Direct answer
The AutomateFlow methodology starts with the operational workflow, not a tool or AI model. It separates repeatable steps from interpretation work, keeps consequential decisions with people, and defines failure states, ownership, and recovery before launch.
The intended result is not an isolated demonstration. It is a system the operating team can understand, validate, and own.
1. Map the current workflow
We document inputs, people, source systems, state changes, decisions, waiting time, exceptions, and the accepted outcome. We identify where time or context is lost without assuming every step should be automated.
The output is a map precise enough for the team to correct misunderstandings before implementation.
2. Draw responsibility boundaries
Every activity receives a responsibility type:
Swipe or scroll to compare the columns.
| Type | Examples | Required control |
|---|---|---|
| Deterministic rule | validation, deduplication, routing, synchronisation, calculations | tests, idempotency, logs, and retries |
| Bounded AI | extraction, classification, summarisation, research, drafting | sources, thresholds, evaluations, and authority limits |
| Human decision | exceptions, approvals, important messages, commercial changes | visible context, an owner, and a deadline |
Data and permissions are reviewed before a model receives access. The system of record for each field remains explicit.
3. Design the system and its exceptions
We define the necessary interface, integration contracts, workflow states, and failure behaviour. Every consequential step needs an answer to these questions:
- What validates the input?
- Who owns the state?
- What happens on duplication, delay, or unavailability?
- What may AI do, and what may it not do?
- Where does the workflow pause for review?
- How can an operator correct or resume the case?
4. Build a narrow first release
Implementation begins with one valuable and repeatable path. Integrations, rules, and interface are built together so the team does not receive an invisible automation without a control surface.
A prototype can validate interaction, but it is not treated as production until security, failure states, observability, and operating responsibilities are defined.
5. Validate real scenarios
Testing includes the normal path, missing inputs, duplicates, slow responses, unavailable integrations, uncertain AI output, corrections, and retries. Acceptance criteria describe the operational effect and resulting system state, not only the interface appearance.
For AI work, representative examples test both output quality and behaviour outside known cases.
6. Release with control and handover
Rollout can be limited to one group, request type, or service line. We establish owners for alerts, exceptions, accounts, infrastructure, data, and later changes.
Handover includes documentation, access, recovery procedures, known limitations, and the criteria the team will use before expanding the system.
Evidence and claims
A solution blueprint shows how a system could be built; it does not prove a client outcome. A claim about savings, speed, accuracy, or revenue is published only when its definition, period, source, approval, and interpretation limits are available.
How we evaluate whether to build
A workflow is a stronger candidate when it has meaningful volume or coordination cost, identifiable rules and exceptions, an active owner, accessible data, and a verifiable outcome. If the problem is rare, unstable, or safely solved through configuration, the recommendation may be not to build custom software.
Material history
Initial public version or material revision.
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