A good AI project does not begin with a model or chatbot. It begins with repeated work whose cost, errors and correct outcome can be defined. We test the business hypothesis first, build one narrow end-to-end flow and decide whether to scale only after measurement.
When automation is worth testing
- The team repeatedly copies, classifies, checks or transfers information between systems.
- Service or operations volume grows faster than headcount.
- Documents or unstructured text require judgment, but a person must approve the result.
- Integrations are deterministic except for one content-heavy or exception-heavy step.
- AI experiments exist, but nobody measured full cost per case or the cost of errors.
Tangible outputs
Concrete pilot outputs
Current-state baseline
Case volume, handling time, loaded labor cost, errors and delay consequences.
Process selection
Candidates scored by volume, standardization, data quality, exceptions and error impact.
Flow architecture
Deterministic rules, AI task, integrations, state, retries and audit trail separated into operable layers.
Control model
Uncertain cases stop, risky actions require human approval and every decision is traceable.
Working pilot
One bounded process tested with representative data and agreed quality and cost thresholds.
Scale, revise or stop
A decision based on net benefit, critical errors, review load, operability and full cost.
Working process
How pilot risk is contained
- 01
Measure before automating
Without a cost and quality baseline, neither benefit nor harm can be proven after launch.
- 02
Choose one narrow task
The first flow has explicit input and output plus representative examples.
- 03
Run in shadow mode
The system recommends without acting autonomously until error categories and thresholds are understood.
- 04
Decide from evidence
Evaluate net benefit, critical errors, escalation rate, adoption and the team's ability to operate the system.
Clear responsibility
Not every process is suitable
Not rare chaos
A process that happens a few times a month and changes every time rarely creates enough return.
No unowned critical decisions
Money, legal commitments and rights-affecting actions keep a named human owner and control point.
A demo is not enough
Scale only when benefit exceeds model, infrastructure, maintenance and exception-handling cost.
AI workflow example
Breach.gg content flow: from source to publication
Detection → processing → AI summary → publishingBreach.gg first introduced simpler AI features in 2022. In a separate 2026 rebuild stage, I created an agent-assisted workflow that detects activity, processes content and media, prepares AI summaries with source links and passes the result into the publishing integration.
View case studyFAQ
Frequently asked questions
Which process should be automated first?+
Choose frequent, digital and objectively verifiable work with manageable error consequences. A first candidate should not autonomously move money or create irreversible legal commitments.
Do we need our own AI model?+
Usually not. Test the business hypothesis with a reliable provider, clear data policy and strong control layer first.
How is success measured?+
Time, full cost, task success, critical errors, human escalation and actual use are measured separately. Average accuracy alone is not business value.
Can AI be integrated with existing systems?+
Yes. The AI task is one layer; states, permissions, integrations, retries and audit remain in the deterministic system.