GUIDE 01 / OPPORTUNITY DISCOVERY
How to identify an AI workflow worth implementing
The strongest first use cases create visible operational value through a clear and manageable scope. They are frequent, expensive enough to matter, understandable, measurable, and bounded by rules that teams can explain.
- Choose a workflow with a named owner and a visible backlog.
- Measure volume, touch time, waiting time, rework, exceptions, and error cost.
- Separate routine execution from judgment that should remain human.
- Confirm that required data and system access are realistically available.
- Define a baseline before discussing projected improvement.
GUIDE 02 / PROCESS MAPPING
Map exceptions before designing the happy path
A clean diagram of the normal process is rarely enough. Operational cost and risk tend to accumulate in exceptions: missing data, conflicting records, approval delays, unusual transactions, and unclear ownership.
- Capture the trigger, inputs, systems, actions, decisions, and outputs.
- Ask what causes the process to stop or leave its normal path.
- Document who resolves each exception and what evidence they use.
- Identify irreversible actions and materiality thresholds.
- Design escalation and recovery before automation.
GUIDE 03 / PRODUCTION READINESS
The difference between an AI demo and an operating system
A demo proves that a model can produce an output. Production requires dependable inputs, authenticated system access, permissions, monitoring, exception handling, approvals, auditability, support, and ownership.
- Test representative cases and failure conditions.
- Define who owns the workflow after launch.
- Log what happened, why it happened, and what source was used.
- Establish how changes are reviewed and released.
- Measure reliability and business outcomes continuously.