AN EXPLORATORY STUDY ON WHY AI LIFECYCLE MODELS NEED TO BE REVISED

This paper examines why AI life cycles need to be revised to suit recent advances in technology and consumer needs. The findings from the research show that the current AI lifecycle models are facing many challenges that can be addressed by revising their states and approaches of how they are operationalized or deployed. The paper outlines the fact that AI lifecycle stages have been ignored by existing lifecycle models. This involves the collection of data, feasibility analysis, reporting, model surveillance, and model risk evaluation [1]. This research also shows that the real complexities of implementing artificial intelligence go far beyond advanced learning algorithms - more attention needs to be given to the entire life cycle. In particular, irrespective of current artificial intelligence development tools, it is noted that they still do not meet specific features of this area. As they progress toward AI, the majority of companies develop data science teams comprised of individuals knowledgeable about AI algorithms, processes, and techniques [1]. However, many of those organizations, instead of getting the initiatives into full operation and incorporated with current applications and processes, are struggling to make their AI projects genuinely applicable to companies. This is why so many stakeholders in the industry see only a small proportion of AI projects as genuine success. Clients from across industries recognize quickly that they need a systemic "operationalization" solution to AI to drive AI performance [2]. This method ensures that the entire AI end-to-end life cycle is revised and managed