AI App Development Guide for Business Owners

A Guide to AI Application Development for Business Owners
To properly begin exploring the AI ​​app development process, it’s important to first understand how these projects differ from regular app development projects. When it comes to AI, each problem requires a unique solution, even if the company has done similar projects before. On the one hand, there are various pre-trained models and proven methods of building AI. AI is also unique because it is based on different data and business cases. For this reason, AI engineers often begin their journey by diving deep into the business case and actionable data, exploring existing methods and models. The development environments of AI projects can be seen as a three-layered pyramid consisting of ready-to-use technologies and solutions.

The top tier has ready-made products suitable for AI use – such as third-party libraries or proven enterprise solutions. A good example is Google’s solutions for fraud detection, face recognition, and object detection.

The second level consists of new niches that describe business challenges. We may have a suitable model to solve the problem, but the technology needs a little change or adaptation to prove its effectiveness during implementation. A model must be specialized for its particular use, leading to a new niche in the use of AI.
So you decide to build a new AI application from scratch. Because of this, you have no infrastructure to integrate an AI application. This brings us to the most important question: Can the development of AI features be managed in the same way that normal app features like logging in/out or sending/receiving messages and photos are managed?

At first glance, AI is just a feature that users can interact with. For example, AI can be used to determine whether a message should be considered spam, to recognize a smile on a face in a photo, and to implement AI-based login using facial and voice recognition. However, the development of AI solutions is still young and research-based. This leads to the realization that the AI ​​parts of the application are the riskiest part of the entire project, especially if the business goal requires the creation of a new and complex AI solution.