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Enterprise AI: Why It Matters & What Will Drive Adoption

enterprise aiArtificial intelligence (AI) is not a novel concept, instead, gaining prominent attention over several decades across the likes of academia, literature, and television. Famously, in his book 1984 (published in 1949), George Orwell vividly predicted a not-so-distant future that featured advanced machine learning phenomena such as telescreens, programmatic surveillance, and conversational user-interfaces. Although the trajectory to bring mainstream these advancements in AI took longer vs. Orwell’s original predictions, recent years have illustrated an AI market in the United States that has grown from under $1.5B in annual funding (2013) to nearly $5.0B in annual funding (2017) spanning thousands of venture-backed deals along the way. And with substantial improvements in data generation, storage, computing, and user-interface design, the barriers to entry for AI startups have never been lower, with no growth slowdown in sight. At Bowery Capital, we believe that a key driver of this pronounced growth is via “Enterprise AI” which we define as AI technology geared towards industries and their leading organizations covering the needs of real-time data procurement, analysis, decision-making, and governance. From AI infrastructure on the edge – an arms race for big tech and startups alike (swim.ai, Thinci, Syntiant, Mythic, Falkonry, Arundo Analytics), to unstructured data recognition in imaging, speech, and free form text which makes over 80% of available data (Butterfly Network, Pony.ai, Momenta, AEYE, Viz.ai, Textio), to vertical AI software (BioCatch, Insitro, Cleo, Atrium LTS, Benson Hill Biosystems) we observe a number of companies looking to shape this trajectory. Our view is that breakthroughs in enterprise AI will continue to provide sustainable value for Fortune 1000 companies and have substantiated this belief through our investments in enterprise AI companies like msg.ai (customer service), Fero Labs (manufacturing), and Electric.AI (IT support). We spend a lot of time speaking with our AI portfolio companies and their buyers, and have come up with a short-list of the key characteristics of enterprise AI platforms that make them highly valuable and adoptable to Fortune 1000 customers:

1. Business Unit Automation. Any cost center (customer service, support, HR, legal, accounting) that presents a variety of tasks that are time-intensive and automatable is a prime target for enterprise AI innovation. Companies like Atrium LTS, Electric.ai, and msg.ai, are examples of AI platforms that will help evolve billions of dollars in variable human capital expense into low-cost automated platforms that perform these tasks behind-the-scenes to let employees focus on more value-add functions.

2. Data Access & Data Quality. The majority of data that is analyzed through automated platforms is structured and trained, however, the majority of data that gets generated is unstructured (images, speech, free form text). Thus it is important to not only access the highest quality of structured and unstructured data that exist, but also ensure that there are solutions in place to effectively map the various types of structured and unstructured data inputs and come up with meaningful takeaways for the business.

3. Integration Potential. Many enterprise clients are now the beneficiaries of various cloud-based technologies, streamlining their operations across several internal and external functions. Many of these platforms harbor highly useful client specific information and come with flexible API configurations to enable an integrated ecosystem. To have the best integration capabilities with existing solutions and help drive a user-experience that is both high in value and customizable to the client, we favor AI platforms that are built to easily work with other software and AI platforms vs. operating in isolation.

4. Executive Sponsorship – All of these other points speak to the ROI of enterprise AI platform but without executive sponsorship, it will be difficult to organically drive the adoption of these platforms which is the key driver of long-term value. We focus on organizations that have some combination of (1) a leadership team and board of directors that is well versed with technological innovation from prior experiences and what they communicate, (2) financial and operating KPI’s (such as R&D as a % of sales) that illustrate a company’s commitment to be on the leading-edge of its peer group, and (3) communications in earnings calls, investor presentations, and other publications that clearly position emerging innovations in enterprise technology as a focal point of long-term growth and differentiation.

We believe that the list above is a great starting point for founders who are looking to sell AI platforms to enterprise customers. Beyond the ROI potential that these platforms offer, we believe that the best way to differentiate and position for long-term success is through the additive impact these platforms would have on the existing technology ecosystem as well as the internal sponsorship from key personnel within the business – thereby determining the “winners” and “losers.”

If you liked “Enterprise AI: Why It Matters & What Will Drive Its Adoption” and want to read more content from the Bowery Capital Team, check out other relevant posts from the Bowery Capital Blog. Special thanks to Sri Bhamidipati for his help with this post.

Michael Brown
Michael Brown
Michael is a Founder & Managing Partner at Bowery Capital based in New York. Prior to Bowery Capital, Brown was a Co-Founder and General Partner at AOL Ventures. Before AOL Ventures, Brown worked for the investment arm of Richard Branson’s Virgin Group. He began his career at Morgan Stanley as an equity research analyst. Outside of his professional life, Brown serves on the Board of Directors of the National Forest Foundation and the Columbia College Alumni Association. He holds a B.A. from Columbia University.