10 hot reasons your AI project might not work out great

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The goal of this blog post is addressing the most common pitfalls happening during AI projects. If your company is starting with AI or planning to hire an external consulting company such as ours, this information is a valuable resource which helps to evaluate your level of readiness for this kind of project. Additionally, this document serves as a guideline to better prepare for our successful cooperation, in case we address most of the issues on this list.

The success of adopting AI and leveraging the insights from the data you are collecting is mostly correlated with the success of the first project you implement. If this first experience fails, most probably you will give up. In case you succeed and see measurable improvements, you will continue implementing small ML modules to improve your product or service. Therefore, it is critical to choose, as your first project, feasible to implement AI improvement that is measurable.


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Nenad Bozic

Co-founder & CEO

Software engineer with more than 10 years of experience currently focused on data intensive systems. Certified Cassandra developer and Datastax MVP for Apache Cassandra for 2016/2017. Strong believer in balance between good technical skills and soft skills. Striving for knowledge is his main drive, which is why he enjoys learning new tools and languages, blogging, working on open source, presenting.