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Enhancing Project Scheduling with AI Driven Analysis: A Case Study on Helipad Construction
This case study shows how AI can help project teams improve schedule quality in Primavera P6 by finding logic gaps, unrealistic durations, missing links, and other common planning errors. The approach uses an Excel export of the schedule for AI-assisted checks and recommendations, improving confidence in the schedule for project control and decision-making.
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Where Do Preconstruction Schedules Go Off Track? An Evidence-Informed Industry Commentary
Preconstruction schedules most often go off track in eight recurring domains, including vendor information and equipment, permitting and regulatory pathways, utilities and power availability, site due diligence, workforce capacity, owner planning discipline, community engagement, and contracting strategy timing. This commentary provides early warning signals and practical controls teams can implement to reduce preconstruction variance, based on interviews and a targeted practitioner survey triangulated with literature.
Togal.AI vs On-Screen Takeoff: A Comparative Analysis of Time Efficiency and Accuracy
Using Togal.AI delivered about 70% time savings for common takeoff tasks while keeping accuracy within ~5% compared to On-Screen Takeoff, with the biggest discrepancies showing up in smaller quantities. The paper emphasizes that AI improves productivity when paired with human review and manual adjustments, rather than replacing professional judgment.
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