The story of how a team of brilliant minds convinced us they have the AI skills needed to allow large companies to make a leap forward and overcome the challenges of deploying and scaling AI. As some of you may have read Thursday, Grove Ventures co-led a $15M Round A in BeyondMinds, alongside private investor Marius Nacht. We are thrilled to partner with Rotem, Roey and BeyondMinds’ brilliant team, who are developing a universally applicable and easily adaptable AI platform. In recent years, we have been closely following Artificial Intelligence (AI) developments. When we met BeyondMinds, we were convinced that their ideas will shape the future of AI adoption for large companies. This has led us to write one of our largest ‘first checks’ to date. In a world awash with buzzwords about Artificial Intelligence, finally, we felt that we’ve met every VC’s dream team: founders with vision, a deep understanding and know-how, and a great idea that actually can solve a growing pain of a lot of large companies. As I will show later on in this article, the vast majority of the market is struggling with implementing, deploying and maintaining AI solutions in a cost-effective manner. BeyondsMinds’ unique way of thinking about the AI space allowed them to build a company like no other. The process of overcoming pre-conceived skepticism wasn’t easy (luckily, I had become acquainted with the company a few months prior to them seeking funding). During this demanding process, I was convinced to write Grove Ventures’ largest first check to date. Hopefully, by the end of this article, I will be able to share my reasoning.
For Some, Scaling AI Might Be Harder than ExpectedThe pace of enterprise AI adoption over the past few years is rapid. IDC’s Worldwide Artificial Intelligence Systems Spending Guide from 2019 stated that Enterprise AI Software is worth $18 billion in 2020 and will be worth $44 billion in 2024, a 24% CAGR. According to Grand View Research, the Artificial Intelligence market size will be worth $733.7 Billion By 2027. Though the numbers look impressive in terms of growth, most companies have not yet succeeded in their AI implementation strategies. It seems that AI is the future of software, and that remarkable progress has been made in the field with problems that can and are already being solved based on data. All tech giants, be they Google, Facebook or Microsoft, invest heavily in their AI capabilities, build AI research labs, and hire the best academic talents. VC money is also pouring towards startups: Venture funding in AI companies had reached a mind-blowing $61 billion from 2010 through the first quarter of 2020. Enterprises, on their end, understand that they can harness advanced AI capabilities to their benefit. Failure to scale AI means not being able to get a competitive edge, which puts their businesses at risk. However, many are still struggling to move beyond the proof of concept stage. Only one out of eight AI solutions makes it to production. A portion of the solutions that pass the deployment hurdle successfully, still result in negative ROI due to high maintenance costs, or issues such as data drift, the need to meet regulation and provide solutions with explainability, confidence, and bias mitigation, as well as continuous monitoring to stabilize AI systems to ensure increased value after deployment. According to a study by MIT Sloan in collaboration with BCG, only 1 out of 10 projects achieve profitability with AI. These all cause enterprises to cancel projects, sometimes even deep into the process. Clever enterprises understand that a successful AI project relies on three things: (1) Empowering the right champion; (2) Designing the “correct” AI system that can meet production requirements; and (3) The right implementation within existing organization workflows. Only a combination of all three, will mean that their AI project will be deemed successful. In the past few years, we have seen lots of reports from Gartner to McKinsey trying the analyze this slow adoption and systematic failure to achieve profitability with AI. While these reports focus on the management aspects of barriers, BeyondMinds is focusing on the technology-production barriers that hold the adoption back. While most (~70%) of companies are piloting with AI and have broken the management/organizational challenges, only 10% have broken the technical/production barriers. BeyondMinds has developed a unique holistic proposition for companies to accelerate AI adoption and break these challenging barriers.
AI Companies: The Horizontal Model vs the Vertical ModelAI startups are on a race to build a ‘magic machine’ that can cater to enterprises and help them use the power of AI for their specific needs at scale. Two types of startups emerged:
- Horizontal AI startups, who build data science platforms and aim to solve AI infrastructure challenges that most sectors will face as they deploy and scale AI solutions. Many great companies as Databricks, DataRobot, C3, H20.ai (and Grove Ventures portfolio company Deep-Checks) are defining this domain.
- Vertically integrated AI startups, on their part, aim to bring AI solutions and solve a specific need of a specific market. They become subject-matter experts, understand the types of data and workflows, understand the organizations and the players in the space in which they operate in as well as their needs and prioritizations, and lastly, they understand how to best integrate with unique systems and create an end-to-end solution for this business vector. Grove Ventures’ portfolio company Navina, for instance, is doing exactly that: it replaces chaotic patients’ data for primary care physicians with intuitive Patient Portraits, based on multi-sourced data and complex AI capabilities that analyze and organize the most important medical information, then serves it to the doctor in an intuitive way that saves time and improves the quality of treatment.