AI TRAINING
Managing AI Implementation Projects at Scale
Lead complex AI programmes with confidence across workstreams, vendors, and executive stakeholders.
What it covers
This practitioner-level programme equips engagement managers and project managers with the frameworks and tools to orchestrate large-scale AI deployments from initiation to value realisation. Participants learn multi-workstream coordination, stage-gate governance, vendor selection and oversight, and how to communicate AI programme progress to C-suite stakeholders. The format combines structured modules with hands-on case simulations drawn from real enterprise AI rollouts. Participants leave with reusable templates for risk registers, steering committee decks, and AI readiness assessments.
What you'll be able to do
- Design a multi-workstream AI programme plan with clear dependencies, milestones, and stage-gate criteria
- Conduct a structured vendor evaluation and draft an SLA framework tailored to AI solution providers
- Build and deliver an executive steering committee presentation that frames AI programme risk and progress in business terms
- Apply an AI-specific risk register to identify and mitigate technical, organisational, and data-related programme risks
- Define measurable value-realisation milestones and track ROI across the programme lifecycle
Topics covered
- Multi-workstream coordination and dependency mapping for AI projects
- Stage-gate review design: criteria, governance cadence, and go/no-go decisions
- Vendor management: RFP, SLA definition, performance monitoring, and offboarding
- AI project risk identification, mitigation, and escalation frameworks
- Client and executive communication: steering committees, status reporting, and narrative framing
- Change management integration within AI delivery programmes
- Budget tracking, resource allocation, and ROI milestone definitions
- AI readiness assessment and programme scoping methodologies
Delivery
Delivered as a blended programme over three to five weeks, combining live virtual instructor-led sessions (2-3 hours each) with async case work and peer review. Optional in-person intensive available for cohorts co-located in one city. Approximately 60% hands-on through simulations and template-building exercises; 40% conceptual frameworks and facilitated discussion. All materials — governance templates, risk registers, steering deck blueprints — are provided digitally and licensed for participant reuse in client engagements.
What makes it work
- Embedding a dedicated AI programme governance cadence with clear stage-gate owners from the start
- Co-creating risk registers with both technical leads and business stakeholders to ensure completeness
- Establishing a steering committee rhythm with pre-agreed reporting formats so executives stay informed without micromanaging
- Aligning programme milestones explicitly to business KPIs rather than technical delivery outputs
Common mistakes
- Treating AI projects like standard IT rollouts without accounting for iterative model development cycles and shifting data requirements
- Underestimating vendor management complexity — AI vendors often require hands-on co-development rather than traditional delivery handoffs
- Delaying executive communication until major milestones, leading to misaligned expectations and last-minute scope changes
- Failing to define value-realisation checkpoints early, making it difficult to justify continued investment after initial build phases
When NOT to take this
This programme is not the right fit for individual contributors or junior analysts who do not yet own delivery accountability — they would benefit more from a foundational AI literacy or prompt engineering course before taking on programme management content.
Providers to consider
Sources
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