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4 Crucial Lessons for Launching an AI Startup

Artificial Intelligence is as controversial as it is useful. While many voices, including Elon Musk, warn about potential threats of AI and advocate strong control over the technology, AI is already widely adopted. It is in our phones, the cars we drive, and even helps make the food we eat.

Therefore, AI is clearly a very attractive niche for young tech scientists and entrepreneurs. But, what does it take to successfully launch an AI startup? Here is what we would recommend.

What Is an AI startup?

Although AI engineers are in demand and the niche is very profitable, bringing any startup to life isn’t easy, and AI is no exception.

Besides, there are many wannabe AI startups. As AI became a buzzword, thousands of startups claim to use it, while in fact, they don’t.

So, what is a genuine AI startup? We would say that it is a business plan that is impossible without (rather than merely assisted by) machine learning. 

Now that we’re clear on that, let’s see a couple of important things you should have in mind before pitching your AI-based business idea.

Hire People Knowledgable In Business and Use the Right Tools

Running an AI business requires a lot of processes to manage and a team with a variety of skills. Considering the post-pandemic trends, your team will probably have to work remotely.

In practice, that means you want to have as many business automation tools as you can afford. From hiring and HR engines to CRMs and accounting software, you want to leave repetitive work to machines and save as much time as possible. 

CRMs are especially important for startups for several reasons. Firstly, they will help your team streamline the business processes and manage every step of the customer journey. On the other hand, CRM tools will provide you with valuable data and statistics that will allow you to track your results.

Besides, the core of AI startup teams are people knowledgeable in technology. However, although their expertise is vital, it is not enough. Therefore, they should be assisted by people who will help them swim in the business world. Machine learning engineers need economists, consultants with business experience in the relevant niches, lawyers, etc.

Having a professionally diverse team is essential. Examples of failed AI startups show that engineers don’t always know that something they are doing is illegal. In turn, they could accidentally get themselves in big trouble and ruin the entire project without intending to. Also, taxation, paperwork, and penalties can be hard to deal with unless they consult a lawyer.

Build a Proprietary Dataset

Data that will be used to train AI algorithms is a cornerstone of every AI startup. Where will you find the right data, and how? That is a crucial question your team needs to answer.

There are several strategies to build a proprietary dataset. Some startups build an initial product/service and then collect the user data while the product is being used. However, if you decide on this approach, make sure to stay fair to the customers and use only ethically sourced data.

The other option to collect data is to build an algorithm that will collect the data itself. You will be targeting early adopters and be able to collect colossal amounts of data. The risky part here is that you’re in the public, so it is easy for your competitors to steal your idea.

The third option is to partner with another company, organization, individual, or institution and gain access to their databases. In return, you could offer your AI solution for free.

Most professionals suggest the third solution, as it proved to be the most efficient and quickest of all.

Solve Real-Life Problems

Never forget that your customers are real people with real-life problems. Although your start-up requires highly proficient tech knowledge, you have to translate that to an average user, and explain what is your solution and why would people invest in it and pay for your services.

Currently, AI is being applied in almost any industry. However, there are several industries that particularly thrive from AI solutions.


Banking services and the crypto industry enjoy great help from AI algorithms. For example, some of the most reliable exchanges use AI to enable their customers to buy Ripple, Bitcoin, Ethereum, and other crypto-assets.


During the pandemic, AI proved to be of great use. It helped patients access remote care with more ease while relieving the brick-and-mortar healthcare system for more severe cases.


The virtual game interfaces have never been more engaging, interactive, and empowering thanks to AI technology.


Cars run by AI-powered systems are faster, safer, and consume far less energy than manually driven cars.

Business Process Automation Tools

Finally, AI is a basis for a variety of CRM software, which makes artificial intelligence very helpful in marketing.

For example, paid search ads are largely run by smart algorithms. At the same time, tools such as QuillBot AI and Jarvis are great automatic content writers.


Determine Your Funding Strategy

Designing, building, and testing an AI product is expensive. It takes a lot of money, time, and energy, which is why you need a secure and sufficient source of funding in order to succeed.

The main catch with AI startup funding is that people still don’t quite get it. Investors sometimes simply don’t understand the idea, although it is actually great. Therefore, it is impossible to overestimate the importance of a detailed yet comprehensive pitch deck presentation. 

You need a good story, a lot of research, and knowledge of local legal regulations, markets, and current industry trends. 

Also, you will need patience. Just because people didn’t get it the first time, it doesn’t mean that the idea is bad. Some successful SaaS companies had more than 20 presentations before getting the investment.

It is important to carefully think about which strategy is more suitable for your business idea:

The former comprises institutional investors that value strong teams, a large market size, and good initial traction. They are willing to risk if the potential gains are significant. 

On the other hand, invoice financing (a.k.a. Receivables financing and invoice trading) is a form of loan that allows you to borrow money against customer invoices, which serve as collateral on the borrowed capital. Businesses find invoice financing to be easier to qualify for because it doesn’t depend on any lending minimum requirements.

Angel investors are usually individuals who are not willing to wait too long and don’t like to risk much. However, angel investors tend to stay out of your business and you get more freedom in developing your project.

Are You Ready to Launch Your Startup?

As we could see, before even thinking of launching an AI startup, you need to have people knowledgeable and experienced in computer science and machine learning. But, it is not enough. Apart from expertise in technology, you will need business consultants and lawyers, a solid proprietary dataset, a comprehensive story behind your product/services. Finally, you will need a carefully planned funding strategy.