Securing venture capital funding is a tricky terrain to travel. It’s hard enough for founders to accrue the capital needed to continue scaling, but it’s even more difficult for entrepreneurs from underrepresented demographics.
In a recent study jointly published by Babson and Wellesley colleges, it was found that just 3 percent, or $1.5 billion, of the $50.8 billion in VC funding handed out between 2011 and ‘13 was raised by women. And companies with all-male executive boards were four times likelier to garner funding than boards that included at least one woman.
Optics such as gender and race can at times dissuade VCs from supplying worthy companies with the funding they need. But what if VCs awarded funds by using a blind approach to assessing a company’s potential trajectory? Artificial intelligence informed by concrete data could lower that curtain, crafting a future in which machine learning helps VC funding lean less on appearances and more on a company’s potential merit.
Scant funding for minority and women-led startups is an issue that’s been building for some time. Less than 1 percent of VC funds raised go to minority-run business, while 2 percent goes to companies fronted by women, despite the fact that 38 percent of U.S. companies have women in charge.
Trends like that, no doubt, prompted Dell entrepreneur-in-residence Elizabeth Gore to create Alice, an AI platform that uses a litany of data points in order to open female, minority, and LGBT founders up to greater VC funding opportunities. Biases also exist in favor of younger entrepreneurs or those from certain universities. By using AI, investors can leave behind biases that they may not even be aware of and focus solely on a company’s merits as an opportunity for returns.
In the PricewaterhouseCoopers Digital IQ Survey of 2017, 52 percent of professionals in the industry reported making “substantial investments” in AI, and two-thirds expect to be doing the same three years from now. Perhaps even more telling is that 72 percent of business leaders and decision makers picked AI as the most compelling future business advantage.
The metrics and data points that define successful startups are becoming increasingly visible and increasingly repeatable, giving investors a recently accessible degree. AI lets entrepreneurs align their metrics with a successful blueprint. For VC firms, it’s a chance to focus less on closing deals and more partnering more diverse, high-quality startups.
Venture capital is an industry that revolves around people and relationships, but it doesn’t come without its own risks. VCs may relate better to individuals who resemble themselves at different parts of their career, and in a male-dominated business, this might be one reason for the existence of a systematic bias toward men.
June Manley saw that bias firsthand when she pitched her software enterprise company in 2015. She participated in more than 80 VC meetings, repeatedly witnessing funders disregard her product, condescend to her about her qualifications, or even suggest her husband take the lead when pitching to VCs. She even witnessed similar companies fronted by men get the nod as she went to meeting after meeting looking for someone to take a chance on her.
From that frustration sprung Female Founders Faster Forward, a nonprofit organization that uses a tech-based model designed to minimize that type of bias. Using a Startup Investment Model Index, a kind of startup FICO score based on attributes from some 750 VC-funded businesses, the software will be an evolving entity that female founders can use as a complementary resource to shield their funding quest from bias.
This fluid, AI-inspired approach will use metrics such as startup risk and maturity to compile a score that founders can attach to their startups and use in the funding process. Manley hopes the tech will help raise female funding from 3 percent to 20 percent by 2020.
Data and figures can cut through whatever potential biases a VC might have when it comes to funding companies. Machine learning can sift through metrics and stray from any biases a VC might have and drill down to the numbers that will ultimately point to a startup’s chances for success.
AI can establish a different kind of relationship, one that hinges more on what the data says about a company’s potential and less on any personal connection or potential biases. For any AI product or startup to be successful, there needs to be data. Feeding empirical information into an AI engine allows engineers to confirm their theories and demonstrate its impact. Without data, there is nothing to learn from, no matter how effective the algorithm.
AI never stops learning, which is why it’s an ideal match for VC firms. Data and numbers are unencumbered by personal bias, free to assess bodies on the data in front of them. As that information continues to pour in and change from minute to minute, a VC can take startups at face value, making decisions on the potential a company brings to the table instead of who is sitting across it.
