AI workflow engines can greatly support the PRINCE2 principle of Manage by Exception by automating the monitoring of project tolerances and reporting deviations to the appropriate management level.
- Automated Tolerance Monitoring: AI engines can be programmed with set tolerances for various project constraints such as time, cost, and scope. They continuously compare actual performance to these levels in real-time.
- Deviation Detection: Machine learning algorithms can examine project data to detect deviations from planned limits, as well as subtle trends that may lead to an exception.
- Automated Escalation: When tolerance is projected or exceeded, the AI engine can automatically initiate the escalation processes outlined in the project management plan, contacting the appropriate project manager or Project Board member.
- Contextual Information Provision: In addition to the escalation, the AI can provide contextual information about the deviation, such as likely reasons, implications for other project areas, and suggested beginning actions based on previous data or predefined guidelines.
- Exception Report Generation: The AI can automatically generate draft exception reports by pre-filling them with critical data and analysis, saving project managers time and guaranteeing reporting uniformity.
- Learning and Refinement: Over time, the AI engine can learn from previous exceptions and resolutions, improving its monitoring thresholds and escalation triggers for greater accuracy and relevance.
By automating the detection and escalation of tolerance violations, AI workflow engines guarantee that management attention is focused on true exceptions, allowing project managers to manage by exception more effectively and the Project Board to make informed choices only when necessary.
