Modélisation Business Case & ROI pour les Engagements IA

Ce qu'elle couvre

Ce programme dote les partenaires conseil et les équipes stratégiques internes d'un cadre rigoureux pour quantifier la valeur de l'IA, structurer des thèses d'investissement progressives et éviter les pièges de surestimation qui nuisent à la crédibilité. Les participants apprennent à construire des modèles financiers intégrant l'incertitude, les plages de sensibilité et des scénarios ajustés au risque, adaptés aux courbes de valeur non linéaires de l'IA. Le format combine des ateliers animés par un formateur et des sessions de modélisation en direct sur des données d'engagement réelles. Les participants repartent avec des modèles réutilisables, un business case évalué par les pairs et les arguments pour défendre leurs hypothèses auprès des parties prenantes techniques et dirigeantes.

À l'issue, vous saurez

• Construct a risk-adjusted ROI model for an AI initiative including sensitivity ranges and staged value milestones
• Identify and neutralise the five most common AI value overclaim patterns before they reach a board presentation
• Structure a staged investment thesis with go/no-go criteria tied to measurable value gates
• Communicate financial assumptions credibly to both technical teams and non-technical executives
• Perform a Monte Carlo sensitivity analysis to quantify uncertainty bands around projected AI returns

Sujets abordés

• Anatomy of a defensible AI business case: cost, benefit, and risk layers
• Value identification: direct savings, productivity uplift, and revenue enablement
• Sensitivity analysis and Monte Carlo ranges for AI-specific uncertainty
• Staged value capture: horizon 1/2/3 framing and milestone-gated funding
• Common overclaim traps: automation fallacy, productivity paradox, and attribution errors
• Risk-adjusted NPV, IRR, and payback period for AI investments
• Stakeholder communication: translating model outputs for CFOs and boards
• Governance checkpoints and assumption audit trails

Modalité

Delivered as a two- to three-day intensive programme, available in-person or as a live virtual cohort (Zoom/Teams with shared financial modelling workbooks). Approximately 40% lecture and framework delivery, 60% hands-on modelling and peer case critique. Each participant or team works on a real or realistic AI engagement throughout. A shared Excel/Google Sheets template library is provided and retained post-training. A follow-up 90-minute coaching call can be added to review participant business cases after a two-week application window.

Ce qui fait que ça marche

• Anchoring financial assumptions to independently verifiable benchmarks or internal baseline data before building the model
• Using staged funding gates (proof-of-value, pilot, scale) so the business case evolves with evidence rather than being locked in upfront
• Involving finance and risk teams in model review early, converting potential critics into co-authors of the business case
• Maintaining a living assumption log so that when inputs change, the model updates transparently rather than silently

Erreurs fréquentes

• Projecting 100% automation rates rather than task-level augmentation percentages, inflating savings estimates by 3-5x
• Ignoring change management, retraining, and integration costs, which typically add 40-80% to the bare technology cost
• Presenting a single-point ROI figure without sensitivity ranges, making the case brittle under CFO scrutiny
• Conflating correlation-based productivity gains with AI attribution, undermining the business case when challenged

Fournisseurs à considérer

• Strategia (AI Finance & Strategy Practice)www.strategia.com →
• London Business School Executive Educationwww.london.edu/executive-education →
• INSEAD Executive Educationwww.insead.edu/executive-education →
• Maven (AI Business Strategy cohorts)maven.com →

Sources

• McKinsey Global Institute — The State of AI in 2024 →
• Gartner — How to Build a Business Case for AI Projects →