Research

AI ROI Research and Business Case Development

Make AI value measurable and comparable across initiatives with credible ROI models and benefits frameworks that leadership and finance can trust.

The Challenge

AI investments are made on intuition and vendor claims more often than on rigorous value analysis, leaving boards with low confidence in AI business cases.
Benefit claims are not tied to measurable baselines; when an initiative is complete, there is no way to confirm that value was delivered.
Value-driver mapping is missing: the chain from AI capability to business outcome is not documented, making benefit tracking impossible.
Sensitivity analysis is absent: business cases do not reflect the real uncertainty in AI performance, adoption rates, or process change.

Our Approach

Structured AI ROI and benefits research covering value-driver mapping, baseline definition, measurement plan design, cost modeling, and sensitivity analysis. The output is a benefits framework, ROI model templates, a measurement plan, and an assumptions log that finance and leadership can review and trust.

What You Can Expect

A benefits framework that defines how value will be created, measured, and attributed for each AI initiative.
ROI model templates that your team can apply to new initiatives without building from scratch each time.
A measurement plan that defines the metrics, baselines, and review cadence needed to confirm value delivery after launch.
Sensitivity analysis that reflects real-world uncertainty in AI performance, adoption, and cost, so business cases are defensible rather than optimistic.
An assumptions log that makes every claim in the business case traceable and auditable.

3 to 5 weeks per initiative set, scaled to the number of initiatives and complexity of the value chains involved.

Our Process

Value-Driver Mapping: Trace the chain from AI capability to business outcome for each initiative, identifying the key assumptions at each link.
Baseline Definition: Define the current-state metrics that will serve as the reference point for measuring AI-generated improvement.
Cost Modeling: Build a realistic cost model covering development, deployment, and ongoing operation.
Measurement Plan: Design the measurement infrastructure: which metrics, which systems, which owners, and on what cadence.
Sensitivity Analysis: Model how outcomes change under different assumptions about AI performance, adoption rate, and implementation timeline.
Deliver: Produce the benefits framework, ROI model templates, measurement plan, and assumptions log.

Frequently Asked Questions

Build investment-grade business cases for AI initiatives with documented value drivers, realistic cost models, and a measurement plan that confirms delivery after launch.