Pitching an idea to seasoned investors can be a nerve-wracking experience. This is particularly true for first-time entrepreneurs in highly specialized fields like artificial intelligence (AI).
Before reaching an agreement with a company, a venture capitalist (VC) will need to be certain that they are committing their time and resources to the right team. So, as investors vet companies to be part of their portfolio, how can an AI startup ensure that its business stands out from the competition?
How Do Investors Evaluate AI Startups for Investment?
The competition within the deep tech space is intense and investors are acutely aware of the falling constraints that firms will face as they look to enter the AI market. The likes of machine learning, publicly accessible libraries, pre-trained models, and application programming interfaces (APIs) have all served to lower the barriers to entry for entrepreneurs and startups. Companies launching a product using these toolsets alone will have a myriad of competitors in no time, which naturally poses a risk to investors.
To mitigate this, investors will be on the lookout for AI startups that are innovating both at the science and application level. VCs will be keen to invest in solutions that are genuinely pushing the frontiers of the technology and will therefore look for companies that are inventing novel AI for their purposes — and building the underlying infrastructure as they do so.
Most importantly, investors are seeking truly groundbreaking ideas, instead of just regurgitations of third-party APIs — which is often the case. It is very easy to spin a story, create an alluring narrative, and sell a dream with no substance. Because of this, investors will need to determine whether the story behind a business is an authentic one and whether the product is genuinely innovative. True deep tech looks at what to explore next. It knows what is possible at the boundaries and the problems to solve to advance to the next level.
Further, startups will need to be able to demonstrate how they have installed AI, and how they intend to leverage it to bring value to customers. After all, technologies will only be attractive to investors if they are commercially viable. Products with promising prospects for commercialization will be the ones that will stand out from the rest.
A good idea alone will not be enough and the architecture behind the solution will be a major sticking point. VCs need to be confident that this can be scaled in line with the long-term vision of the business. Will the architecture be able to handle increasing inputs of data? Can it be scaled to enterprise-level?
What Experience Is Important?
A critical part of investing in AI startups is understanding the technology. However, many investors will not have advanced technical knowledge in this field.
This can make the screening process difficult, particularly if companies attempt to pass off conventional computer programming or automation as innovation in AI. Indeed, a report released by MMC Ventures in 2019 raised questions about how “artificial intelligence” has become a blanket phrase for startups looking to attract investment. Of the 2,830 European AI startups surveyed, in only 40 percent of cases did the technology actually constitute a key part of the product offering.
Investing in early stage AI startups that have little to show beyond a compelling idea can be a risky business. Some investors will therefore build out their in-house technical team to ensure that they have the capacity to screen companies before there is a product and market traction. Others will work with partners that have in-house data scientists and a solid understanding of deep tech to better select and support their investees.
Deep tech entrepreneurs should also be aware that, for investors that do not specialize in AI alone, one important aspect of the evaluation process is assessing the founder’s background and the company’s wider talent pool. VCs will be on the lookout for teams with strong technical backgrounds and relevant experience in the niche market that they are targeting. This necessarily involves verifying the qualifications and previous field experience of the engineering team behind a product.
What Traits Do Investors Look for?
Beyond this, remember that investors are committing themselves not only to an idea, but to the people behind the vision. This isn’t an exact science and VCs will look to support individuals based on instinct, rather than by following a predetermined checklist. Generally, they will be looking for a number of traits and characteristics, including charisma and general business sense, as well as sound technical knowledge.
That said, it is important to remember that many of the brightest minds in the AI space did not have any relevant business experience when they started. So, while having some business acumen is a bonus, it is not the be-all and end-all of a promising AI startup. Depending on the model of investment, investors may be able to bring their own knowledge and network into the playing field to help founders navigate common pitfalls.
An awareness of their own strengths and weaknesses then, as well as a readiness to onboard the right talent to plug existing skills gaps, is of far more importance. Likewise, seeking out and responding positively to critical feedback from peers, customers, and experts is a sought-after attribute. It illustrates a founder’s strength of character and ambition to succeed.
And finally, a positive, can-do attitude will never go amiss. Building an AI product is challenging, but ensuring it is sustainable and truly serves a market need is even more so. A positive mindset will see a team through the inevitable tough times and inspire inventive ways to overcome obstacles that are thrown in their path.
Ultimately, the viability of an idea is important — but the personality of the founder can truly determine the success of an AI startup.
