There are many multibillion dollar industries where massive incumbents make data-dependent decisions that can be enormously enhanced by AI. But what are the keys to success in building—and selling—AI solutions that provide a huge competitive advantage to your customers, rather than a techno-boondoggle?
It’s imperative to start off with a deep understanding of current industry workflows and needs. AI is really good in situations where delivering an answer based on high volumes of data quickly is the critical task. In insurance, for example, underwriting workflows may be more data intensive than claims adjustment situations, thereby offering a different set of opportunities. It’s important to identify one of these needs and focus on it. This is about end-to-end solutions, not about creating a general platform. At first, practical applications should be built to minimize changes or disruption to current customer workflows. Don’t think about what the customer should want. Rather, build and sell solutions that fit their models today.
Ryan Kottenstette details how to systematically identify both the use cases best suited to AI and those better served by humans. Ryan then explains how to build end-to-end solutions that complement the existing backend systems you’ll be integrating into and how to go about selling a practical AI solution into a new industry.
Ryan Kottenstette is cofounder and CEO of Cape Analytics, a leading computer vision and machine learning company backed by DCVC, Formation8, and XL Innovate, which ingests geospatial imagery and extracts property data for millions of homes at massive scale. A former principal at Khosla Ventures, Ryan has been at the forefront of using AI to disrupt traditional industries: he was a lead investor in Blue River Technologies, which sold to John Deere in 2017 for $300M.
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Might you cite some specific data on benefits of AI using pre-AI and post-AI performance data? Bob