What investors look for in AI and ML startups

Getting to yes – What investors look for in AI and ML startups

What investors look for in AI and ML startups

Everyone knows that early-stage investors want to back great founders and ideas, but it is difficult to anticipate precisely what will resonate with VC firms. A panel of tech investors gathered at Big Data and AI Toronto 2020 to talk through their process of evaluating AI and ML startups.

Three tech investors based in Canada and the United States came together at Big Data and AI Toronto 2020 to talk about their process of evaluating AI and ML startups:

AI and ML startups panelists

One thing that was made clear early in the conversation is that artificial intelligence can be helpful but is not enough in itself. According to Ivy, “what we’ve seen time and again is customers that will try out a solution if there is some AI. It helps you get a foot in the door, but at the end of the day, they never buy AI for AI’s sake. They buy solutions to their problems.”

They all agreed that when evaluating companies that are in the machine learning space, especially at the early stage, the “team is super important to investors”. When taking a deeper look at the team members, investors pay close attention to both the technical and domain expertise. Those are two of the most important factors when deciding if they might be interested in investing or not.

“Another thing I spend a lot of time looking at when we’re evaluating early-stage machine learning startups, especially in the applied machine learning space, is the data set,” replied John. “As much as we care about the quality of your technical team and it’s incredibly important to us, it’s also important to us, especially in machine learning companies, to understand your data strategy”.

Technical and domain expertise

According to Jamie, “on the technical expertise front, obviously you need to have the credentials and the experience”. Regarding the domain expertise, he continued “, it can be played by the same person, but really what we’re looking for there is someone that understands the non-obvious problems that show potential for really meaningful solutions”.

It was also mentioned that there is no miracle recipe into how to find the exact right person for your team. Some may prefer to go with an ex C-suite or CEO of a major incumbent in their space while others may decide to bring “a domain expert who represents the operation point of view; somebody who knows the day-to-day work very well and what the end-user pain point could be”. Both have their pros and cons so technical data scientists who are thinking about building AI and ML startups will have to think about what they want that domain expert to bring to the table as a full-time member of the team.

It is also important to realize that this is not going to be a walk in the park; intellectual curiosity, hustle, and putting oneself in the industry will be crucial. Thankfully, there are systems in place to help founders. “The same way businesspeople have set up all these different platforms to go meet technical co-founders, there’s the other side of the coin […] Businesspeople who are getting into the entrepreneurial world are often really interested to meet you if you have technical skill set”.

Common pitfalls for technical founders

Finding the right members for their team is not the only challenge that AI and ML startup founders will have to face. The experts on the panel discussed other common pitfalls that they have seen time and again over the years.

  1. Resting on its laurels

It is not because you have leveraged artificial intelligence to solve a problem that you are done. Building the solution will give you a head start on the competition, but it is only the beginning of the journey. The world is not set in stone so your product and the way you are thinking about how you are supporting your customers need to evolve with it.

  1. Negative externalities

Bias has become a hot topic in the tech space and cannot be ignored anymore. Even though AI and ML startups can have a positive impact, there have been countless examples of unintended consequences of technology. “The people with the technical capacities to build these products need to think about how these are affecting the people that are using their products because whatever short-term gain you produce by getting distribution, there’s potential for enormous net utility loss.”

  1. Not finding the right balance

As revenue and customer demand start to increase, companies are often tempted to mainly focus on the service delivery operations, increase the size of their team, and continue servicing that revenue sometimes at the expense of further investing in the technology. To be a breakout business, you will have to be a tech forward, tech first business so you will need to figure out what the right balance for your specific company is in terms of scaling up the humans in the loop versus delaying revenue a little bit. This will allow you to further invest in the technology to enable future scale.


It is vital for founders of AI and ML startups to focus on how they want to build their team to ensure that the technical and domain expertise are perfectly balanced. This will allow them to tackle most problems coming their way. Furthermore, the composition of the team is one of the most important factors for investors when first looking at a company. If they feel confident about the team, your chances of landing a deal will drastically improve.

Once they have found the right pieces for their team, founders must also avoid some of the most common pitfalls, which could be detrimental to the business. Too often, investors encounter founders that are making mistakes that they have seen time and again. Being aware of these pitfalls and having a solid plan in place will go a long way for your company.

At the end of the day, it all comes down to who you are trying to sell to, who the decision maker is, who is going to use the product, and does that population of end-users add up to enable you to build a billion-dollar business.Watch talk
Interested in learning more? Join us virtually at Big Data and AI Toronto on October 13-14 where we there will be several sessions about investing in AI and ML startups.

Big Data and AI Toronto - Register now

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