Key ROI Metrics for Generative AI from KPMG, Gartner & Boston Consulting

After all the AI hype, many businesses struggle to measure Generative AI’s profitability. Insights from KPMG, Gartner, and BCG reveal why this is a challenge and how to overcome it. KPMG has revealed that most organizations are not yet ready to measure the success of their generative AI projects, due to the PoC and experimental stage of the projects. Even among the 44% of the businesses that applied GenAI and went beyond the experimentation phase, only 15% have established metrics to measure returns of ROI. When measuring ROI, what metrics should be considered, and how should they be done?  

In this blog, I gathered a guide on how to get prepared to measure the ROI of Gen AI effectively before even implementing AI in your business.  

Set clear business objectives

Instead of adopting AI for its novelty, define your goal. According to Boston Consulting Group, 45% of leaders expect AI to generate significant value, and 60% expect more in revenue growth. Most companies I worked with aim for the following metrics:  

• Reduce the cost of operation  

• Improved efficiency in enhancing customer experience  

• Revenue Growth
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According to Gartner, some businesses consider productivity improvements but that may not be the source of differentiation over time. Companies can reinvest AI-driven time savings into higher-value work, such as training or innovation, to see greater long-term ROI.    

Identifying Key Performance Indicators (KPIs) will help to measure success such as operational efficiency, customer satisfaction and sales growth.  

Measure Current Performance Metrics  

It is important before implementing AI, that businesses measure the performance of their current processes that AI will impact, for example, if you’re automating customer service, what is the response time for customer service to resolve a claim and what’s the customer satisfaction rate?  

Estimate the costs of AI

Before implementing AI, evaluate the process and estimate the initial cost such as:  

• Initial investment for cloud infrastructure  

• Hardware and software  

• Data gathering  

• Training/outsourcing  

• How long take AI to deliver tangible results  

• Maintenance and monitoring  

Quantify benefits  

When the AI plan is ready to execute, consider tracking the following metrics:

• Revenue growth 

• Calculate the cost saved through automation

• Labor cost

Additionally, measures the impact of AI on customer experience and the productivity of the team.  

Risk Factors

Similar to any other project AI projects are prone to risk, it is important to take implementation risks into account such as    

• Data quality/availability issues (Impact: 10-30% cost increase)  

• Integration challenges with existing systems  

• Security vulnerabilities and compliance issues  

• Market changes affecting AI value proposition  

• Competitor AI implementations  

• Increased operational costs  

The simple formula to calculate ROI based on the above could be:  

ROI = ((Total benefits – Total Costs) * Risk Factors / Total Investment) *100  

By measuring these factors, you can determine whether AI initiatives are contributing to the business and driving profitability.  

If you need help implementing these metrics for your GenAI project, our team offers AI workshops, custom solution development, and AI agents to help you achieve measurable ROI. Contact us to learn more about how we can help you succeed with GenAI advancements.  

Reference:  

https://www.bcg.com/publications/2024/wheres-value-in-ai  

https://www.gartner.com/en/articles/take-this-view-to-assess-roi-for-generative-ai?utm_source=chatgpt.com  

https://www.cfodive.com/news/companies-roi-metrics-generative-ai-kpmg/730520