9 Essential Principles of Creating a Successful AI-based Product (a must-read for founders)

Against the backdrop of the AI rush, intelligent solutions are popping up exponentially, and the desire to quickly launch and market an AI-based product is often so strong that companies sacrifice the quality of the product against it.

If you are working on a new AI-powered product and feeling a bit lost, this article will help you answer questions and organize your ideas. We have hand-picked essential entrepreneurial principles (rooted in our own experience) that will help you create and launch a high-quality product, rather than another pathetic imitation of dozens if not hundreds of AI-based products flooding the market.

1. Focus on Business Objectives Rather Than Algorithms

Yes, AI technology might help your product stand out from other software on the market, but technology is just a means to the end of providing customers what they really need. To be honest, users don’t care whether you use AI technology or not. They care about their solutions, and if your product provides them, they are in.

That’s why as with any other product, start by focusing on the users’ pain points. What will your value proposition be? Then the users will be hooked. Not by the AI itself, but by how effectively it effectively addresses their pain points. And AI will only enhance the value proposition, providing one or more of the following benefits—reducing costs, speeding up processes, or streamlining the customer experience using automation and personalization.

2. Choose Your Niche Responsibly and Carefully

Before entering a new market, test the water. Is the market ready for AI solutions? Are there any regulatory hurdles that you should consider?

There are industries where most of the data is still not digitized or poorly structured. For example, some industrial enterprises are only at the start of the digital revolution. This means that the AI ​​may not have sufficient data to work with. Long story short, if you cannot solve at least one problem that guarantees the successful implementation of your product while remaining relevant, think twice before launching.

3. Make Sure the Data Can Be Collected and Processed

What are the data sources you’ll be using to train your AI-based software? Training ML models requires large amounts of high-quality data. Think over all process implementation stages: the methods you will use to collect and process the data, its volume and variety, confidentiality, and security. Remember that the quality of your service and the reputation of your product depends on these crucial factors.

This is very important because if you decide to use a supervised learning approach, and your clients do not have that information, or the resources it is based on are not secure, the data will be insufficient for the model to act upon.

4. Focus on Usability and UX

Even a good product may end up in the garbage if it is challenging to implement or use. Remember who you are creating this product for. In other words, who is your ideal customer, and why might they need your product?

The same principle applies to your website, your contact form, and so on. It would be a shame to lose leads because logging into the system is so complicated it’s impossible to do without a tutorial, or the website doesn’t load, or if it takes hours to fill out the contact form.

5. Don’t Undervalue Marketers and Salespeople

Many business owners mistakenly believe that AI sells itself, and an “AI-powered” label on a landing page is enough to attract a steady stream of customers. That’s not necessarily true. Human communication is still key. At the end of the day, turning leads into actual customers is the responsibility of marketers and salespeople.

Making your product stand out from a crowd is a competitive endeavour, and promoting incredible AI solutions is the task of marketers and salespeople. They are the ones who develop connections with current and future clients, and must also maintain them. The stronger the marketing and sales teams, the better the results will be.

6. Don’t Go Overboard with AI Tags in Your Ads

Try not to focus on AI as your only selling point. By all means take advantage of the hype surrounding AI—such positioning may be an effective marketing strategy. But in the long run, it may be too single-minded and lack strategic depth. As we said before, the product itself should be more important than the packaging. Therefore, your communication and promotion shouldn’t be solely AI-focused.

Instead, let potential users know how your AI-based solution will help solve their specific problems and give them good reasons not to ignore it. Overall, do not don’t assume too much, and perhaps avoid going on and on about how fantastic your AI product is. Consider a range of examples and a variety of user cases.

7. Manage Customer Expectations

Those customers who have never encountered AI before, or have chosen an AI-based solution over other options because they want to solve their problems immediately and expect instant results, might be disappointed. Yes, AI can process large amounts of data and quickly carry out routine and repetitive tasks as well as manual processes, but it is not a miracle cure. Your customers should be reminded that when it comes to more creative tasks, or tasks that require specific knowledge, the first AI iterations may not be as accurate as customers expect them to be. Why?

Because it learns as it performs, with the expectation that performance will improve over time. In other words, the more tasks they perform, the better they get. This is especially true for small-scale niche businesses that may lack large amounts of data.

Explain to customers what problems can and cannot be solved with the help of your software and what results they can expect. Provide example-based explanations and show user cases similar to their niche. Better still, anticipate answers to questions like: “when will the numbers go up?”; “why are my sales still not increasing?”; or “I can’t see the effect, is your software not working?”

8. Don’t Neglect Technical and Industry Experts

Building a successful team is hard because expertise is limited, and the right specialists are hard to find and expensive to hire. However, having a team of experts is crucial to your product’s success.

So what type of competency should you be looking for? It’s impossible to create a high-quality AI-based product without ML engineers, experienced programmers, and data specialists, or without experts from the market that you want to enter.

Industry experts know the market like the back of their hands and can anticipate the target audience’s expectations, give valuable insights about your competitors, understand strategies, and know how to promote the product.

As soon as you put together a balanced team of data scientists, ML engineers, programmers, and industry experts, you will have the right skill-set and expertise to carry out your solution.

9. Iterate and Experiment

To effectively manage an AI startup, you need to cultivate an experimental team spirit: constantly remain open to innovations and transformations of the product, test, and be ready for uncertainties and operational adaptation. If you talk to your customers and constantly gather feedback, this should be a no-brainer.

Each iteration you make will improve your AI-based product in general by expanding its features, increasing model performance, and training the model with better data. So don’t be afraid to experiment, test, and fit into new initiatives and collaborations. In other words, be open-minded and think outside the box.

Wrapping Up

Remember that the Ai industry is far from consolidated—there are still many strategies and things to experiment with if you want to build ML-enabled products. So the playbook on this continues to be written and you are the one who can add a new chapter. Either way, we hope that this article will help you organize your ideas and get started in building an AI-based product.

Good luck!

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