Research · AI Strategy & Adoption

Business Case & ROI

An AI business case must survive the question every CFO asks: what will this cost, what will it return and how will we know? Credible cases count the full cost of ownership and measure outcomes, not activity.

Why it matters

AI spending is under growing scrutiny. Productivity claims based on hours saved often fail to show up in budgets because the saved time is not redeployed or the work was never a bottleneck. Business cases that tie AI to specific financial or operational outcomes are easier to approve and easier to defend later.

What to include in the cost

• Licenses, API usage and infrastructure, with usage growth scenarios.
• Data preparation, integration and security work.
• Governance, testing and ongoing monitoring.
• Training, change management and process redesign.
• Ongoing support, model updates and vendor management.

How to measure the return

Tie benefits to metrics the business already tracks, such as cycle time, cost per transaction, error rates, revenue per customer or case backlog. Establish a baseline before deployment and compare against a control group where possible. Separate leading indicators, such as adoption and task completion, from lagging financial results.

Common pitfalls

• Counting hours saved as cash savings without a plan to capture them.
• Ignoring usage-based costs that rise with adoption.
• Measuring adoption instead of outcomes.
• No baseline, which makes any result impossible to prove.

How to get started

• Pick one business metric per use case and capture the baseline now.
• Build a cost model with low, expected and high usage scenarios.
• Define in advance what result would justify scaling or stopping.
• Report results in the same terms used in the original case.