Piloter des projets d'implémentation IA à grande échelle

Ce qu'elle couvre

Ce programme de niveau praticien dote les responsables de missions et les chefs de projet des cadres et outils nécessaires pour piloter des déploiements IA à grande échelle, de l'initialisation à la réalisation de valeur. Les participants apprennent à coordonner plusieurs flux de travail, à mettre en place une gouvernance par jalons, à sélectionner et superviser les fournisseurs, et à communiquer l'avancement du programme aux parties prenantes dirigeantes. Le format combine des modules structurés avec des simulations de cas pratiques tirés de déploiements IA réels en entreprise. Les participants repartent avec des modèles réutilisables : registres de risques, présentations de comités de pilotage et évaluations de maturité IA.

À l'issue, vous saurez

• 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

Sujets abordés

• 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

Modalité

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.

Ce qui fait que ça marche

• 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

Erreurs fréquentes

• 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

Fournisseurs à considérer

• DataScientestdatascientest.com →
• Wavestone Academywww.wavestone.com →
• Mavenmaven.com →
• Coursera (AI Product Management Specialisation)www.coursera.org →

Sources

• PMI Pulse of the Profession: AI in Project Management →
• McKinsey: Delivering large-scale IT projects on time, on budget, and on value →