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Estimating AI Automation ROI

Build AI business cases that survive contact with reality: measured baselines, task decomposition, review time, total cost of ownership, NPV and payback, sensitivity and Monte Carlo, adoption ramps and post-launch measurement, with every model run.

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What you'll learn

  • Frame an AI automation use case with a measured baseline and clear ROI, payback and NPV definitions.
  • Estimate benefits realistically by decomposing tasks, counting review time, error effects and how freed time turns into money.
  • Build a full cost of ownership: build, run, oversight and maintenance.
  • Model uncertainty with sensitivity (tornado) analysis, Monte Carlo simulation and adoption curves.
  • Measure realised ROI after launch with control groups and reforecast from actuals.
  • Write a credible business case that decision makers can trust.

Syllabus

ROI Foundations

  1. Why AI ROI Estimates Go Wrong
  2. ROI, Payback and NPV
  3. Framing the Use Case

Measuring the Baseline

  1. Measuring the Current Cost
  2. Task Decomposition
  3. Types of Value

Estimating Benefits Realistically

  1. Time Savings With Review Included
  2. Quality and Error Effects
  3. Realisation: When Freed Time Becomes Money

The Full Cost Picture

  1. Build and One-Off Costs
  2. Running Costs
  3. Oversight and Human-in-the-Loop Costs
  4. Maintenance and Total Cost of Ownership

Financial Models Under Uncertainty

  1. NPV and Payback for the Worked Example
  2. Sensitivity Analysis (Tornado)
  3. Monte Carlo Simulation

Adoption, Ramp-Up and Risk

  1. Adoption Curves
  2. Risk-Adjusted Value
  3. Staged Investment and Pilots

Measuring Realised ROI

  1. Attribution With a Comparison Group
  2. Tracking the Drivers
  3. Reforecasting From Actuals

Writing the Business Case

  1. A Business Case Template
  2. Case Study: The Invoice Automation Decision
  3. An AI ROI Checklist