Accurate labor estimation is essential for effective project management, directly influencing budget, scheduling, and resource planning. This session introduces a hybrid data-driven framework that combines similarity scoring with predictive modeling to improve project labor forecasting.

The first approach uses similarity metrics to identify historical projects that closely match current project parameters, drawing on those comparisons for baseline labor estimates. The second layer incorporates machine learning models that leverage project attributes, historical data, and real-time inputs to deliver tailored labor projections.

Together, these complementary methods empower project managers to make faster, smarter staffing decisions and optimize resource allocation. Attendees will leave with a practical understanding of how these techniques can improve accuracy, reduce project risk, and support scalable project planning.

Takeaways:

• Apply similarity scoring to find historical projects that inform initial labor estimates.
• Build predictive models that integrate operational, historical, and real-time project data.
• Combine historical baselines with ML-powered predictions for a more accurate and adaptive estimation approach.
• Improve project outcomes through better budgeting, scheduling, and staffing decisions.
• Explore the potential of AI in automating project estimation and planning processes.