More Accurate Project Planning

In many organisations, resource planning in project management relies on individual knowledge, templates and manual estimates. Historical project data from project planning, time tracking and project controlling exists, but evaluating it for effort estimation, cost planning and capacity planning requires significant effort. At the same time, the causes of planning deviations often remain invisible in variance analysis, even though they are critical for new projects.

Artificial intelligence analyses historical projects, identifies comparable initiatives and makes typical effort patterns usable across work packages, sub-projects and entire projects. This creates data-driven resource planning for projects that combines semantic search, effort estimation and capacity planning. Planners receive robust reference values for project planning, resource demand and cost planning, and spot critical variance patterns earlier. This strengthens project controlling and makes experience from completed projects systematically available for new project requests.