Why AI Investment ROI Is Difficult: Because Effects Are Hard to See
When investing in AI, many people stumble first on ROI (Return on Investment). It's not as simple as advertisements where spending more automatically boosts sales: AI enters the middle of business processes, and its value tends to distribute across multiple departments. Moreover, while a PoC (Proof of Concept) gives a tactile sense, in production operations data preparation, operating governance, and related aspects start to matter, changing both costs and benefits in shape.
In this article, we translate the concept of ROI for AI investments into a practical form you can apply in real work.
First Things First: AI ROI Is Not Only About Money But Also Time, Risk, and Quality
When evaluating AI investment returns, it’s helpful to categorize them into four boxes for clarity.
- Increase revenue (Revenue): CVR improvement, upselling, churn reduction, sales productivity, etc.
- Reduce costs (Cost): labor savings, outsourcing cost reductions, fewer inquiries, etc.
- Lower risk (Risk): prevention of misdelivery, data leakage, regulatory violations, audit readiness, etc.
- Improve quality (Quality): response quality, hit rate of planning, code quality, knowledge management, etc.
What’s important here is to separate what is quantifiable for ROI (quantitative) from what is not immediately quantifiable but still important (semi-quantitative/qualitative). AI investments tend to have a lot of intangible value, so designing with that in mind from the start helps gain internal buy-in.
ROI Basics: Put It on a Single Table
A simple ROI takes the following form.
ROI (%) = (Annual benefit − Annual cost) ÷ Annual cost × 100
However, with AI, you need to break down the benefits and costs; otherwise, discussions become speculative. A recommended approach is to create an ROI decomposition table from the outset.
Representative Benefits
- Labor time savings: Reduced time × personnel cost per hour (or outsourcing rate)
- Revenue uplift: Factorization such as CVR improvement × inbound volume × average order value
- Loss avoidance: Decrease in expected loss from incidents (probability × amount of damage)
- Reduction of opportunity losses: Faster response improves order win rate and retention
Representative Costs (Commonly Missed Here)
- Model usage fees: API charges, token-based pricing, inference costs
- Development costs: requirements definition, implementation, testing, reviews
- Data costs: collection, cleansing, labeling, data infrastructure
