4 Ways to Build a Successful AI Startup The startup life is full of ups and downs -- here's how to make it easier.

By Archil Cheishvili

Opinions expressed by Entrepreneur contributors are their own.

When I conceived the idea of my first AI startup, Palantine Analytics, I was a bright-eyed go-getter who dreamed of brewing up a storm in the industry. By the time I moved on to my current venture, GenesisAI, I had transformed into a veteran who had seen the good, bad and downright ugly days that mark a startup's journey. But for what it's worth, it did offer me enough exposure to learn from my mistakes and grow as an entrepreneur. As a result, GenesisAI enjoys a modicum of popularity and is growing steadily.

As they say, a wise man learns from the mistakes of others. On that note, here are some of the key things I learned about how to ensure a successful AI startup.

1. Develop and maintain proprietary data

High-quality proprietary data is an invaluable asset for an AI startup. Be aware that it will play a clinching role in whether or not your startup succeeds.

So before you get started, ask yourself questions regarding data types, data sources, data strategy and data-collection methodology.

You can acquire proprietary data in multiple ways. You can build proprietary data by collecting it yourself, deploy an ML-based algorithm for manual data capturing or source data from third-party providers. Out of these, I recommend the second option as it gives you intrinsic data that is relevant and accurate. Of course, you may face a few difficulties initially while training your machine, but once you fine-tune this detail, you can easily harness viable data that will give you a competitive edge.

Related: How Entrepreneurs Can Use AI to Boost Their Business

2. Offer real-life solutions to real-life problems

While building a practical AI-driven solution, you need to focus on real-life problems that it can address. You could present a solution that helps sales and marketing or an application-modernization solution. Whatever it may be, it needs to be grounded in reality to find many takers. Once you have identified this solution, make it the guiding light of your AI startup and focus on it solely.

Since startups are largely flexible, it can be tempting to assimilate other products and offerings, but it will only make you lose sight of the main goal. So hold off diversification until you can consolidate your presence with an existing solution.

3. Hire staff that can support your initiatives

During my time at Palantine Analytics, we greatly emphasized the role of employees and staff as enablers of growth in a startup. So, of course, I wanted to replicate that when hiring for GenesisAI. Our primary goal was to hire the right talent for the right position with due consideration to their qualification and how they would fit in our work culture.

But our focus was not limited to technical hiring. We tried to make GenesisAI as diverse as possible. Hence, if you were to take a look at our business Advisory Board, you will find leaders from various industries and walks of life from professors to CEOs to directors who offer a balanced view of how to proceed.

Related: Here's What AI Will Never Be Able to Do

4. Speak the language of your clients

When you are working in a tech startup, it is easy to get carried away with all the under-the-hood workings and ramble about it to maintain your subject-matter expertise. That's when you start throwing around jargon and fancy terminologies to express what a high-end solution your AI startup is and this is where the problem germinates.

Even if you have the most brilliant solution to the most pervasive problem, your clients (and investors, for that matter) will be unable to comprehend it if everything is Greek and Latin to them. Simply put, present your AI startup's solution in the simplest words possible. In fact, the more critical it is to your functioning, the more you should dumb it down for maximum impact.

Related: When Should You Not Invest in AI?

Archil Cheishvili is CEO at GenesisAI and the former CEO of AI startup Palatine Analytics. He previously worked at Bridgewater Associates. He graduated from Harvard University with a B.A. in economics.

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