KPIs for Generative AI

From The Executive's Guide to Generative AI - 

When evaluating projects, consider the feasibility, actionability, affordability, anticipated business value, and ultimate return on investment of each generative AI project.

Commonly used generative AI Key Performance Indicators:

1. Accuracy - Measure the accuracy of the generative AI model in producing relevant and correct outputs. This can be quantified using metrics such as precision, recall, F1 score, or mean squared error, depending on the nature of the use case. 
2. Productivity - Assess the impact of generative AI on the productivity of the target persona or department. This could include metrics like the number of tasks completed per unit of time, response time, or reduction in manual effort required.
3. Customer satisfaction - If the generative AI use case involves customer-facing applications, use customer satisfaction surveys or feedback to gauge how well the AI system meets customer needs and expectations.
4. Cost savings - Measure the cost savings achieved through the use of generative AI. This may involve comparing the costs of employing the AI system to the expenses associated with traditional manual processes or outsourcing.
5. Turnaround time - Evaluate the time taken for the generative AI model to generate responses or outputs compared to traditional methods. Faster turnaround times can  lead to increased efficiency and improved customer experience.
6. Quality of output - Assess the quality of the generative AI outputs against predefined criteria. This can be done through manual review or automated quality checks, depending on the use case.
7. Error rate - Quantify the rate at which the generative AI model produces incorrect or undesirable outputs. Minimizing error rates is crucial for maintaining accuracy and reliability.
8. Business impact - Identify specific business metrics that are directly impacted by the generative AI use case, such as increased sales, reduced customer complaints, or improved employee retention.
9. Training time and cost - Measure the time and resources required to train and fine-tune the generative AI model. Efficient training processes can lead to faster implementation and quicker time-to-value.
10. Human-in-the-loop metrics - If human intervention is involved in the generative AI process, track metrics related to the efficiency and effectiveness of human oversight.
11. Scalability - Assess how well the generative AI model scales to accommodate increased usage or higher demands. Scalability is essential for long-term success.
12. Regulatory compliance - For sensitive domains like healthcare or finance, monitor how well the generative AI system adheres to relevant regulatory requirements and data privacy standards.