Cambridge
Academy
In London

Introduction

AI is reshaping program management by accelerating planning, improving forecasting accuracy, enhancing risk sensing, and strengthening decision support across complex, multi-workstream programs. This practical program equips program managers with AI-enabled methods to build integrated plans, anticipate delivery risks, generate executive insights, and govern responsible AI use with strong controls and human oversight.

Course Objectives

By the end of this course, participants will be able to:

·        Identify and prioritize high-value AI use cases across the program lifecycle

·        Use AI to improve planning quality, dependency mapping, and workstream orchestration

·        Apply AI-supported forecasting to predict schedule, cost, and delivery confidence

·        Enhance risk and issue management with AI-enabled early warning indicators

·        Produce executive-ready decision packs using AI-assisted narratives (with validation)

·        Establish governance, controls, and safe workflows for responsible AI in program delivery 

Target Audience

This course is designed for:

·        Program managers and senior project managers leading multi-workstream initiatives

·        PMO and transformation office professionals supporting program governance

·        Project controls, planning, risk, and reporting managers

·        Product/program leaders working in agile, hybrid, or waterfall environments

·        Business and IT leaders involved in program decisions and reporting

Course Outlines

Day 1: AI Foundations for Program Management & Use-Case Discovery

·        Where AI fits in programs: planning, forecasting, risk sensing, reporting, decision support

·        AI capabilities and limits: accuracy, explainability, hallucinations, and human-in-the-loop

·        Data readiness: baselines, definitions, access, confidentiality, and quality

·        Use-case backlog and prioritization: value sizing (speed, predictability, decision quality)

·        Activity: Build a personal “AI for program management” use-case map + prompt library

Day 2: AI-Assisted Planning, Work Breakdown & Dependency Mapping

·        AI-assisted scope shaping: outcomes, deliverables, and acceptance criteria

·        Work breakdown structure (WBS) and workstream decomposition using AI support

·        Dependency mapping: critical path identification, sequencing, and integration points

·        Capacity constraints: resourcing, change saturation, and bottleneck detection concepts

Workshop: Build an integrated plan outline (WBS + milestones + dependencies + RAID baseline)

Day 3: Forecasting Delivery Confidence (Schedule, Cost & Risk)

·        Forecasting approaches for program leaders: trend-based and probabilistic concepts

·        Predictive signals: milestone slippage patterns, burn rates, backlog trends, and risk exposure

·        Early warning indicators: thresholds, triggers, and escalation workflows

·        Model validation basics: avoiding bias, managing false positives, and monitoring drift

·        Practical activity: Forecasting simulation (predict slippage/cost pressure and define interventions) 

Day 4: AI-Enabled Decision Support & Executive Reporting

·        Decision framing: turning program signals into clear decision asks and options

·        AI-assisted narratives: executive summaries, variance explanations, and recommended actions (with checks)

·        Dashboard modernization: exceptions-first views, confidence indicators, and dependency views

·        Steering committee readiness: preparing materials, pre-reads, and meeting outcomes

·        Case study: Steering committee simulation using AI-generated options and trade-offs

Day 5: Responsible AI Governance, Controls & Implementation Roadmap

·        Governance for AI in programs: roles, approvals, accountability, and escalation

·        Controls and assurance: data validation, audit trails, documentation, and human review gates

·        Risk management: confidentiality, IP, vendor tools, and misuse prevention

·        Adoption plan: training, playbooks, and embedding AI into program operating rhythm