AI is great. But what does it actually deliver?
As artificial intelligence (AI) projects continue to multiply, one question is increasingly top-of-mind for decision-makers: what return on investment (ROI) can we actually expect? And more importantly, how can we measure it objectively?
It’s a fair concern—every project comes with a budget, timelines, and teams to mobilize. To build internal buy-in, organizations need to be able to estimate the potential value of AI initiatives. The promises are often clear: automation, speed, improved quality. But how do you actually measure impact and align stakeholders around concrete metrics?
This article will help you understand AI’s ROI potential, how to quantify it, and the best practices to maximize your chances of success.
Why measure the ROI of an AI project?
Once seen as purely exploratory, AI must now demonstrate true operational value to gain internal support—especially at the executive level. It needs to be part of a broader effort to improve efficiency and optimize resource use.
Measuring AI ROI serves several key purposes:
- Justifying the investment to general management or finance.
- Aligning business and tech teams around tangible outcomes.
- Prioritizing the right use cases and scope—where impact is real.
What benefits can you expect from an AI project?
This is where things can get tricky—there’s no universal formula. The results depend heavily on the project’s context and scope. Still, if you're not sure where to start, here are some commonly observed benefits from AI projects:
- Time savings on repetitive, manual, or time-consuming tasks.
- Error reduction and quality improvement.
- Better decision-making through faster or more accurate insights.
- Enhanced user and customer experience.
For example, in its deployment of Ask for the Moon for the Technical Support team, DEF—a leader in fire protection—freed up over 500 hours in 2024, allowing the team to focus on higher-value tasks.
How to quantity benefits and compute AI ROI?
The formula is simple on paper:
(Value Generated – Project Costs) / Project Costs
But the challenge lies in quantifying both gains and costs accurately.
Estimating gains
To assess the financial value of an AI initiative, you'll usually need to make educated estimates. Still, even simple indicators can lead to realistic and useful insights. Here are a few levers you can explore:
Time Saved
Whether it’s automating repetitive tasks, speeding up information retrieval, or reducing daily interruptions, time saved is often the easiest and fastest benefit to calculate.
Formula:
Time Saved (hrs) × Average Hourly Cost × Usage Frequency
Example: If an AI saves 30 minutes per case, and your team handles 200 cases/month, that’s 100 hours saved. With an hourly cost of €50 (wages + overhead), that’s €5,000/month or €60,000/year saved.
Error Reduction / Non-Compliance
AI often helps avoid errors stemming from poor access to information or misinterpreted documents.
Formula:
Number of Errors Avoided × Average Cost per Error
Example: On a production line where an error costs €800 (corrections, penalties, downtime), avoiding 10 errors means €8,000 in savings.
Improved Customer Experience
This can be harder to quantify but may result in fewer complaints. You can use a similar formula:
Fewer Complaints × Cost per Complaint
How to measure such gains?
Depending on your setup and available tools, several methods can help:
- Before/After Comparison: If you already track KPIs (e.g. non-compliance rates or processing time), you can monitor changes pre- and post-AI deployment.
- A/B Testing: Compare a group using the AI tool with one that isn’t. This is robust but requires good planning.
- User Feedback: Employees can provide valuable qualitative and quantitative insights.
For pre-project estimates, benchmark results from similar companies or reference large-scale studies by firms like McKinsey or BCG.
What costs should be taken into account?
If you’re using an external vendor or SaaS provider, the solution cost is usually straightforward. However, you may also need to consider:
- Development and maintenance costs (for in-house or hybrid solutions).
- Integration and initial configuration.
- Training and change management for users.
💡 Did you know? Ask for the Moon offers turnkey packages with onboarding included, with no complex technical integration required.
What is a "Good" ROI for an AI project?
Of course, this depends on your industry, objectives, and project scope. But here are some benchmarks from the field:
- Positive ROI in 3–6 months: A well-structured AI initiative should start delivering value quickly, especially for high-volume or recurring use cases.
- 100%+ ROI in 12 months: In a B2B setting, a “good” ROI typically ranges from 100% to 200% annually. Over 300%? That’s an excellent result.
Best pracices to maximize ROI
Here’s how to boost your chances of strong ROI:
- Target a clear business need: Start with a well-defined use case with strong value potential.
- Involve business users early: Co-design the solution with the teams who will use it.
- Track impact from day one: Install KPIs from the start to allow real-time adjustments.
- Prioritize user experience or use a proven tool: Simplicity and adoption go hand-in-hand—no adoption means no ROI.
- Plan for change management: Even with a great tool, internal education is essential.
A successful AI project is one that solves a real business challenge and proves its value in numbers. While estimating ROI may seem complex, it remains a critical lever for decision-making and internal alignment.
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