How To Separate The Wheat From The Chaff In A Sea Of Ventures With AI Use Cases Posted on : Sep 08 - 2020

Had you invested in a dedicated Artificial Intelligence ETF one year ago, you could have made close to a 40% return. Investing in individual companies that trust in AI use cases could have been even more lucrative. Consider Netflix, which uses machine learning for content recommendation, or Amazon, which uses AI not only for product recommendations but also in its fulfillment centers, AWS and Echo: both more than doubled their market capitalization over the last 12 months, though admittedly driven by coronavirus. Irrespective of the pandemic, AI is a growth business – which is rightfully reflected in share prices – with spending on AI systems projected to grow annually by 28%, according to a report by IDC.

Not only established organizations have found AI to be beneficial. Many start-ups now tap into the power of AI. A case in point is Lemonade, the insurtech with a mission to disrupt insurance by using AI and behavioral economics. Lemonade aims to offer a delightful user experience based on a hassle-free sign-up, lower prices and quicker claims payments. Founded in 2015 in Tel Aviv, it went public in July and surged 140% on its debut day, reflecting the appeal of its AI-powered chat bots to investors and customers alike. But how can you sort the wheat from the chaff in an ocean of ventures branding themselves as being powered by AI?

 “There are two criteria that set the great AI ventures apart from the rest,” says Scott Beechuk, Partner at Norwest Venture Partners. He focuses on early- to late-stage investment opportunities in enterprise SaaS, with a focus on companies building business applications taking advantage of AI and advanced analytics. “One, the most successful AI ventures narrow their focus and scope – either by vertical or business function – to solve a specific set of business challenges. Two, they have expertise in both data science and software architecture," Beechuk argues.

A narrow focus is prerequisite for the humility to not want to boil the ocean. In other words, specialization is a driver for the pragmatism needed in new ventures, which depend on bringing products to market fast. The potential to expand into other areas of application is important to attract investors, but nothing beats an early proof-of-concept in a confined space. However, only when that is paired with the latest expertise in data and software engineering does the venture have a shot at making it big.

"Excellence in AI is not just about engineering. It is the combination of engineering, mathematics, and go-to-market strategy that produces winning companies. Too often, we see companies founded by data scientists who have some bleeding-edge deep learning algorithms but are not strong business leaders. Without someone who understands the technology and knows how to limit its scope to narrow business problems, most of these companies end up building horizontal solutions that either never make it to market or never become great at solving any single problem," Beechuk explains. The investor, who founded tech start-ups himself before serving as Senior Vice President for Salesforce Service Cloud, believes particularly strongly in natural language processing and computer vision as some of the most important AI domains. View More