; report more confidence in building a sound AI business case; and favor buying AI tools over building them in-house.
The study also provided four recommendations for closing the performance gap: benchmarking AI spending against actual revenue impact; moving from scattered pilots to full process redesign, standardizing adoption with repeatable toolkits and vendor frameworks; and prioritizing use cases for margin potential instead of ease of demoing.
“The highest-impact AI use cases in consumer are not out of reach," Shea said. “They require more discipline and cross-functional commitment, but the revenue growth potential is significant, and the research proves it.”
