The Impact of Artificial Intelligence On Agile Project Management: Enhancing Team Performance and Project Success in Technology-Driven Organizations

Abstract

In Agile environments, this is the case because,’ he said, and pointed out that ‘one reason of many is rapid development. In three cases, described above could it be that the behavior of general project management as symbolized by researcher is now outdated and irrelevant? Why Artificial Intelligence: the new way to do project management the adoption of artificial intelligence in project management can dramatically enhance efficiency. At low cost, comparable to or at most exceeding 3% of your budget large and medium-sized companies so why on earth not do that? In Singapore, where many China, India IT male students go to study IT technologies.  In Agile, predictive analytics, machine learning, and artificial intelligence are the means through which project managers leverage data to what extent planning is accurate; whether risks can be predicted or not; how well work processes are functioning. In this paper, we offer empirical support for new frameworks based on actual industry experience and comparative analysis. This awareness was also affirmed in a global survey we conducted during those years. After all, there is not a wealth of evidence that shows those methods actually settled anything for a company. Study subjects were selected from 420professional including project managers, scrum masters and also members of the technical team (by means of collective sampling survey). None of the data was excluded from testing or analysis, and all instruments used in the pre-survey and pilot production have been described fully. These were AIII (AI Integration Instrument), TPIS (Team Performance Inventory Scale) and the PSI (Project Success Index).Data were analyzed using Pearson’s correlation, multiple stepwise regression, and others with SPSS27.Preliminary findings of this research are that integrating AI with one aspect of the working teams (n=59) really has an enormous effect on performance. The efficiency of friends is greatly higher than cold-blooded persons although they may have similar qualifications otherwise. There requirements for soft skills and tedious stuff increase significantly in contrast to this flexibility to change as a team. AI practices backed up by agile practices can also have an impressive effect on project outcome (B=.350, >.001). It’s guaranteed that delivery time is reduced; costs will be cut and the quality still better. With stakeholders satisfied like never before in history of our company the effect will be more than just “very excellent”.  Moreover as a major moderating influences organizational readiness causes AI to have all the more impact in denuding performance and success metrics both. Thus AI works as a strategic enabler for Agile by providing data driven explanations. It automates tedious tasks and improves the accuracy of decision-making. Business Insider reported on June 6th that regular people, colleagues, as well as friends, test the product in real life. But in order to create a version of the weapon for journalists to mass-test its functionality well this process is different from what we see as articles or blogs. The study also found that readers and writers, the least established of all fields, were most break headed by it. Power plant day workers collectively suffered more from information overload than any other group. So the conclusions to be drawn are as follows: A company seeking sustained increases in performance must pursue lasting equilibrium between the knowledge workers on one hand and AI on the other partnership not adversarial contest The implications are indeed vast: for researchers, technologists, and practitioners of Agile development who seek to exploit AI advantage, ideas which are vital in fluid interactive worlds replete with digital technology.