Artificial intelligence is now one of the hottest segments among venture capitalists, but simply integrating AI into a product does not automatically qualify a startup for funding. Venture capitalists want startups that target meaningful problems, have established customer demand, and can scale with costs growing at a slower rate. For founders exploring venture capital for AI startups, these factors are important to attract investor interest.
In order for AI founders to be described as venture-backable, it must be shown that they are able to create a sustainable competitive advantage in a fast-evolving environment.
1. A Clear and Valuable Problem
Investors must understand the problem for which startups have come for funding and why that problem is worth solving now. They may not be interested in a highly complex AI offering that has no customers urgently needing it.
This means that business founders must provide answers to the following questions:
• Who is affected by the problem?
• How often does the problem arise?
• What are the costs of suffering from the problem in terms of time and money?
• Why is the existing solution inadequate?
• How does the use of AI provide a more effective solution compared to regular software?
For instance, using AI to increase productivity for businesses is too vague. The better option would be to say, “Our AI solution reduces by 60% the time to review claim requests for insurance companies while providing certification of approvals.”
2. A Large and Expanding Market
Venture capital companies often invest in startups that have the potential to become big businesses. They focus on a niche market, but it does not necessarily need to be aimed at a broad audience. A niche that has a customer base might be interesting if it has the potential to grow.
The founders must determine:
1) Their initial customer segment.
2) The size of the serviceable market.
3) What kind of growth the market is expected to have.
4) The other niches to be penetrated at later stages by the company.
5) How profitable each of their customers can become.
To have a realistic market analysis, one should rely on certain facts like numbers of customers, prices, and buying behavior rather than make claims about entering a multi-billion-dollar industry.
Take a new AI company that is focused on hospitals. At first, the company works in clinical documentation and then might go into revenue-cycle management, patient support, and medical compliance.
3. Evidence of Customer Demand
Traction can be seen as one of the best signs that an AI startup is ready to receive venture capital funding. Investors want proof that a startup’s product has a market for customers who buy the product, use it, and/or renew it.
Here are some points that prove traction:
Paying customers
Growth of revenue
Successful paid pilots
Retention and renewal rates
Frequency of use
Expansion revenue
Letters of intent from reputable clients
Sales cycle time
Clear customer savings or revenue growth
Free customers can be seen as an indicator of interest, but a paid customer should be more convincing. Founders should distinguish between a pilot and a repeatable product-market fit.
4. Defensible AI Technology
Numerous AI solutions rely on identical foundation models and openly obtainable tools. This advances the process, but simultaneously complicates the matter of defensibility.
As a result, investors inquire about the reasons why one company can’t easily replicate the product from another rival. Potential aspects of the competitive advantage might include:
– Access to proprietary/permissioned datasets.
– Access to unique industry-specific knowledge.
– High level of evaluation and monitoring of models.
– Unique procedures or integrations created.
– Expertise in a given technical area.
– High switching costs.
– Network effects.
– Partnership with distributors.
– Possession of compliance and legal kknow-how
To be of significance, a proprietary dataset must be acquired legally, be extremely difficult to duplicate, and directly contribute to product improvement. Saying just that the company uses “better data” is not enough anymore, and it is important for the founders to demonstrate how the data improves the accuracy, the speed, and reliability of the product.
5. Strong Unit Economics
While AI can create great products, it can also lead to bad economics. Along with the model cost, there are expenses incurred by the associated cloud infrastructure, data processing, and human quality checking.
The investors take a look at the following metrics:
Figures of gross margin.
Cost of acquiring a customer.
Value of a customer during his lifetime.
Period for payback.
Income per account.
Cost of inference per task.
Cost of infrastructure as a % of income.
Burn rate and runway.
6. A Credible Go-to-Market Strategy
If a company cannot effectively contact its audience, it will not matter how great its product is. Investors want to know who the customer is, who the user is, and who makes the purchase decisions, as well as how the startup plans to reach similar customers.
The go-to-market strategy may include:
Direct sales.
Alliance with established software providers.
Self-service subscription model.
Connecting with the industry community.
Developer-led distribution.
Using third-party distribution channels.
Use of previous customers’ relationships.
AI startups need to learn about their ideal customer characteristics and explain why this particular market segment will likely buy their product. Having a small target market can be a plus, for example, because it can lead to faster learning and better focus on sales.