Capturing Business Outcomes Through AI KPIs When driving business use cases for AI adoption, it is critical that outcomes are measured through well-defined Key Performance Indicators (KPIs). These KPIs should fall into three broad categories:
1. Direct Business Impact This includes metrics such as revenue impact or time savings. For example, one customer implemented AI in production support. The measurable outcome was a reduction in call resolution duration by 8–10 minutes. This type of KPI is directly observable, monitored, and reportable, making it easier to quantify ROI.
2. Indirect Business Value Certain use cases deliver value that is less immediate to measure. A good example is GitHub AI, which generates code, test cases, and improves software quality. The outcomes here are not captured through a single metric, but instead require tracking over time—such as QA defect rates, production issues, levels of technical debt, and deviations from functional requirements. While the business benefit is clear, KPIs in this category require ongoing data collection across weekly or fortnightly cycles to provide meaningful insight.
3. Adoption & Sentiment Tracking In addition to performance metrics, CIOs should track AI adoption sentiment across the workforce. This can be done through qualitative and quantitative employee surveys, repeated over time, to identify trends in user confidence, productivity, and satisfaction. Combining sentiment with operational KPIs provides a 360° view of AI’s impact on both business outcomes and employee engagement.
Capturing Business Outcomes Through AI KPIs
When driving business use cases for AI adoption, it is critical that outcomes are measured through well-defined Key Performance Indicators (KPIs). These KPIs should fall into three broad categories:
1. Direct Business Impact
This includes metrics such as revenue impact or time savings.
For example, one customer implemented AI in production support. The measurable outcome was a reduction in call resolution duration by 8–10 minutes.
This type of KPI is directly observable, monitored, and reportable, making it easier to quantify ROI.
2. Indirect Business Value
Certain use cases deliver value that is less immediate to measure. A good example is GitHub AI, which generates code, test cases, and improves software quality.
The outcomes here are not captured through a single metric, but instead require tracking over time—such as QA defect rates, production issues, levels of technical debt, and deviations from functional requirements.
While the business benefit is clear, KPIs in this category require ongoing data collection across weekly or fortnightly cycles to provide meaningful insight.
3. Adoption & Sentiment Tracking
In addition to performance metrics, CIOs should track AI adoption sentiment across the workforce.
This can be done through qualitative and quantitative employee surveys, repeated over time, to identify trends in user confidence, productivity, and satisfaction.
Combining sentiment with operational KPIs provides a 360° view of AI’s impact on both business outcomes and employee engagement.