How To Get Funding For AI Startups
What are the biggest challenges AI startups have when pitching to investors? Learn how to grab their attention with these recommendations on how to start building your AI company.
After having worked with several VCs and startups, I noticed that successful startups tend to share the same characteristics. Hence, I decided to write an article that could help entrepreneurs understand what can make an AI startup catch investors’ attention. As always, and most particularly, the findings detailed below stem from my experience.
The main challenge for AI startups is to prove to investors the scalability of their business model.
Over the years, I noticed that some consumer products powered by automation were randomly/easily using the term AI in their communication when they actually only relied on data analytics to automate low added-value tasks. In such cases, the technology does not become more intelligent over time. AI projects’ leaders seeking fundings should be prepared to be technically challenged by investors as soon as they mention the term AI.
These companies often throw around the word algorithm linking it to AI. A sole algorithm that drives to certain outcomes does not imply or legitimate the existence of AI.
Most AI startups that I have met had to do a lot of R&D before they were even able to ship a real AI product.
It is frequent to see startups building a V1 of their product with no real AI in it. Instead, they use a combination of traditional algorithms and human-powered task. Of course, it is a good idea to gather data and get real feedback from customers, but this is limited from a business model perspective. However, you will have to develop a “real” AI V2 at some point. This moment when you actually transition from V1 to V2 is really tricky since it might have an impact on your structure and clients.
Indeed, you cannot scale with a “human in the loop” strategy.
Of course, the goal is that the AI accuracy will improve and slowly replace most of the people involved, but the reality is that the timeline is highly unpredictable and clearly depends on external factors.
In my opinion, a “good” startup ready to attract fundings will probably close a Serie A investment with a couple of paid pilots running and some early indications that customers are ready to pay once the product is out of production. While for Serie B investments, they will often have figured their product-market fit, but still, suffer from an immature go-to-market solution.
A Strong Marketing & Sales Approach
Investors pay a lot of attention to teams and especially diverse teams that can address all the challenges of starting and scaling an AI business. Building a fantastic AI solution is one thing, but finding ways to monetize it efficiently is another. For any startup to succeed at scale, distribution, communication, and sales matter just as much as technology.
A good AI startup will also rely on the experience of both senior sales and marketing professionals to educate the market through a strong content strategy.
Sales and marketing are often overlooked in tech-heavy AI startups, yet sorely needed for success.
It is impossible to underestimate how hard it will be to sell your solution. Most of the time (in B2C), people have no idea what AI is and why they should use it. Additionally, when it comes to selling an AI solution to a large firm, decision-makers do not really care whether it is AI or not. Instead, they are much more focused on the added-value of your solution, whether it can be efficiently “industrialized” and how easy it is to implement and, finally, run.
Professional investors also like to witness some early signals that there will be an efficient way to distribute the product/solution (ACV vs. CAC ratio at scale).
I noticed that homogenous startup teams, especially when composed of mainly AI specialists with no industry-specific or startup experience, tend to fail relatively more often. Building and successfully growing a company requires more than computer science skills.
Real Business Issues
Before developing any AI solution, you must ensure strong interest and validation from potential future customers. You have to make sure that your solution will address an important pain point for a well-defined and big enough target audience/market.
Customer Validation: evaluates the stability, satisfaction, and adoption of a technology product before launch and throughout maturity by leveraging real customers using the products in real environments over time.
The idea is to prove that customers can validate that the product has a chance to provide a large enough ROI for them to try it out or to switch from competing non-AI-powered SaaS products. In the B2C context, will people see a real benefit to the adoption of AI-powered solutions? Most of the time, AI solutions cost more than “traditional” solutions, so it is essential to justify the price difference with clear benefits for the customer.
Focus on pain points and use cases where artificial intelligence can address previously unsolved problems or tackle existing ones 10 times better.
In general, it is never a good sign when a startup brags too much about the AI aspect of its solution while the actual problem that it helps to solve remains unclear. Investors tend to invest more in startups that can bring real outcomes rather than in more science projects.
Your customers expect that you understood their business while being very good at carrying your AI solution out. In a B2B context, your solution might be great from a technological perspective but still has to deliver on an operational point of view. It should not add more complexity to existing processes but easily integrate with the architecture of your client’s tools while being able to maintain low running costs.
Don’t assume that because it’s AI, companies will immediately buy it.
Investors seem to appreciate when a startup is applying AI to a narrow domain. Solving business problems requires to think beyond narrow technical approaches, but also to focus on and own a specific business domain and function.
Are you solving the right problem?
The amount of data needed depends on the scope of the problem. I recommend you to identify your domain before you start collecting data. Moreover, it makes more sense to develop an AI solution for a very precise business issue in a given industry. It will help your sales team focus on key decision-makers.
Product & Revenue
Unfortunately, getting customers to commit to a single pilot program is not sufficient to support the viability nor scalability of your business.
- Is the source of your revenue durable?
- Do you enjoy ongoing revenues?
Furthermore, the added value of your AI solution must be delivered to customers in the form of dashboards or actionable insights that they can interact with. Adding personalization options to the outcome of your solution could not please your clients more.
From an investment perspective, VCs are interested in companies that have the potential to become a category/segment leader and dominate their market.
I noticed that an excellent approach to AI is to get as many users to contribute with their data to the product. The more you do so, the less they are likely to churn as they can witness that the product has improved and has been able to adapt to each of their specificities.
Basically, each new data labeled by a user is increasing the lock-in of this specific customer and increasing the overall value of the product. Your solution/product becomes better while and because the customer is using it.
AIaaS companies are different from SaaS companies. Indeed, the nature of AI creates several differences with traditional business models. AIaaS ROI will depend on many factors such as the amount of data processed, time, and product usage. As a consequence, investors will expect you to have these key elements well planned!
Data is perhaps the most important aspect of an AI startup. As such, investors tend to raise the following questions:
- How do you source data?
- What is your data strategy?
- Are you relying on big firms to provide you with data?
When it comes to data, I realized that both methods could be interesting for a startup, but investors will always prefer data independence over. The simple fact that you have built a unique dataset is highly appreciated and valuable for an investor. A unique dataset is a real asset for a startup.
In addition to being unique, your data sets, of course, need to be relevant to the challenge to be overcome.
A unique dataset will prevent other players with more resources to gather more data faster and consequently improve their algorithms’ performance. If you have just started, I recommend you to come up with creative ways of creating and obtaining meaningful datasets; try partnering with unique organizations in exchange for your AI solution!
Other solutions exist such as APIs, open source, or private databases, that you can purchase. As mentioned before, why not explore the potential for mutually beneficial partnerships and innovative business models (revenue sharing) that allow access to proprietary of hard-to-access data.
Investors also take into consideration whether a company works with fast-moving or static data. Algorithms for fast-moving data, such as the real-time images processed by a self-driving car, are often much more complex.
Another important thing that I have noticed when dealing with VCs is that it is important that the startup demonstrates an ability to continually enhance its performance based on the uniqueness of its data. It is a huge plus when your startup can showcase the ability to quickly process training data and optimize efficiently its algorithms while systems become more robust.
Most investors do rely on technical experts and industry advisors that can determine whether the startup is properly managing data architecture, data collection, storing, parsing, etc.
The ability to create and benefit from feedback loops is also appreciated. Indeed, close integration of user feedback into the product allows for superior model performance and, more broadly, a better product experience.
More users, More Data, Better Products.
Investors tend to particularly appreciate when the user experience adapts to the type of data required to improve the algorithm’s performance. This feature is still a work in progress for most startups.
Don’t focus on building an AI infrastructure
Finally, I recommend startups not to focus on AI infrastructure. I believe it will remain a field dominated by much larger firms such as Google, Microsoft, and Amazon. If the solution already exists, then you would benefit more from building on top of it.
Building an AI startup takes time and it is easy to lose sight on your customer and some business metrics that are key to attract VCs.
Original. Reposted with permission.
Bio: Alexandre Gonfalonieri is an AI consultant and writes extensively about AI.
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