Venture capital firms are using machine learning to make their investment decisions. Here’s how to use AI for better business insights and better access to funds.
Any current discussion of artificial intelligence in the workforce centers on how it will impact — or outright replace — certain departments or even employees.
But that’s not the most pragmatic conversation for venture capitalists. considering AI’s utility in identifying the most interesting startup investments. InReach Ventures co-founder Roberto Bonanzinga, for example, is investing $7 million on software that utilizes machine learning to find worthwhile European startups to pour capital into.
Which raises the question: Why isn’t every venture capital firm already doing this? Well-established metrics are being used to assess startup potential, and one glance at the market unveils a trove of data points being used by AI algorithms to establish correlations and patterns. These historical points are valuable in assessing how an early-stage startup will perform, while their disjointed and scattered natures provide the perfect environment for AI to thrive in.
AI is a powerful tool that can filter through all the noise and present VCs with potential candidates for investment. And that will make it easier for entrepreneurs to find the best way to appeal to VCs and attain the optimal level of funding.
AI as an investment aid
An inherent risk comes with investing in first-time entrepreneurs, who succeed just 18 percent of the time, according to a Social Science Research Network study. Creative novice business owners are unproven, which can give VCs pause when it comes time to invest.
An AI framework arms VCs with the tools and information to use reasoning, knowledge, planning, communication and perception to boil startup viability down to metrics that can complement gut instinct. AI can internalize data — much like an automated financial adviser — to quickly summate findings and attach a success probability to a company on the basis of previous industry experiences, churn, revenue growth and market size.
By better clarifying which data best translates to successful startups, VCs can educate current and future entrepreneurs. Business owners can tweak their pitches and modify their companies’ profiles to better align with what AI deems successful startup metrics, hopefully resulting in more readily available capital. Here’s where they can start.
1. Mind the market.
Every entrepreneur dreams of running a company that’s seen as a market leader. However, raising the money to make that dream a reality is a difficult feat, especially without the right data to act upon. Attracting funds is even harder for less traditional founders who are competing with well-connected rivals for funding.
Alice, an AI platform tailored toward entrepreneurial women, whittles the crowded startup scene down for founders by using data to spotlight minority-fronted businesses worth funding. With just 2 percent of U.S. women-owned businesses grossing more than $1 million in revenue, Alice measures metrics in any given industry and uses concrete data to provide personalized recommendations using concrete data, giving business owners the chance to assess their companies against a saturated field.
Most founders hope to build a company that can stand out from its most successful competitors while exhibiting similar characteristics to them. Entrepreneurs should use AI platforms like Alice to look at key metrics to see how their startups stack up to competitors who have received funding or are on the cusp. From there, use this information to mold the kind of company that inspires faith — and funding — from VCs.
2. Track investor trends.
Entrepreneurs historically educated themselves about investors by preparing their applications and pitching to investors, only to find out after meeting with them that they’d been rejected in favor of later-stage companies or ones in different verticals. Many entrepreneurs give up before they’ve found success because of this, but AI is changing that.
Berlin-based VC firm Fly Ventures targets seed and pre-Series A startups and just closed its first fund at $41 million. The plan is for Fly Ventures to target European startups in the seed round and use machine learning to generate deal flow. The company’s AI algorithm is reportedly discovering 1,000 new companies a week and can even find burgeoning tech startups before they’ve begun fundraising.
This type of technology directs entrepreneurs toward meeting the right investors at the right times. After surveying the market, use the AI-provided information to ensure your company’s metrics line up with what investors seek in a viable startup partner. Being cognizant of what traits appeal to investors can make the search for funding more fruitful and a little less frustrating.
3. Never stop improving.
The great thing about AI is that — like a business — it’s never done evolving. Machine learning is constantly receiving and analyzing information, so entrepreneurs should use these nonstop updates to constantly tweak their businesses and pitches for investors.
Hone Capital is already modeling this behavior. The tech-focused investment firm partnered with AngelList to create a database of more than 30,000 deals from the past decade to feed to a machine-learning model. It explored 400 characteristics and narrowed them down to 20 that are most indicative of future success.
Hone provides entrepreneurs a living database of companies to consult for tips on how to improve and differentiate themselves. Founders can take this information and simplify the investment process by applying concrete success metrics to their own businesses. Use these AI capabilities to continually improve your company’s profile and gain that first — or future — round of funding.
By harnessing the power of AI, entrepreneurs at every stage will have access to better data and better insights. The technology isn’t just disrupting business operations — it’s changing the entire game from top to bottom. And if you’re already working with it, you’ll know you’re doing it right when a VC calls you out the blue because you fit the entrepreneurial profile that his AI was looking for.