Across industries, companies are collectively investing billions each year in AI technologies, yet the actual business impact is proving difficult to assess consistently. Many struggle to measure real-life returns on AI.
To tackle this, we have outlined an approach that offers a practical way forward, with discipline.
Get Serious About AI ROI
Why are we talking about ROI?
Companies have honed their skills in measuring investments, e.g., in their factories, tools, and people, yet when it comes to measuring AI investments, there seems still quite some work to do to improve ROI measurement maturity
Based on our empirical experience, we claim that 90–95% of AI investments are nowhere near the required level of systematic approach – or anywhere near the CEO’s and Executive Team’s agenda it needs and deserves to be.
What is ROI in AI?
AI ROI capability refers to an organization’s ability to define, measure, and manage the business value created by AI initiatives. At its core, it’s about setting clear, outcome-oriented KPIs and building the technical and financial infrastructure needed to track them accurately.
In short, AI ROI capability is all about building the systems, skills, and discipline to make AI value measurable and actionable.
How to move forward with AI ROI?
Renessai has been collaborating with the top experts from the academic world, and together we have developed a model for our clients for measuring AI's ROI.
Several things matter when it comes to AI ROI
AI type, design, and purpose
Industry context
AI Maturity
Leadership philosophy
Measurement style
Regardless of the approach, one principle holds across the board:
ROI increases significantly when measurement is focused and steered to the high-leverage and high-volume use cases of the company.
Further, you shouldn't underestimate the impact of small productivity gains in high-volume work as they might compound into big numbers at scale.
If you want to hear more about how this discipline, contact us: