Current-state workflow map
Actors, systems, inputs, outputs, delays, rework, decision points, and the manual process that remains available as fallback.
Controlled AI Workflow Automation
Automate one important workflow without losing oversight, evidence, or a safe fallback.
A bounded design-and-implementation engagement for teams that want practical AI assistance, but also need the workflow to remain understandable, testable, reviewable, and maintainable.
Fixed-scope pilotOne bounded workflowHuman review and fallbackPricing confirmed after diagnostic
Why this exists
Useful automation needs more than a model connected to a form.
Many workflow projects begin with a tool and search for a problem. This engagement begins with the work itself: where time is lost, which decisions are repeatable, what information is authoritative, which errors matter, and where a person must remain accountable.
The result may combine deterministic rules, APIs, databases, retrieval, and AI. AI is used only where it adds enough value to justify its uncertainty and operating burden.
Strong fit when
- The workflow is repetitive, document-heavy, or information-heavy
- A knowledgeable person can review outputs and define unacceptable errors
- Current effort, delay, rework, or quality can be measured
- The workflow can retain a manual fallback while the pilot is evaluated
Controlled implementation
A working pilot with an operating record
The engagement is designed to leave the team with a functioning bounded workflow and enough evidence to understand how it should be used.
Automation suitability assessment
Separate steps that belong to deterministic automation, AI assistance, human judgment, or deliberate non-automation.
Target workflow and control design
AI and human responsibilities, approved sources, permissions, review gates, exception states, escalation routes, and recovery behaviour.
Working bounded pilot
A functional implementation using the smallest practical combination of scripts, APIs, databases, workflow tools, retrieval, and models.
Acceptance and failure tests
Expected cases, missing or malformed inputs, conflicting sources, unsupported outputs, duplicate processing, tool failures, overrides, and rollback.
Handover and operating package
System map, operating instructions, configuration record, test results, known limitations, ownership, maintenance needs, and reassessment triggers.
Quality standard
The workflow should leave enough evidence to explain what happened.
Where appropriate, the pilot retains the input or source reference, workflow version, model or rule configuration, output, exceptions, reviewer action, override, and final disposition. The required record is proportional to the decision and risk rather than copied from a generic compliance checklist.
Every pilot should define
- What the automation is allowed to do
- When a human must review, approve, or intervene
- How missing, conflicting, or out-of-scope information is handled
- How the team returns to a manual process when the system is unavailable or uncertain
Scope boundary
A pilot, not an open-ended transformation program
The engagement remains commercially workable by freezing one workflow, a limited system boundary, and explicit acceptance criteria.
Normally included
- One workflow with a defined start and end
- One primary workflow owner and a limited stakeholder group
- A limited set of approved systems and sources
- One pilot implementation and acceptance-test package
- Handover documentation and a defined review period
Excluded unless separately quoted
- Company-wide AI strategy or multi-department transformation
- Large custom applications or extensive user-interface development
- Formal computer-system validation, legal advice, or security certification
- High-risk autonomous decisions without meaningful human control
- Unlimited integrations, revisions, training, or ongoing support
Review or build?
Design sprint first, or build with controls from the start?
Workflow Evidence Hardening is a paid design sprint for an existing workflow: map the evidence chain, register gaps, recommend controls, and plan hardening, without production implementation inside that fee. Controlled AI Workflow Automation is a separately scoped design-and-implementation pilot for a new or revised bounded workflow with those controls built in.
When the current process is poorly defined or carries unresolved record-chain risk, the design sprint is usually the correct first step.
Inquiry outcomes may include
Proceed with a pilot: the workflow is bounded, valuable, and reviewable.
Clean up the process first: ownership, source data, or exceptions are too unstable to automate responsibly.
Use conventional automation: rules, lookups, scripts, or database changes can solve the problem more reliably than AI.
Do not automate: the risk, ambiguity, or maintenance burden exceeds the likely value.
Bring one repetitive workflow, not a request to “add AI.”
A short inquiry tests fit. Free structured diagnostics stay on the one-workflow evidence path; automation is separately scoped if justified.