3 Qualities Investors Look for in AI Startups
As a startup company in a hyper-specialized, high-demand industry like artificial intelligence (AI), it's not uncommon to feel like a tiny fish in a bottomless pond.
You know you have a great product, and you understand how it can help solve real-world problems. But how can you articulate this to potential investors and build enough confidence—while establishing you and your team's credibility—to get to the next stage of the process?
What are investors looking for in a startup?
As an early-stage venture capital management firm, we've worked with many passionate data-driven founders and firms committed to using AI and Machine Learning (ML) technologies to make the world more efficient.
If you've been following Kevin's blog series on AI and Web3, you already know data products that have the ability to analyze, interrupt, and relay information that's available in a decentralized network could create a bigger impact in AL/ML's future.
That being said, from our experience, we can say with confidence when it comes to the type of qualities investors look for in AI startups, we look for the following traits:
Solution focused
Data products should be centered on solving real-world challenges that exist today. Traditionally, AI has been a domain largely dominated by two recurring personas:
(1) the academic researcher-turned founder; and/or
(2) the futurist
Of these two personas, the former typically wants to fund his technology because he finds it intellectually stimulating or complex. The latter is less pragmatic in her desire to use AI to solve the challenges we experience today. Instead, she’d rather focus on future AI capabilities that are based on novelty vs. practicality.
Neither, however, is focused on end users (i.e., customers) and solving their immediate needs (the here and now).
Using the example of Corrily—a company that specializes in intelligent price automation—we saw a business that offered an effective solution to scaling prices via market demand. They acknowledged that most subscription companies do not utilize analytical data when it comes to setting their own pricing structures.
Let's break this down set by step to help convey how Corrily adopted the type of solution-focused trait AI investors look for:
The real-world problem:
A price structure that was based more on guesswork than actual concrete market data, resulting in lost revenue.
The AI solution:
With the emergence of Corrily's dynamic pricing tool, companies can now experiment with pricing in real-time, boosting conversion rates and generating more revenue.
The Result:
Companies can modify prices with better return on investment (ROI).
In reality, the data was always there, but the challenge was harnessing this information and relaying it in a way that effectively captured market demand. While keeping the end user in mind, Corrily was able to solve a real-world challenge with an immediate, data-driven solution.
Deep understanding of the technology
It is true that many investors may not speak the same data language as you do. Because their technical knowledge may be limited, you may start to question just how much data and resources you'll need to provide—and if, in doing so, it will cause their eyes to gaze over.
When you find yourself in this scenario, the key is to demonstrate your own understanding of how the data product is being used and delivering added value to the end user's experience compared to your competitors:
“Make sure you understand the underlying AI concepts relevant to your business, and understand how those concepts map to creating an unfair/differentiated advantage in your business model," (Kevin Novak).
Investors care about simplicity, not complexity, and they want to know how your product simplifies a particular challenge. Therefore, it's essential that you convey the information they want to hear, without confusing them with too much industry terminology.
On the other side of the spectrum, pitching an idea to an investor well versed in data science can feel equally nerve-wracking. This is actually an advantage at our firm, as our founder Kevin Novak already has a vast amount of experience in this industry. We asked him for his advice on how to approach this second scenario:
“Don't be afraid to pitch me the technology, because I understand it and will ask about it. This allows us to have an open, transparent conversation without all the rah-rah, though I'm fine with that, too. What I'm more interested in is talking to someone with a passion for data and ideas on how to use it to make the world better."
Qualified team behind the startup
Your co-founder(s), as well as the team working beside you, play an important role in whether or not an investor will back your company. Having the right skill set, passion, experience, and work ethnic is just as vital in the final decision as having a valuable product you can bring to market.
If you’re raising your first capital, you and your co-founding team should be able to answer the following questions:
Why are you choosing to start this company together?
What makes you think it will be uniquely successful with each of you working with the other?
Of all the founding teams pursuing this particular solution, why do you feel like this co-founding team will be successful vs. competitors?
What are some weaknesses each of you possess that you think your co-founders can help minimize?
What are some strengths you possess that they can maximize?
Your recruitment practices will also factor into the equation. Therefore, be sure you feel confident answering these next set of inquiries:
Have you identified the correct domain experience you intend to hire for next?
Have you thought about the right sequencing and timing for making your next hires?
What are your recruiting practices?
Have you already hired a data scientist? If not, how will you fill this important management role?
In addition to these questions, here are a few other topics of discussion you'll need to have a firm grasp on, as well:
Potentials risks/rewards
Project growth & revenue
Gross margin
Key performance indicators (KPIs)
Marketing/advertising
Though there are many questions to answer, and different factors to consider, pinning down the basic qualities we highlighted above can help prepare you, and your team, for your next great pitch.
A Final Thought
As Kevin highlighted in his Web3 series, there continues to be a noticeable gap between where AI is and where it should be:
"Consider that the innovative and emerging use cases for AI come from models that are trained in insanely massive datasets and comprise billions and billions of features [...] Under this current infrastructure, the only way to utilize AI at its highest level dramatically shrinks the aperture of who can contribute to its usage and development (Medium).
In truth, there are a larger number of great AI companies than the industry currently recognizes. Though many of these businesses are ignored, we aim to reach them, coach them, and help them kickstart their early-stage funding.
"I like to be open minded, and believe we can find truly great companies, before the rest of the industry figures out their true potential. I look for those who are truly invested in their products and who value the data behind it,"(Kevin Novak).
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