Reduce manual drag without removing human judgment.

• The same data is copied between inboxes, documents, spreadsheets, and systems.
• Approvals and handoffs wait because status or ownership is unclear.
• One person knows how exceptions work, but the process is not inspectable.
• Existing automation completes the happy path and hides failures.
• AI or a new platform is being considered before the operating decision is defined.
• The team cannot see cycle time, rework, exceptions, or adoption consistently.

1. Observe the current path. Trace a real work item across people, tools, decisions, waits, repeated entry, and exception handling.
2. Define the states. Name the trigger, owner, inputs, approvals, completion condition, and route when the expected path fails.
3. Simplify before connecting. Remove unnecessary steps and resolve disputed rules before software makes them faster.
4. Automate the predictable handoffs. Use native rules, focused integrations, or custom work only where each adds enough control to justify its burden.
5. Test exceptions and recovery. Exercise retries, duplicates, timeouts, permissions, partial failures, and the manual path.
6. Hand off an owned system. Document access, decisions, monitoring, maintenance, recovery, and the person accountable after release.

• New-client onboarding, document requests, approvals, and internal assignment.
• Proposal preparation, approval, delivery, decision status, and project handoff.
• Content or campaign review, evidence checks, release gates, and update cycles.
• Project status, recurring reporting, exception queues, and record reconciliation.
• Lead intake and response when the recurring path begins with a prospective customer inquiry.

These are representative workflow categories, not claims about a named client or a guaranteed outcome.

Rules are useful when the decision is narrow, repeatable, observable, and recoverable. Human review should remain where the work depends on fit, sensitive context, unusual exceptions, commercial commitments, or consequences a machine should not own.

Generative AI may assist with classification, extraction, or a draft when the source, confidence, permissions, review, and fallback are defined. It should not be presented as certain, quietly make sensitive decisions, or impersonate a person.

• Current-state workflow and friction map.
• Target-state states, decision rules, owners, permissions, and exceptions.
• Tool and integration decision with explicit reasons and rejected alternatives.
• Implemented automations, data mappings, credential storage, and access controls.
• Tests for expected behavior, retries, duplicates, partial failure, and manual recovery.
• Monitoring, exception queue, runbook, access inventory, and maintenance ownership.
• A measurement plan for cycle time, manual touches, exceptions, rework, and adoption.

Inspect Field Theory's own Field Note release workflow for a clearly labeled owned-system example.

Which workflow should be automated first?

Start with a repeated, consequential path whose trigger, states, owner, exceptions, and definition of done can be made explicit. High volume alone is not enough if the process is still disputed or unstable.

Can the business keep its current software?

Often. Field Theory begins with the operating requirement, then tests whether existing native rules, a focused integration, or custom work is justified. Replacement is not assumed.

Which decisions should stay human?

Judgment about fit, exceptions, sensitive customer context, commitments, and consequential approvals should stay with an accountable person unless a narrower rule is both appropriate and explicitly authorized.

Who owns and maintains the finished workflow?

Ownership, access, documentation, monitoring, and maintenance responsibility are defined in scope. The client should retain the production accounts, source, operating records, and recovery instructions included in the engagement.

APIs change, credentials expire, rules evolve, and people use systems differently than diagrams predict. Field Theory can implement and test what is in scope, make failures visible, and define ownership. It does not promise zero errors, universal compatibility, fixed savings, or an automation that never needs maintenance.