AI Augmented Project Management: Using Machine Learning to Improve Delivery Outcomes

Abstract

Background: Artificial intelligence (AI) and machine learning (ML) are progressively practical in project management to improve efficiency, prediction, resource distribution, and risk mitigation. The use of AI in American businesses keeps increasing yet researchers have found no solid proof about which AI features lead to success in various industry sectors. Methods: A quantitative survey was conducted among 210 project professionals across IT & Software, Construction, Healthcare, Manufacturing, Government, and Other Services sectors. The research gathered information about participant demographics and their use of AI technology and their views on project success. The measurement instrument showed reliability through Cronbach’s alpha which produced an overall score of 0.91. Results: Findings indicate that ML predictive analytics for forecasting contributed 25% to project success, followed by AI automation (15%) and risk intelligence (15%). The examination of deterioration results displays that ML predictive analytics harvests the most considerable result on project outcomes (β = 0.41, t = 7.12, p = 0.001) shadowed by AI mechanization (β = 0.33, t = 5.86) and danger intelligence (β = 0.29, t = 4.92). The sector analysis shows IT & Software professionals experience the most significant AI impact at 38.1% while Manufacturing, Government and Other Services report lower percentages. Conclusion: The study reveals that predictive analytics and automation and risk intelligence deliver substantial improvements in project efficiency and decision-making and quality outcomes. AI adoption shows different patterns across various sectors and workforce characteristics which makes strategic planning essential for successful project outcomes.

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