5 ways a non-technical founders can start an AI company

In current times and with the boom of artificial intelligence, you might be thinking about starting an AI company. You have an idea that you believe will make an impact. However, it appears that the conversations around building an AI product, let alone a company, centers around people with PHDs, academics or founders with tech backgrounds. 

As with any early-stage innovation and when the industry is not mature enough, those who are actively invested in improving and advancing new technologies, like AI, are typically tech people who communicate and speak in their language about the latest developments and strategies to their network. It can be daunting for someone who can’t even build a website to jump in and understand what they are talking about. The information shared around the web and at events feels so inaccessible to people who don’t have a tech background.

The reality is that everyone doesn’t need to be an AI developer in order to benefit from advanced technology. You can still build a company; it is in society’s best interest to bring diverse thoughts and ideas to bring these innovations to life. Here are 5 ways you can get started to build your own AI company. 

#1. Choose a business idea that centers around your expertise

Just building an AI product alone has many learning curves and trial and error. In order to be effective with your time and money, it is best to leverage your expertise. 

AI is expensive now, and if you think you can acquire the capital to invest in areas you’re not familiar with, you may get away with it. Realistically, you will be the minority. Choosing business ideas not centred around industries, problems, or activities you previously have experience with is a difficult path to take.

By sticking to your lane or by choosing to focus on what you know, you will have a good solid understanding of the problem you are solving. After all, when you are starting a business, you are doing so because you see that no better options exist in the marketplace, and you want to improve the situation.

AI is the process that leads to the solution, but not the solution to itself.

Your goal is to focus on the business case for your solution. know what already exists or what’s not working with the current offering. Get clear on your problem and assess why AI is the best way to solve the problem.

#2. Get a plan to start collecting and managing data

At the core of all AI is data. Having access to data and lots of it can make a difference if your product works. Most of us are starting with no access to data and will need to start collecting data.

When it comes to AI, data can be text, audio, image, video, etc. These data contain information that you will provide to the AI system to memorize and learn details of the content.

Data can be collected either internally. Many organizations have access to data. Some of the data may be previous works or assignments that you have done. You will need to gather these data in a more convenient format for the AI system to learn.

Data can also be collected externally. You may purchase data or take data from free and open-source sites. However, be mindful that if copyright laws exist, you want to ensure that the data you purchase or get for free can be used for your intended purpose. 

#3. Start a data governance plan from the beginning

As new laws and regulations start to take shape in the AI space, it is important that you learn which data is considered private information in your industry, country and the features of your application. You will need to track where you collected the data and can explain to someone how you intend to use the data. 

In some cases, when you are handling sensitive data, you’ll need to oversee how that data is being used and that you are not breaking any laws for accidentally mishandling them.

When your team is small and you are contracting people to help build your product, they may not have time to do all the data collection and management logistics. You will need to be responsible for all the data you collect. 

I think it is smart to start planning how you collect, manage and use the data. So that when new regulations call for it, you are already and ahead of the game.

#4. Find a solution to the problem that has the biggest impact, but a narrow focus

When data is so important to the success of the application, you don’t want to be in a situation where you spend too much time collecting data that may be irrelevant to the problem you are solving or doesn’t bring an immediate impact. It is costly and timely.

If you seek to gain maximum results right away, you need to be looking narrow but not too narrow so that your solution doesn’t provide value to the customers or people using the AI system. 

Take a look at automatic-driving vehicles. To date, these technologies haven’t been successful at being on the road with other vehicles. The reason is that companies are collecting as much data as possible. Just getting the car to stop at a stop sign requires tons of data. There are stop signs with lights. There are stop signs with symbols. Then you have to collect data on where the stop signs are located. Is it front, to the left or right side or above? Are they slanted or perfectly upright? What about the person managing the crosswalk or the police directing traffic? How does all of this work in your state, province, or country?

Suddenly you see that there are so many visuals for stopping a vehicle. Collecting all these data can take years, but by starting small. One company focused on building an autonomous vehicle for one retirement community. where homes, crossroads, street lines, and roads are so predictably the same. They were able to get all the necessary data to build the autonomous vehicle. They didn’t try to start building a vehicle for anywhere and everywhere.

#5. Start looking for someone who has the expertise to build your first model

Once you have your data sorted and you have a use case or problem that you want to build, you then have enough resources to start building the technology. You don’t need a cofounder and shouldn’t spend time looking for one. I believe your time is better spent looking for someone you can hire to reach each milestone. That is freelancers and contractors as needed. Use your advisors as your CTO or tech lead to help direct your product roadmap.

You can find these people on freelance sites, through your network or visiting groups where people with AI want to learn and improve their skills. You aim to start building a basic model to see how the machine performs and learns well. You won’t get the best model on the first try. You may discover that you may need to collect more data or that you need to try different algorithms and techniques to generate better results. As I said, you are going through a trial and error process when building an AI product and tool. Don’t overly invest too soon.

Just get started

Building an AI company is about getting started, building new experiences, and learning new lessons. Don’t worry about not having all your resources upfront. You can always start with one of the five focuses I’ve shared here.

If you are thinking about starting an AI business, get serious and commit to building it today. None of the points that I’ve shared requires you to have a technical background. It is the foundation for building any business.