AI should only enter when the process can explain the result.
The method exists to avoid two common mistakes: implementing a tool before the problem and publishing results without evidence. We start with process, baseline, risk and owner. Then we decide technology.
Each stage leaves an artifact that can be reviewed before moving forward.
Do sinal operacional ao sistema em uso.
A visualização 3D carrega como aprimoramento progressivo. O método permanece legível nos cards e nesta versão estática.
Separate symptom, process and decision
Measure the current state before proposing a solution
Choose technology by the real constraint
Put it into routine with review and trace
Separate symptom, process and decision
The first conversation identifies where operations lose time, where the decision becomes fragile and which systems already participate in the workflow.
described process, initial owner and value hypothesis
Measure the current state before proposing a solution
Without baseline, any improvement becomes opinion. We record volume, time, rework, exceptions, error cost and source quality.
comparison metric and success criterion
Choose technology by the real constraint
AI, software, agent, automation or governance enter by fit to risk, data, latency, maintenance and team capacity.
technical scope with boundary, fallback and integration
Put it into routine with review and trace
Delivery only counts when someone uses it, reviews it and can explain what happened afterwards. Logging and supervision are born with the workflow.
system in use, review point and next cycle
What stays documented
- Opportunity map
- process, pain, owner, data, risk and expected value
- Decision matrix
- when to use AI, software, automation, agent or governance
- Implementation spec
- scope, integration, fallback, logs and acceptance criteria
- Review plan
- metric, owner, cadence and stop condition
The method also defines what does not enter.
AI does not enter as the default answer
When rule, integration or interface solves better, the project follows software without forcing a model.
Sensitive action requires authority
Pricing, credit, compliance, human risk or material impact require review, limit and identifiable owner.
Public metric needs baseline
Without previous data and honest comparison, the result stays as internal learning, not commercial proof.
Questions about the method
Criteria to know when AI helps, when it gets in the way and when a result can be called evidence.
Does the method start with technology or process?
It starts with process. Technology enters only after we understand workflow, decision, available data, risk, owner and success criterion.
How do you decide whether AI is necessary?
We compare value, risk and maintenance. If a rule, integration or interface solves better, we do not use AI as technical decoration.
What makes a delivery auditable?
Input, output, source, executed action, owner, exceptions and review point must be logged according to workflow risk.
When can a case be published?
Only when there is baseline, verifiable result, authorization or adequate anonymization and no inflated metric.
Does the method work for companies without high data maturity?
Yes, as long as there is a concrete process. When data is dispersed, the first cycle organizes source, quality and usage criteria.