Develop leaders who can weave AI into everyday project practice—turning data into foresight, streamlining delivery, and raising the quality of decisions, governance, and outcomes.
This version emphasizes practical orchestration: how to convert messy project artifacts into reliable intelligence, automate routine coordination, and use predictive signals to steer scope, schedule, cost, and benefits. Participants leave with adaptable playbooks, prompt patterns, and governance guardrails that fit any delivery approach (waterfall, agile, hybrid).
Target Group
Project and program leaders, PMO staff, delivery managers, product owners, scrum masters, business analysts, and team leads driving cross-functional initiatives.
Goals
Spot high-leverage AI opportunities across initiation, planning, execution, monitoring, and closure.
Design prompt patterns and automations that produce consistent reports, action logs, and decision briefs.
Enhance planning quality with AI-assisted WBS, backlog shaping, schedule options, and resource scenarios.
Use predictive analytics and what-if analysis to surface early warnings and prioritize mitigations.
Elevate stakeholder engagement with tailored narratives, visuals, and meeting-ready summaries.
Establish responsible AI operations: data standards, validation, audit trails, KPIs, and ROI tracking.
Target Competencies
Target Competencies
AI-assisted planning and estimation
Predictive risk sensing and scenario evaluation
Stakeholder storytelling and decision support
Delivery orchestration and team enablement
Governance, data quality, and ethical AI practice
Outlines
Leadership Essentials for AI-Enabled Projects
Framing the problem: value hypotheses, constraints, readiness checks
Choosing AI use-cases that move KPIs (time, cost, quality, benefits)
Operating agreements for teams using AI (roles, approvals, escalation)
Data foundations: structuring plans, risks, issues, decisions, and metrics
Guardrails: confidentiality, bias mitigation, and human-in-the-loop signoff
Intelligent Planning & Estimation
AI-assisted scope statements, acceptance criteria, and dependency mapping
Schedule options and buffers derived from historical patterns and risks
Resource scenarios: capacity signals, cost envelopes, and trade-off views
Value-based prioritization for backlogs and change requests
Planning brief: concise, AI-generated pack for kick-off and alignment
* Password must be at least 8 characters long * Password must contain at least one lowercase letter * Password must contain at least one uppercase letter * Password must contain at least one digit
