Most AI workflow audits hand you a list of automation ideas. This one tells you what to automate, what to keep human, and what your team can actually maintain after handover. It's a focused audit with a clear delivery scope, run by the same person who would help build it.
Common pain points
Your team has 15 automation ideas from leadership and no way to decide which are worth doing.
Your pilots work in a demo, then break when they hit real edge cases the team can't fix alone.
The AI tools that show ROI in vendor decks turn into shelfware three months in.
Automations that depend on a consultant who knows the prompts and integrations — and that nobody else can troubleshoot.
Workflows where AI should stay out — because the cost of being wrong is higher than the time saved.
Engineering hours quietly absorbed maintaining automations that solve a small problem and create a bigger one.
The audit is a focused engagement with a clear delivery scope. We map every workflow your team is considering for AI against three decisions: automate, keep human, or hand back to the team to maintain themselves. We look at the volume, the cost of getting it wrong, the maintainability after handover, and whether the workflow needs to be redesigned before AI touches it. The deliverable is a prioritised decision map with the workflows ranked, the reasons for each call, and the safety criteria your team can use to stay aligned over time. It's the operational audit your team needs before our full AI workflow automation work, not a replacement for it.
How we work
We map every workflow your team has flagged for AI (and the ones it hasn't but should consider).
For each workflow we make the three-way call — automate, keep human, or hand back to the team — and we score the cost of being wrong and test maintainability.
We close by telling you, for each workflow, what to build, what to redesign, and what to keep manual — with the reasoning behind every call.
What we deliver
Step 1
We map every workflow your team has flagged for AI — and the ones leadership hasn't flagged yet but should be considered. Each gets segmented by volume, current cost, and operational criticality.
Deliverable
A segmented inventory of every workflow flagged for AI, with the ones not yet flagged.
Step 2
For each workflow we make one of three calls: automate it (with scope), keep it human (with reasoning), or hand it back to the team to operate (with criteria). No workflow gets a "maybe later".
Deliverable
A documented automate / keep-human / hand-back decision per workflow, with reasoning.
Step 3
We test every "automate" candidate against your team's ability to maintain it without us — prompts, integrations, exception handling, monitoring. Workflows that fail the maintainability test get demoted.
Deliverable
A maintainability verdict per automate candidate — prompts, integrations, exception handling.
Step 4
We score each workflow by how much it costs the business when the AI is wrong. High-cost-of-being-wrong workflows often stay human even if they look "easy to automate".
Deliverable
A cost-of-being-wrong score per workflow.
Step 5
Some workflows need to be redesigned before AI is added — the same workflow with AI bolted on top usually breaks. We flag the redesigns and scope them.
Deliverable
A list of workflows that need redesign before AI is added, with why.
Step 6
We deliver a build plan with workflows ranked by impact and maintainability, the reasoning behind each call, and explicit safety criteria so your team knows when to stop or roll back.
Deliverable
A prioritised build plan with the reasoning per call and explicit safety criteria.
You stop spending engineering hours on automations that don't pass the maintainability test.
You know which workflows justify AI and which don't — with reasoning your team can defend.
Your team owns the decision map after delivery. No vendor lock-in.
You avoid the workflows where AI's cost of being wrong is higher than the time saved.
You get a plan you can execute in phases, not a vendor-led transformation.
Your "automate later" workflows get retired explicitly instead of haunting the backlog.
A generic AI audit grades you against an AI readiness framework — data maturity, governance, infrastructure. This audit grades each workflow against three concrete decisions: automate, keep human, or hand back to the team. It's operational, not strategic. If you need a strategy framework, this isn't it.
No. The audit decides whether a workflow is worth automating before we touch tool selection. Tool choice depends on what we decide to automate, not the other way around. If the audit ends and a workflow is "automate", tool recommendation is a separate conversation.
For each workflow we automate, we test whether your team can keep it running after handover — fix broken prompts, handle exceptions, update integrations, monitor accuracy. If they can't, we either redesign it so they can, or we demote it from "automate" to "keep human". The audit makes this test explicit.
It doesn't write the automations. It doesn't pick vendors. It doesn't replace strategic AI consulting. And it doesn't pretend every workflow can or should be automated — many should stay human, and we say so.
We do, but they're separate engagements. The audit is the decision map. Implementation is our full AI workflow automation work, which uses the audit's decision map as its starting point. You can stop after the audit and take the plan to your own team or another vendor.
Yes. The audit is built for bilingual EN/ES teams. Decision maps and criteria are delivered in both languages if your team operates in Spanish. The same workflow-level analysis applies regardless of language.
From lead gen to onboarding, support and reporting — we standardise the steps your team repeats by hand and connect the tools they already use.
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Read moreBook a call to scope your AI workflow audit. If the audit leads to implementation, it becomes the entry point to our full AI workflow automation work.
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