There is no dearth of investments flowing in for emerging tech sector like analytics, data science and AI, among others. There are lots of new startups coming in areas of AI and analytics and its verticals like speech AI, NLP, computer vision, AI in mental health, AI in fashion, driverless tech, virtual agents and others, which are getting a strong backup from investors. While it is true that investors are open to supporting emerging tech startups, what is it that they look for in these startups before investing huge amounts?
Depending on whether it is venture capital, angel investor or other types of funds, they might have different criteria to evaluate the business, but the underlying factors mostly remain the same. There are some investors who consider execution over great ideas, while there are others who totally go by the personality and passion of founders. It also helps to get attention from venture capitalist through a trusted colleague, lawyer, investment banker or other investors. But when it comes to tech startups there are several other criteria that play a crucial role.
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This article talks about a few such pointers that a tech startup must have to catch investors attention.
1| Is the startup tapping the right market: As the frenzy around the term such as AI and machine learning is going to a whole new level, there are many startups that have come up in the space. To live up to this competitive race, while many startups may claim to have come up with a unique AI solution but taking it to the market may be unrealistic, owing to the fact that there is no potential market for the product. Investors are looking for realistic projections from AI startup. The lack of market fit kills most startup and therefore it is important to understand what customers need.
2| Is it a genuine AI startup: There is an overflow of AI-based solutions in the market. Almost every other startup likes to call itself an AI startup which makes it a difficult task for investors to pick up the relevant startup. It is therefore important to have a solid prototype of your product ready with use cases and the technology ready to be explained to the investors.
3| How is your AI product different from others: With a lot of AI solutions overflowing, there might be overlap with the existing solution. In such a scenario, it is important to explain how your product is different, what are the competitive advantages of your product over others that already exist in the market, and how is it going to cater to the market differently. There is a huge competition today which makes it challenging for them to scale up to the levels higher than others.
4| Domain expertise of the team and team management: This is probably one of the most crucial things that investors are looking in tech startups especially in analytics and AI as it requires specialised expertise to come up with tech solutions. They are looking for a blend of team members who understand the technology, can build AI/analytics product from scratch and tap into the market effortlessly. That is not all, investors also strongly back a startup who has a strong management team who can run the startup with much ease.
5| Is the market already engaging with your products: Demonstrating the numbers and the fact that your product is not just a talk but is already getting engagement from the market, will de-risk an investment opportunity. It can work wonders if you have data that supports your claim. It not only backs the product that you have but shows on greatly on your commitment and initiatives that you can take for the startup.
6| How do you plan to utilise the money: Is it product development, expanding team, advertising or building the right infrastructure, investors are looking to have a fair idea about how you plan to use the funding amount. This is to ascertain that the plan you have in hand is trustworthy and that the founders have a fair deal of idea about the technology that they are dealing with.
7| Value creation of the product: For AI startups value creation comes from the product itself and the data aggregation that can help them in the long run. Aggregating data, especially that is unique and exclusive is valuable. For instance, an agricultural AI startup can aggregate data points such as crop image data, weather data and others that may prove to be valuable in the long run. It suggests that startup is serious about using this data to improve further, bring revisions in product development, and more. The startups that have short term value creation models along with long term ones, looks to be more promising to investors.
8| What is the startup’s focus on investment in R&D: The most important point that investors look for in a tech startup is how much is the money that will be invested in its product development and R&D. For AI and analytics startup it is crucial that a lot of focus goes on developing a quality product that looks genuine and is easy to use. Continued efforts in R&D pays off well for startups and therefore for investors.