AI projects require a different approach than traditional IT projects in order to succeed. This article covers the 5 main differences between AI and traditional IT projects that would help you adopt the right approach towards clients. First and foremost, specification is completely reversed. What this means is that instead of defining rules, you give the result as an input that will train the AI. This, in turn, means that you have to know the data you are working with. AI projects need to be conducted predictively, with a project design that supports predictive project development.
There are many AI solutions on the market – however, expecting AI solutions marketed as finished products should be taken with a pinch of salt. This article is dedicated to the steps that need to be taken, from AI algorithm to production solutions. All you have to do is make sure that you data is within your ecosystem. Once you have identified a spot where AI solution could be implemented, there are some steps you need to have into consideration. But no worries, a team of experts will help you go through each step.
Check out the interview with Magnus Andersson, our Division Manager for Development and Architecture (DNA) at Seavus Stockholm. He has an impressive background in Microsoft technologies, and a nearly 20 years of working experience in system development. In the interview he talks about what it means to work with Artificial Intelligence in Seavus and the after-work seminar events that they organize to share their passion for AI development.
Development and Architecture (DNA) is in Seavus’s DNA and vision and it will be in the main focus in the Stockholm office. Magnus Andersson, the Division Manager for DNA at Seavus Stockholm says that they have built a solid competence within the AI over the past few years and they now have several customers they are working with.