Standard AIa San Francisco-based artificial intelligence company, today announced that it’s pivoting from its original give attention to autonomous point-of-sale systems to providing computer vision analytics solutions for retailers.
VentureBeat has also learned that the corporate is doing this now valued at $1.5 billion, a major milestone within the realignment of the 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 recent range will help retailers gain priceless insights into shopper behavior, optimize merchandising strategies, reduce out-of-stock items and forestall losses – all without facial recognition or collecting personal data.
“We found that by tackling specific challenges like analyzing shopper interactions and monitoring inventory, retailers and types can achieve ROI and generate immediate returns on their investment,” said Angie Westbrook, CEO of Standard AI, in an exclusive interview with VentureBeat. “This targeted approach allowed us to deliver a far more tangible solution.”
Adapting to the realities of the retail market
Founded in 2017, Standard AI initially got down to make fully autonomous checkout a widespread reality. However, the technology has not yet achieved mass adoption, due partially to its high cost and slower-than-expected consumer adoption.
“Autonomous checkouts simply haven’t caught on within the mass market,” Westbrock told VentureBeat. “While infrastructure and computing costs presented some obstacles, the largest challenge was that buyer adoption was slower than expected, leading to a poor return on investment.”
Standard AI recognized that the advanced AI models it developed for autonomous stores, which might track individual products and shopper actions with as much as 98% accuracy, had priceless applications beyond cashierless systems.
“The autonomous technology stack now we have built is the inspiration for all of our vision products,” said Westbrock. “We began first with probably the most complex problem of autonomous checkout… But this technology has applications far beyond autonomous checkout.”
Using AI to realize recent insights
The recent vision analytics products leverage Standard AI’s “autonomous tech stack” to generate real-time insights for retailers without the necessity for a totally autonomous setup.
Heatmaps show detailed shopper movement and product interaction data, not only foot traffic. Detecting out-of-stocks enables proactive inventory management. The system may even measure potential lost sales resulting from out-of-stock items.
“The amount of knowledge we will provide is (similar) to the best way Google Analytics has opened up e-commerce,” Westbrock said. “Knowing how shoppers interact with products and where they buy products has never been possible before.”
Partnering with Google Cloud and others will provide the computing infrastructure to bring this AI-driven future to retailers. But the important thing differentiator is Standard AI's software and data accuracy, refined through years of autonomous systems development.
Navigating a competitive landscape
The pivot puts Standard AI in competition with several major retail analytics providers comparable to IBM, Oracle, SAP SE, Salesforce and others. However, the corporate believes its unique full-journey tracking and highly accurate 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 stay competitive within the e-commerce era. As foot traffic has rebounded post-pandemic, brick-and-mortar stores are on the lookout for ways to optimize operations and merchandising to maximise sales per visit. Spending on artificial intelligence in retail is predicted to rise to $29.45 billion by 2028in accordance with a recent forecast from ReportLinker.
Standard AI's ability to trace individual products and shopper interactions sets it other than competitors that depend on more general foot traffic data. This granular level of knowledge might help retailers optimize store layouts, product placement and inventory management in ways not previously possible.
The change because the autonomous checkout faces headwinds
The company's transformation also reflects the challenges faced by startups in search of to develop comprehensive autonomous retail solutions that may usefully replace traditional checkout. Amazon, which is robust in its “Just exit” 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 been banking on a fast transition to autonomous shopping, moving to neighborhoods like vision analytics tools that may provide shorter-term value could provide a more viable path to commercialization and growth. Standard AI's move can be prone to prompt other autonomous retail technology developers to rethink their go-to-market strategies.
Still, the corporate stays optimistic about AI's long-term potential to remodel physical retail. “This is absolutely about constructing the infrastructure for the longer term,” Ms. Westbrock said. “We provide the software element to bring this whole infrastructure to life…to remodel and deliver this future-ready infrastructure.”
