What Every AI Startup Needs to Know to Attract the Right Investors

Illustration: Getty Images.

Move these five things to the top of your pitch deck when seeking investors.

It’s a good time to be an early-stage investor, and it’s an even better time to be an AI-first startup looking for funding.

Investments in AI companies topped $77 billion last year globally, more than doubling from $36 billion in 2020. However, with more and more AI-first businesses launching, it can be tough to stand out and find a partner ready to take your business to the next level. As someone who has both founded an AI company and now invests in early-stage AI-first startups, I know how critical it is to have a plan for connecting with the right investors.

Five things I consider before investing:

When evaluating potential investment opportunities, I start with the basics: How big is the company’s total addressable market, how strong is the founding team and how transformative is their vision (and excited I am about that vision!). Once I feel confident in these areas as an AI-first investor, it’s time to dig into all aspects of AI.

Here are five key areas I look at closely–and elements any AI-first company should pitch to potential investors:

1. Spotlight core AI intellectual property.

These days, almost all startups claim to be AI companies. It’s my job to find out if they really are, and so I evaluate:

  • If AI / Machine learning techniques is/are core to the business, its products and/or its go-to-market strategy.
  • If there is a company that creates unique intellectual property (IP)–products, technology and or services–built using machine learning, deep learning, or computer vision, speech analytics etc.

I also believe that It’s not enough to use off-the-shelf technologies. The team has to have core domain expertise and be able to build these complex models internally. AI features and language should be embedded into all aspects of the company, and their presence or absence indicates whether AI solves a central business problem and serves as a key differentiator. Ultimately, I assess if the company authentically leverages AI solutions and whether AI is even really needed to solve the problem. If AI is not really needed, then it can complete operations without machine learning. If it can, then it’s not the right fit for my investment.

2. Highlight AI expertise on your team.

If a startup has AI IP, my next step is to determine if the company has the right technical team to back it. I research whether the company’s team has the capabilities and machine learning background (both education and real-world experience) to build and maintain the technology-proposed IP. Roles like data scientist, machine learning engineer and computer vision expert should be central to the team structure–if these skills aren’t available in-house, it’s challenging to run a successful AI-first company. I also look at the org chart–is there AI representation on the core founding or executive team. This is often very telling and indicates how serious the company is about being AI-first.

Team considerations go both ways. In highlighting AI usage and staffing during investment conversations, you ensure potential investors are up to the task of supporting your business long term. AI-first companies want investors who understand AI and offer domain expertise.

3. Position data as a competitive advantage.

Access to unique data is a must for any AI company I consider investing in. I want to know if the startup has proprietary data that doesn’t already exist in the world, and if that data is used to generate unique insights no other company provides.

When speaking with investors, it’s tempting to shy away from the detailed inner workings of your data. But we want to know! It’s important to highlight data metrics and practices in your pitch decks, such as how you acquire data and obtain user consent, how you annotate/index data, and how your data warehousing and infrastructure are designed – and demonstrate that everything can scale. For AI-first companies, data is often your most competitive advantage. Clean, reliable and scalable data ensures faster time to market for products, smarter analytics and more accurate reporting.

4. Value and promote diversity.

Beyond AI techniques, I explore company diversity and assess whether DEI principles are core to business operations. I’ve learned firsthand how diversity fosters innovation. And diversity isn’t just looking at age, gender and ethnicity, but also diversity of experiences and backgrounds. When I’m evaluating a startup, it’s a red flag if the team lacks diversity and I call it out. If I do end up investing in a team that I believe could be more diverse, I will use my investor influence to ensure the company corrects this dynamic as it grows.

Especially at AI-first companies, a diverse team decreases the likelihood that the technology developed will perpetuate racial and other inequities. We’ve all seen articles about how AI is racist or sexist, and maybe you’ve unfortunately experienced this reality yourself. If an AI company values and promotes diversity in its ranks, my hope is there’s less risk for these problems thanks to more diverse perspectives powering decision-making.

5. Meet economies of scale and scope.

Finally, I want to make sure a potential investment has a long shelf life, both in terms of scale and scope. When we think of scale, we examine how costs diminish over time as a business improves its ability to complete more tasks in better ways. But when we think of scope, we’re exploring how a company can take what it’s learning and apply it elsewhere – this is a unique property of AI-driven businesses.

With machine learning, companies gather super interesting data about specific use cases and customers. Over time, this data helps expand the scope of the business and potentially surfaces new products or services beyond initial focus areas. For example, if a company incorporates AI to help users find rental properties, data collected could offer additional insights into customers’ larger financial situations. Perhaps information gathered through the rental process about credit scores can be leveraged to offer customers loan refinancing options or additional financial consulting services.

AI and machine learning are huge spaces that apply to almost every industry and it’s exciting to see more companies taking flight. As a fan of AI and the businesses that bring this technology to life, I’m always in your corner.

Maybe I’m even your next investor.