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Standard AI, a San Francisco-based artificial intelligence company, announced today that it is pivoting from its original focus on autonomous checkout systems to providing computer vision analytics solutions for retailers.
VentureBeat has also learned that the company is now valued at $1.5 billion, a significant milestone as it shifts its business strategy. In conjunction with this strategic shift, Standard AI has promoted COO Angie Westbrock to CEO and SVP of Technology Strategy David Woollard to CTO.
The company says its new suite of products will help retailers gain valuable insights into shopper behavior, optimize merchandising strategies, reduce out-of-stock items, and prevent loss — all without using facial recognition or collecting personally identifiable information.
“What we found is that by addressing specific challenges like analyzing shopper interactions and monitoring stock levels, retailers and brands could reach ROI and get immediate returns on their investment,” said Standard AI CEO Angie Westbrook in an exclusive interview with VentureBeat. “This targeted approach allowed us to deliver a much more tangible solution.”
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Adapting to the realities of the retail market
Founded in 2017, Standard AI initially set out to make fully autonomous checkout a widespread reality. However, the technology has not yet reached mass adoption, due in part to high costs and slower than anticipated consumer uptake.
“Autonomous checkout just hasn’t taken off to mass market scale,” Westbrock told VentureBeat. “While infrastructure and computing costs have posed some barriers, the primary challenge has stemmed from slower than anticipated shopper adoption, resulting in low returns on investment.”
Standard AI realized that the advanced AI models it developed for autonomous stores, which can track individual products and shopper actions with up to 98% accuracy, have valuable applications beyond cashierless systems.
“The autonomous tech stack that we’ve been building is the foundation for all of our vision products,” said Westbrock. “We started with the most complex problem first with autonomous checkout…But this technology has applications well beyond autonomous checkout.”
Leveraging AI to provide new insights
The new vision analytics products leverage Standard AI’s “autonomous tech stack” to generate real-time insights for retailers without requiring a fully autonomous setup.
Heat maps show detailed shopper movement and product interaction data, not just foot traffic. Out-of-stock detection allows proactive inventory management. The system can even measure potential lost sales from items being out-of-stock.
“The level of information that we can bring is [similar to] how Google Analytics unlocked e-commerce,” said Westbrock. “Knowing how shoppers are interacting with products, where they’re buying products from — this is something that’s never been available before.”
Partnering with Google Cloud and others provides the computing infrastructure to make this AI-driven future accessible for retailers. But the key differentiator is Standard AI’s software and data accuracy, honed through years of developing autonomous systems.
Navigating a competitive landscape
The pivot puts Standard AI in competition with several major retail analytics providers such as IBM, Oracle, SAP SE, Salesforce, and more. But the company believes its unique full-journey tracking and high precision AI models give it an edge. Westbrock told VentureBeat “the only thing worse than no data is bad data,” emphasizing Standard AI’s data fidelity.
The shift comes as retailers look to adopt more data-driven strategies to remain competitive in the e-commerce age. While foot traffic has rebounded post-pandemic, brick-and-mortar stores are seeking ways to optimize operations and merchandising to maximize sales per visit. Spending on artificial intelligence in the retail sector is predicted to grow to $29.45 billion by 2028, according to a recent projections from ReportLinker.
Standard AI’s ability to track individual products and shopper interactions sets them apart from competitors that rely on more general foot traffic data. This granular level of data can help retailers optimize store layouts, product placement, and inventory management in ways that were previously impossible.
Pivoting as autonomous checkout faces headwinds
The company’s shift also reflects the challenges facing startups seeking to develop full-scale autonomous retail solutions that can viably replace traditional checkout. Amazon, which has been investing heavily in its “Just Walk Out” technology, has opened a handful of fully autonomous stores but has yet to expand the model significantly beyond its own footprint.
For AI startups that had banked on a rapid transition to autonomous shopping, pivoting to adjacencies like vision analytics tools that can provide nearer-term value may offer a more viable path to commercialization and growth. Standard AI’s move is likely to prompt other autonomous retail tech developers to reassess their go-to-market strategies as well.
Still, the company remains optimistic about the long-term potential for AI to transform physical retail. “This is really about building the infrastructure for the future,” said Ms. Westbrock. “We provide the software element to bring all of this infrastructure to life…to transform and provide this future-enabled infrastructure.”
Update: 2:12 p.m. PT: In an interview with VentureBeat, Garry Tan, CEO of Y Combinator and a board member of Standard AI, commented on the company’s strategic shift and the potential impact of AI on the retail industry:
“The promise of AI has given way to unlocking new capabilities for every type of industry. Particularly in the retail space, the rapid innovation of AI has the potential to enhance personalization, optimize inventory management, and streamline supply chain operations. Standard AI is leading this new wave with its Vision Analytics platform — this type of data just hasn’t existed previously for retail. With this pivot, Standard AI is bringing a deep understanding of the relationships between people, products, and interactions in the physical retail environment.”
