AI Workflow Automation Consulting
Put GenAI on the steps that need judgement over documents and language — and leave deterministic clicks to RPA — with one eval suite and a human fallback.
- Service
- AI Automation
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
AI workflow automation consulting maps a business process, assigns each step to RPA, a GenAI agent, or a person, then builds the first production path with scoped tools, an eval suite, shadow mode before system-of-record writes, and a human fallback. It is not “replace RPA with an LLM.” A standard engagement is four weeks in your cloud, and you own the process map, tools, evals, and runbook.
Why teams pick this engagement
AI Automation × EnterpriseGenAI vs RPA on purpose
RPA wins on stable, high-volume, deterministic clicks. Agents win on language, documents, and branching judgement. Mixing them up is how programmes stall or invent risk.
The process is the product
We map the existing workflow, mark which steps retrieve, which decide, and which write, then automate only the steps with a written policy and a checkable outcome.
Agents act with scoped tools
Each write is a named tool with the narrowest credentials that can do the job. Shadow mode then write access. Irreversible steps keep a human confirmation.
People on exceptions, not on every item
The happy path can run. Exceptions, low-confidence, and high-stakes items land in the same queue your team already works, with the trace attached.
Four-week first workflow
One process, one success metric, environments, shadow-mode pilot, handover. The next workflow reuses tools, identity, and evals.
You own the automation
Process map, tool schemas, eval suite, and runbooks are yours. Work runs in your perimeter. Model-agnostic. No token markup on inference.
Key takeaways
- 01
RPA is still the right tool for deterministic, high-volume, stable clicks. GenAI earns its place on language, documents, and judgement that does not fit a branch.
- 02
Start with one process that already has a written policy and a numeric success metric. Undocumented tribal knowledge becomes confident errors at scale.
- 03
Agents act with scoped tools. System-of-record writes stay off until shadow mode holds on live items.
- 04
Exceptions need a designed queue. Automation that dead-ends without a person is how “straight-through” dashboards hide a pile of stuck work.
- 05
You own the map, schemas, eval suite, and runbooks. Work stays in your perimeter. You pay the model provider; we add no token markup.
What the engagement covers
How we work
- 01
Discover
Week one: one process, step classification, metric, systems, golden items.
- 02
Design
Hybrid path, tool scopes, write gates, exception queue, eval plan.
- 03
Build
Agent, RPA hooks, traces in your perimeter with weekly item demos.
- 04
Validate
Shadow mode on live items; system-of-record writes stay off until evals hold.
- 05
Enable
Handover of map, evals, and runbooks; 30 days on-call included.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
GenAI vs RPA Step Classifier
A step-level rubric: deterministic click, document judgement, or human-only. Use it in discovery so the first build is not an RPA bot wearing a model.
Get the classifier ·Workflow Write-Gate Worksheet
List each system-of-record write, the credential, the confirmation rule, and the eval that has to hold before the gate opens.
Get the worksheet ·