AI FIELD MANUALOPERATIONS, FINANCE
A decision-tree approach to AI-assisted workflow automation that routes the routine cases automatically and keeps a human, and a paper trail, on every exception.
Last reviewed 1 September 2026
An operations lead at a mid-sized company manages a purchase-request approval process: a request comes in, gets checked against budget, routed to the right approver by department and amount, and logged. It's mostly repetitive - the same few routing rules apply to 90% of requests - but it currently runs through a person manually checking each one, which is slow and inconsistent when that person is out.
The instinct is to "automate it with AI": have a model read each incoming request and decide the routing. Done carelessly, this creates a real problem finance will catch immediately - if the AI's routing decision is a black box with no record of why it decided what it decided, the process fails an audit the moment someone asks "why was this $40,000 request approved by a junior manager."
The actual task is not "replace the approver with AI." It's building a workflow where AI handles the mechanical classification and routing, a defined rule (not a model's judgment) makes any decision that needs to be defensible, and every step is logged.
Split the workflow into what needs a deterministic rule and what needs judgment. Amount thresholds, department routing, and budget-category checks are rule-based - a model does not need to "decide" that a $2,000 request from marketing goes to the marketing budget owner, a plain lookup does. Reserve the AI step for the part that's genuinely unstructured: reading a free-text request description and extracting the structured fields (department, category, amount, justification) a deterministic rule can then act on.
Every request gets a full audit trail regardless of path: what the AI extracted, what rule fired, who (human or automatic) approved it, and when. Exceptions - anything the deterministic rule can't confidently classify, or anything above a defined amount - route to a human, always, no matter how confident the AI extraction step was.
Separating extraction (AI, good at turning messy free text into structured fields) from decision (a deterministic rule, auditable and consistent) means the part of the system a regulator or auditor would actually question - "why did this get approved" - is always answerable with a plain rule, never with "the model decided."
Routing anything outside defined confidence or amount bounds to a human by default, rather than only when something looks obviously wrong, is what prevents the classic failure mode of automated approval systems: an edge case the AI extraction got subtly wrong sailing through because nothing was watching for subtly wrong, only for obviously wrong.
The routing decision
Incoming purchase request received
Extraction confidence high AND amount below threshold AND category maps to a defined budget owner
Auto-routed to the mapped approver. Logged: extracted fields, rule fired, timestamp.
Extraction confidence low, OR amount above threshold, OR category ambiguous
Routed to a human reviewer for manual classification. Logged: why it was flagged, reviewer's decision, timestamp.
Amount above a second, higher threshold (regardless of confidence)
Always routed to two-person sign-off, no automatic path exists at this tier. Logged in full.
Building it
Document every routing rule the current human approver actually uses - not the official policy, the actual practice, which are often different. This map is what the deterministic rule engine will encode.
Set the amount below which auto-routing is allowed at all, and the amount above which two-person sign-off is mandatory regardless of what any automated step concludes. These are business decisions finance should set, not defaults picked by whoever builds the workflow.
Use a workflow tool (Zapier or Make, both with AI-step support) to read the incoming free-text request and extract structured fields: department, category, amount, justification summary. This step's only job is turning messy text into clean fields - it does not decide approval.
Feed the extracted fields into a plain rule engine (a lookup table or simple conditional logic in the workflow tool) that applies the mapped thresholds and department routing. No model judgment in this step - if the extracted category doesn't cleanly match a defined budget owner, that itself routes to a human.
Every request writes a record: original text, what was extracted, which rule fired (or that it was flagged for human review and why), who approved it, when. This is the audit trail - build it into the workflow from day one, not bolted on after the first audit asks for it.
For 2-4 weeks, let the automated workflow classify requests without actually acting on them, and compare its routing decisions to what the human approver actually did. Fix systematic misclassifications before the workflow controls anything real.
Workflow automation connecting the request intake, AI extraction step, rule-based routing, and logging.
zapier.com (opens in a new tab)Alternative visual workflow builder with AI-step support for the same pipeline.
www.make.com (opens in a new tab)The extraction step - reading free-text requests and returning structured fields for the rule engine to act on.
claude.ai (opens in a new tab)This design only works if the amount and routing thresholds are actually enforced as hard rules in the workflow tool, not as guidance the AI extraction step is merely told about in a prompt. A model instructed "route anything over $10,000 to two-person sign-off" can still misclassify an amount it misread from messy text - the threshold check needs to happen on the extracted, structured number in a real conditional, not be trusted to the model's own judgment.
Shadow-mode testing catches systematic misclassification patterns; it does not catch every edge case, especially ones rare enough not to appear in a 2-4 week sample. Keep the human-review path easy to reach and actually used, not a formality, for the first few months after launch.
An audit trail that logs "what the AI extracted" is not the same as an audit trail that explains "why the AI extracted it that way" - current LLMs cannot reliably produce a trustworthy account of their own internal reasoning. The workflow's defensibility comes from the deterministic rule that acted on the extraction, not from the model's own explanation of itself.
Advertisement
A 40-person company automates its purchase-request routing while keeping a defensible audit trail.
Manual process mapped: five departments, three budget tiers, current de facto rule of two-person sign-off above roughly $15,000 that wasn't actually written down anywhere.
Thresholds formalized with finance: auto-route under $2,000, human review $2,000-$15,000, mandatory two-person sign-off above $15,000.
Zapier workflow built: incoming request (email or form) → Claude extraction step (department, category, amount, justification) → rule-based routing against the three tiers → logged record on every request.
Shadow mode run for three weeks: automated classification matched the actual human approver's routing on 94% of requests; the 6% mismatch was almost entirely one category ("software subscriptions") the extraction step was under-confident on, which was then added as an explicit keyword rule.
Live cutover, with the human-review and two-person sign-off tiers unchanged from how they worked before automation.
Roughly 70% of requests (the ones under $2,000 with a clear category) now route automatically with a full logged trail; everything above that still reaches a human exactly as before, and finance can answer "why was this approved" for every single request from the log.
Nakoda editorial · last reviewed
This entry describes a workflow Nakoda recommends - it is not a claim about how any named tool behaves in every case, and it is not paid placement. Spotted something out of date? Tell us.
Decision tree
A confidence-threshold approach to AI-assisted invoice data extraction and reconciliation that speeds up the routine matches and routes anything uncertain to a human - never auto-posts on a guess.
Pipeline
A concrete retrieval-augmented workflow for using an LLM to debug a production incident against your actual codebase and logs - not against its training data's vague idea of what your framework does.
