Target is reassessing its artificial intelligence deployment strategy as vendors shift toward usage-based pricing models [1, 2].
This shift highlights a growing tension between the rapid integration of AI in retail operations and the unpredictable costs associated with scaling these technologies. As companies move from experimental pilots to full-scale implementation, the financial volatility of consumption-based billing can disrupt corporate budgeting and long-term planning.
Andrea Zimmerman, the head of Target in India, said the challenge during a Reuters summit in Bengaluru on Monday, May 25, 2026 [1, 3]. She said that the retailer is currently weighing the costs of AI tools because the new pricing structures are making it difficult to forecast expenses [1, 3].
Zimmerman said that the company is undergoing a fundamental transition in how it interacts with the technology. "We are moving from using AI to running on AI," she said [1].
Despite the operational ambition, the cost of these tools for employees has become a primary concern. Zimmerman said, "Usage-based pricing is complicating our planning" [1].
The reassessment comes as Target seeks to balance the efficiency gains of AI with the need for fiscal predictability. By evaluating the current vendor landscape, the company aims to determine if the current cost models align with its goals for widespread employee adoption, and operational scaling [1, 2].
“"We are moving from using AI to running on AI."”
Target's struggle reflects a broader industry trend where the 'honeymoon phase' of AI experimentation is ending. As enterprises transition from limited trials to embedding AI into the core of their business logic, the shift from flat-fee subscriptions to usage-based billing creates a 'success tax' — where increased efficiency and higher adoption lead to exponentially higher costs. This may push major retailers to seek more predictable pricing contracts or develop more proprietary internal tools to avoid vendor lock-in.
