Three Ways AI Improves VC Decision Making

Oct 27


Nicole Davis

The venture capital industry has been going through many changes lately, ranging from how meetings take place to rising valuations, increased deal sizes, and rapid due diligence cycles. All this transformation has led to many positive performances, aided in part by technology. According to data from Pitchbook-NVCA Venture Monitor (presented by NVCA at a recent webinar), 2020 was a record year for the U.S. venture capital industry with $164 billion invested over the entire year. 2021 is on track to nearly double the record amount of investment seen last year, with $150 billion in investment raised in the first half of the year.

The upward trend of investment started long before the pandemic, however. The total value of VC funds raised grew from $3.5 billion in 2009 to $14.8 billion in 2019, illustrating the growth in the number of funds, and an increase in fund size, according to a recent article by Brink News. This growth in volume translates to VCs needing to invest more capital into their deal flow, while also taking measures to ensure sound investments.

What the current explosive growth means for VCs

The uptick in the number of deals and the rising stakes as the volume of investment dollars continues to increase means the pressure is on VCs managing the due diligence process for deals. This is no small undertaking considering that investors may spend on average 118 hours on screening and due diligence per startup according to a 2021 study by Harvard researcher Pal Gompers. 

The intensity of today’s market, however, has created pressure to act quickly and reduced the cycle time for due diligence in many deals, shrinking it from days and weeks to hours and days thanks to the often crowded VC market and the fear of missing out on the next big opportunity. Currently, seed deals can be done in hours, and Series As within days. 

VCs can drive efficiencies and scale operations with AI

The expectation of immediacy in today’s venture market points to the important role that technology can play in expediting and improving processes, particularly technology that leverages artificial intelligence (AI). AI has been a beacon of hope that offers the prospect of exponentially amplifying scale and efficiency for busy venture professionals trying to keep up with the ever-increasing demand. There are very real benefits for VCs to be excited about, from automating screening to expedite diligence, to algorithms that can augment investment decisions for higher returns thanks to better insights. AI and machine learning (a subset of AI that allows machines to learn from data sans explicit programming), provide the promise of making better decisions, and faster. 

Customized Diligence Models

Many VC firms that have long invested in AI tech startups and are now finding themselves benefiting from the implementation of AI to benefit their investment operations. Some firms have devoted resources and teams to creating their own algorithms to inform their investment models. Veronica Wu, the founding partner of Hone Capital, the U.S. arm of one of the biggest VC firms in China, created a machine learning model to support their investment decisions. EQT Ventures, a Stockholm-based firm, has been leveraging its homegrown AI platform to score investment prospects on a scale of one to 340, as one important factor in making investment decisions. San Francisco-based venture capital firm Signalfire is using a proprietary platform, Beacon, to track the performance of more than six million companies to weed out optimal deals ahead of other VC firms. As AI technology advances, firms will use increasingly sophisticated models that can better determine the viability, strategy, and potential outcome of an investment in a short amount of time.  

Thesis-based Sourcing

Meanwhile, technology startups focused on solving VC challenges with AI-powered software and solutions are also emerging to empower the VC community and facilitate positive changes in decision-making at the top of the funnel and in end performance. For example, while VCs have traditionally relied primarily on their networks to source deals, there are now software tools like, which provides deal flow data personalized to reflect VCs investment thesis--helping them discover relevant new startups before anyone else. 

Another example of how VCs leverage AI technology to make them more productive is a tool called, VenturePole, which sources opportunities that can benefit firms in other ways, such as sourcing with diversity initiatives augmenting industry and stage-specific theses. Despite the influx of cash in today’s market, the capital is not distributed evenly with the percentage of funding that went to female-founded startups in 2020 has dropped to 2.3% from a high of 2.8% in 2019, according to Crunchbase. This represents a tiny fraction of the total $164 billion of 2020 venture capital dollars. Couple that decline with the fact that startups with a female founder are 63% more profitable than startups founded by all-male founders and you have a serious discrepancy in the market. Tools that source with consideration for diversity as central to an investment thesis can help VCs measurably improve performance. 

Private Market Data Aggregation

While headlines in the venture world highlight the amount of capital raised and the related company valuation, the ongoing conversations between founders and investors are more robust. At Aumni, we offer a glimpse into our VC customer’s historical data and trends, along with an unprecedented aggregate view of anonymized private capital market data to show trends across fund dynamics, ownership targets, normal dilution, and much more. We do this by combining the best of A.I. and human expertise to extract and analyze critical deal data buried in dense legal agreements. Access to accurate data benchmarking provided via Aumni’s platform allows a more fruitful negotiation and aligns the interests between lead investors, other investors in the companies, and the founders. This is important to note as round structure and dynamics of venture financings are evolving, bolstered by the proliferation of emerging managers with capital to deploy at the venture stage.  

One additional tool that Aumni’s platform offers VCs is the ability to keep tabs on their co-investor network. When planning the investment strategy for future deals, having a thorough understanding and quick access to their firm’s history with co-investors can be very valuable, especially with regard to the speed of investment syndication. 

At Aumni, we also firmly believe that AI is the future and an invaluable way for investors to ramp up their productivity. This is why AI is such an important part of how we address VC needs. Aumni’s AI-led software makes it possible for valuable data points on contracts and term sheets to be available to investors in a few clicks. We use a hybrid AI and manual review process to reduce errors (because AI and humans tend to make mistakes that are non-overlapping) as well as regularly refine our data models and validation processes as we see new edge cases, shares Aumni CTO, Rob Wise.

The world looks much different today than it did a decade ago, or even a year ago. Thanks to the innovations of technology companies, the investment community is being armed with the data necessary to compete at the rapid pace of today’s investment climate. VCs can leverage AI-powered tools and technologies as ways to add meaningful value, not as a replacement to previous methods, but rather as an ally. This trend of trusting in technology and looking for data to help aid their process is likely to continue according to Gartner. In fact, in just a few years, by 2025, more than 75% of venture capital and early-stage investor executive reviews will be informed using AI and data analytics. The possibilities that this data can create for investors and companies alike are endless. That is a very bright future indeed.