AI TRAINING
Business Case & ROI Modeling for AI Engagements
Build defensible, board-ready AI business cases that survive scrutiny and guide staged investment decisions.
What it covers
This programme equips consulting partners and in-house strategy teams with a rigorous framework for quantifying AI value, structuring staged investment theses, and avoiding the overclaiming traps that erode credibility. Participants learn to build financial models that incorporate uncertainty, sensitivity ranges, and risk-adjusted scenarios tailored to AI's non-linear value curves. The format combines instructor-led case workshops with live model-building sessions using real engagement data. Participants leave with reusable templates, a peer-reviewed business case for an active or recent AI initiative, and the language to defend assumptions with technical and executive stakeholders alike.
What you'll be able to do
- 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
Topics covered
- 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
Delivery
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.
What makes it work
- 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
Common mistakes
- 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
When NOT to take this
This training is not the right fit for early-stage teams that have not yet defined an AI use case — they need use-case scoping and prioritisation work first, before any ROI model will produce meaningful outputs.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.