AI Discovery and Effectiveness
Select AI use cases based on how work is performed, then measure the results against the same baseline.
Before Deployment
AI Discovery
Identify where AI agents are appropriate.
Discovery mines real execution patterns, scores each one for automation readiness, and separates work suited to rules-based automation from work where AI augments human judgment.
- Automation readiness scored on frequency, complexity, data structure and scope
- Automation separated from augmentation
- Impact quantified before anything is built
After Deployment
AI Effectiveness
Measure AI ROI with execution data instead of surveys.
Effectiveness compares adopters with non-adopters on the same work, and measures speed, accuracy and throughput from activity recorded in the application rather than self-reported adoption.
- Adopter versus non-adopter analysis on the same work
- Measured from execution data rather than self-reported usage
- Extends to agents and hybrid human-agent work
Discovery and Measurement Cycle
Pyze does not run your agents. It identifies where to deploy them and measures their return.
Select AI use cases from measured patterns.
Build and run agents in your own stack.
Compare outcomes against the baseline.
Use results to prioritize the next round.
Plan and Measure AI Deployments
Identify AI use cases before deployment and measure the return afterward.