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.
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.
This article is a reflection of what was talked about AI at the TechWorld Summit on February 15. Many hot topics concerning AI were discussed, among which building solutions with open APIs for more flexibility based on how the framework is developed, privacy requirements, performance and response times.
We are proud to present to you our success story: Jobmore’s case study with DoAI. They have started back in 2012, have 150 full-time employees and 5 consultants who are responsible for the recruitment processes and receive hundreds of applications each week. For that reason, they have decided to implement DoAI as a solution to their recruitment processes.