Standard AI, a San Francisco-based artificial intelligence company, recently announced a significant pivot in its business strategy. Originally focused on developing autonomous checkout systems, the company is now shifting its focus to providing computer vision analytics solutions for retailers. This strategic move comes as the company sees opportunities to provide retailers with valuable insights into shopper behavior, optimize merchandising strategies, reduce out-of-stock items, and prevent loss.
The decision to pivot from autonomous checkout systems to vision analytics solutions was driven by market challenges faced by Standard AI. The company’s initial goal of making fully autonomous checkout a widespread reality was hindered by high costs and slower-than-anticipated consumer uptake. According to the company’s CEO, Angie Westbrook, autonomous checkout technology has not yet reached mass adoption due to infrastructure and computing costs, as well as slower-than-expected shopper adoption rates.
While Standard AI initially developed advanced AI models for autonomous stores, the company realized that these models had valuable applications beyond cashierless systems. The technology developed for autonomous checkout, which can track individual products and shopper actions with up to 98% accuracy, serves as the foundation for the new vision analytics products. These products leverage Standard AI’s “autonomous tech stack” to provide real-time insights for retailers without requiring a fully autonomous setup.
Collaboration and Competition
Standard AI has partnered with Google Cloud and other entities to provide the necessary computing infrastructure for its AI-driven vision analytics products. The company now competes with major retail analytics providers such as IBM, Oracle, SAP SE, and Salesforce. However, Standard AI believes that its unique full-journey tracking and high precision AI models give it a competitive edge in the market. The company emphasizes the importance of data accuracy, stating that “the only thing worse than no data is bad data.”
As retailers increasingly adopt data-driven strategies to remain competitive in the e-commerce age, spending on artificial intelligence in the retail sector is expected to grow substantially. Standard AI’s ability to track individual products and shopper interactions sets it apart from competitors that rely on more general foot traffic data. This granular level of data allows retailers to optimize store layouts, product placement, and inventory management in ways that were previously impossible.
Looking Towards the Future
The shift in focus from autonomous checkout systems to vision analytics solutions reflects the challenges facing startups in the autonomous retail space. While companies like Amazon have invested in fully autonomous stores, widespread adoption of this technology remains limited. Standard AI’s pivot to vision analytics tools that offer nearer-term value may present a more viable path to commercialization and growth for AI startups in the retail sector.
Standard AI’s strategic pivot to providing vision analytics solutions for retailers demonstrates the company’s adaptability in response to market challenges and opportunities. By leveraging its advanced AI models and focusing on data accuracy, Standard AI aims to help retailers gain valuable insights and optimize their operations in an increasingly competitive retail landscape.
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