The second level consists of new niches describing business challenges. We may have the appropriate model to solve the challenge, yet the technology requires a slight modification or adaptation to prove its effectiveness during the implementation. The model is supposed to be specialized for its particular use case, and this leads to the emergence of a new niche in AI usage.
The first thing an app development company does when it has been tasked by a product owner to create an AI-powered application is ask about clients’ needs and data. Is AI the core of the product or an additional component? The answer to this question impacts how sophisticated the solution will be. At first glance, AI is just a feature users can interact with. For example, AI can be used to detect if a message should be considered as spam, to recognize a smile on a face in the photo, to implement AI-based login with the help of face and voice recognition.
You may ask, how come Snapchat-like filters have the most risk? Here is a simple answer: to create a snapchat-like filter you have to involve a lot of cutting edge technologies like AR and deep learning, mix them properly together and put them on mobile phones that operate with low computational resources. To do so, you have to solve a lot of extraordinary engineering tasks.
To prepare input, we need to consider the business problem. Businesses address app development companies with the business problem, and it’s the job of the latter to find the intersection point of the business and the capability of AI.