Given the success (and astronomical returns, if you got in early) of tech investing over the past decade, every investor is trying to find the next “unicorn”. In order to understand what the next big tech thing is, we spoke to some of the largest and most active early stage and venture capital firms around the world. For some context, one firm has invested in 2,300 startups since inception and has a network of 5,000 founders.
The premise was simple – the innovative business models, technology and/or services likely to emerge over the next 5 to 10 years will likely be attracting VC dollars today. We further validated our findings from these discussions by looking at deal flow and funding data in the VC/early stage sector. We highlight some of the themes to emerge.
Smart HR (or HR Tech). HR tech is moving beyond simply just automating the workforce hiring and people management (e.g. performance reviews) process. Companies are now utilizing background check firms (led by technology) to better use all available data in the hiring process. Artificial intelligence (AI) is also being trialed to fully automate the hiring process (AI recruiting), to potentially eliminate all human bias. Team management tools are also attracting investments in the VC sector, such as Monday.com which is a team-based project management software. One of the reasons, we believe, the HR Tech is still not garnering enough interest is because business leaders still see the HR function as process based centre rather than a business strategic partner. With technology adding more tricks to the toolkit, we believe this thinking will change.
Digital health. This is an area that will see significant investment over the next decade. One key enabler of this is improved speed (e.g. 5G) and computational ability (processing large sums of data). Healthcare (and related data) is a key pillar of any developed economy. Whilst the sector has access to vast amount of data, being able to compute it in a timely and efficient manner was not previously possible. Key themes in the digital health area:
(1) Artificial intelligence is being used to transform pathology and increase the speed with which patients are diagnosed;
(2) Artificial intelligence is being used to enhance and speed up the drug discovery & development process but using algorithms to analyse disease-related data and forecast treatment outcomes; and
(3) Value based care such as using existing networks of providers and payers to help improve referrals and decision making.
According to data by CB Insights, the most funded companies include We Doctor (providing primary care using technology) valued at $5.5bn, insurance technology company Oscar Health valued at $3.2bn and genomics startup GRAIL valued at $3.2bn.
Insurance Tech. According to data by Willis Towers Watson and CB Insights, investment in insurance technology in 4Q 2019 was at an all-time high of $2bn. Whilst still early days, the insurance sector is looking to digitize its functions:
(1) Pricing and underwriting;
(2) Quotes, bind and issue;
(3) Policy administration and central systems; and
(4) claims and settlement.
One area which is seeing growth in investment (but still a long way to go) is fully automating claims processing.
Machine Learning. Machine learning is just a branch of artificial intelligence. As the name suggest, machine learning is a process of a machine learning a process. Instead of explicitly programming the functionality into the machine, a system is created for the machine to learn on its own. This can be done by providing the machine labelled or unlabeled data, which it learns and begins to perform functions on its own.
Worth highlighting that this technology has been around for decades with algorithmic approaches such as artificial neural networks (ANN), support vector machines and Bayesian networks. However, whilst these have been recognised as possessing significant potential, the technologies never really gained traction largely due to a lack of practical application. However, there was a step change in AI with the discovery of convolution neural networks, which is also referred to as “Deep Learning” as they contain multiple layers of neural networks. Given the change this new research provided to the industry, most researchers and companies looking to commercial solutions nowadays are using convolutional neural networks.
*This article is just a snippet of the full article sent to clients of BanyanTree Investment Group. Find out more about them here.