GenAI Spending to Rise Despite High Failure Rate With PoCs: Gartner
Generative AI experimentation has flooded nearly every area of business in consumer goods, from accelerating product development through automated design capabilities to boosting product personalization.
Worldwide spending on the technology is set to increase drastically this year — by 76.4% compared to 2024, totaling $644 billion, according to Gartner. Due to high failure rates associated with generative AI proof of concepts, however, spending and implementation trends will likely shift.
John-David Lovelock, distinguished VP analyst at Gartner, said in a statement that there’s an ongoing paradox with the technology that he expects to persist through 2026. While CIOs are lowering their expectations of the technology’s impact due to POC failures and disatisfactory results, “foundational model providers are investing billions annually to enhance GenAI models' size, performance, and reliability,” he added.
Also read: Gartner’s 2025 Supply Chain Tech Trends
Despite these model improvements, CIOs will look to invest in features from existing software providers rather than focusing on POCs and self-development efforts. This is largely due to increased scrutiny on internal projects compared to off-the-shelf solutions.
Getting To Scalability
Just one in six proofs of concept makes it to the final stage of development, according to a conversation with Ramesh Kollepara, Kellanova chief technology officer, at Groceryshop last year, and just one in five of these actually scale. Therefore, he said it’s critical for consumer goods companies to temper their expectations.
Some CPGs such as Procter & Gamble have chosen to test the technology more widely rather than conducting smaller trials first. With its internal generative AI tool ChatPG, for instance, it tested with thousands of employees so it could get the technology in use more quickly.
Other companies such as Unilever might have a larger appetite for pilots, relying on a switchboard approach that uses AI itself to pinpoint the different combinations of models that can solve business problems through scalability with high-value ROI.
Companies will also need to prepare for the direct impact generative AI will have on consumer behavior, as 80% of the overall spending on this technology will be driven primarily by hardware integrations in devices such as consumer smartphones and PCs.
