This article shows practical approaches to measuring ROI in AI projects and presents a structured framework for evaluating success.
The challenge of measuring ROI in AI projects
AI projects differ fundamentally from traditional IT investments. While direct cost savings or efficiency gains are often measurable in conventional digitalization projects, AI implementations often generate indirect added value:
- Qualitative improvements such as higher customer satisfaction or a better basis for decision-making
- Long-term competitive advantages instead of short-term profits
- New business opportunities that were not foreseeable at the start of the project
There is also the challenge of attribution: what proportion of the success can actually be attributed to the AI solution and what to other factors? This attribution problem makes a classic ROI calculation difficult, but not impossible.
The Evoya approach to ROI assessment
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- Business objective analysis: We start with a detailed analysis of our customers’ business objectives. What specifically is to be achieved through the use of AI? Is it about increasing efficiency, reducing costs, growing sales or improving quality?
- Process mapping: In workshops with the client’s experts, we map the relevant business processes and identify bottlenecks, inefficiencies and optimization potential.
- Baseline survey: We measure the current status using relevant KPIs in order to create a solid basis for comparison.
- Potential assessment: Based on our experience and industry knowledge, we estimate the potential for improvement through the use of AI.
- ROI modeling: We develop a customized ROI model that takes into account both direct and indirect effects and runs through various scenarios.
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Key KPIs for measuring the AI ROI
Depending on the application, we recommend different key figures:
Increased efficiency
- Time savings per process (in hours)
- Reduction in manual interventions (in %)
- Reduction in lead time (in days)
Quality improvement
- Error reduction (in %)
- Customer satisfaction (NPS score)
- Employee satisfaction
Business growth
- Increase in sales through new AI-supported products
- Conversion rate improvement
- Customer retention rate
Our ROI measurement framework in practice
In practice, we combine these metrics to create a holistic evaluation framework:
- Direct cost savings: Automation of repetitive tasks, reduction of error costs
- Increased productivity: Faster processes, better decisions
- Strategic value: Long-term competitive advantages, innovative ability
- Risk reduction: improved compliance, reduction of operational risks
It is particularly important to consider the time factor: while some benefits are immediately apparent, others only develop their full potential over a longer period of time.
Example: ROI calculation for an AI customer support project
Here is a concrete example of what an ROI calculation for an AI project in customer support could look like:
| Category | Before AI implementation | After AI implementation | Difference |
|---|---|---|---|
| Costs | |||
| Personnel costs for support (annual) | 500,000 CHF (5 MA) | 300,000 CHF (3 MA) | -200,000 CHF |
| AI license costs (annual) | 0 CHF | 50,000 CHF | +50,000 CHF |
| Implementation costs (one-off) | 0 CHF | 100,000 CHF | +100,000 CHF |
| Efficiency | |||
| Average processing time per request | 15 minutes | 5 minutes | -10 minutes (67%) |
| Number of requests processed per day | 320 | 480 | +160 (50%) |
| Quality | |||
| Customer satisfaction (NPS) | 35 | 48 | +13 points |
| Error rate | 8% | 3% | -5% |
| ROI calculation (1st year) | |||
| Annual savings | 200,000 CHF | ||
| Additional annual costs | 50,000 CHF | ||
| One-off investment | 100,000 CHF | ||
| Net savings (1st year) | 50,000 CHF | ||
| ROI (1st year) | 33% | ||
| ROI (3 years) | 250% | ||
This calculation only takes into account the direct financial impact. The qualitative improvements, such as higher customer satisfaction and a lower error rate, lead to further positive effects in the long term, such as higher customer loyalty and increased sales, which further improve the ROI.
Hypothetical example: AI in knowledge management
Let’s imagine a company that implements an AI solution for internal knowledge management:
- Initial situation: Employees spend an average of 5 hours per week searching for information
- After AI implementation: reduction to 2 hours per week through intelligent search and automatic document classification
- Direct savings: With 100 employees and an hourly rate of CHF 80, this results in annual savings of CHF 780,000
- Indirect benefits: Better decisions thanks to a more comprehensive information base, higher employee satisfaction
Recommendations for companies
1. define clear goals: What should the AI solution specifically improve?
2. measure the current status: you can only demonstrate improvements with a clear baseline
3. Think holistically: also consider indirect and qualitative benefits
4. Focus on agility: regularly review and adjust your AI strategy
5. Plan for the long term: The full benefits of AI implementations often only become apparent over time
At Evoya AI, we support companies not only in the implementation of customized AI solutions, but also in the development of suitable ROI measurement concepts. Because only what can be measured can be improved in a targeted manner.
