Automate high-volume, low-ambiguity approvals that fall under conservative dollar limits. Require role-based authorization, an immutable who/what/why audit trail, human escalation, and a fixed-price handover that keeps decision rules separate from the model. That separation ensures the automation survives model updates and staff turnover.
- Automate only high-volume, rule-based approvals that sit under conservative dollar thresholds.
- Provide role-based authorization and an immutable who/what/why audit trail for every approval.
- Deliver automation as a fixed-price, handoverable project with evidence-based acceptance criteria.
- Expect exceptions, escalations and reconciliation to consume most of the operational effort.
- Separate decision rules from the document-reading model so updates and staff changes don't break approvals.
- Make actions idempotent and run reconciliation jobs to detect and correct mistakes.
Signs this is happening in your business
- Several people spend hours each weekday chasing PO, invoice and expense approvals.
- Approvals stall because the named approver is in meetings and requests are re-routed by email.
- Finance refuses to sign off because they cannot see who approved what and why.
- Small reversals or corrections after approvals because reviewers missed edge cases.
- No single document lists the decision rules; every approver applies their own ad-hoc interpretation.
- Pilot UAT stalls because test data does not include real exceptions.
- Approvals pile up on Mondays and Fridays when approvers are out, creating payment bottlenecks.
If several of these are true at the same time, the problem is usually the process rather than the people running it.
What this is costing you a year
(Team hours per week x hourly cost of employees x 52) = annual payroll loss
- People doing this work4
- Hours each per week6
- Hourly cost of employees$45
- Weeks52
Annual payroll spent on approvals for a four-person team at the stated hours and rate.
Find the number before you spend anything
Run the formula above on your three most repetitive processes. If any of them clears a year of a salary, that is the one to bring to a call.
When to automate routine approvals with AI agents
Automate approvals that repeat, follow explicit rules you can write down, and sit below conservative monetary limits. Start where the work is frequent, predictable and produces a low exception rate. Those cases reduce approver time while giving you a controlled environment to build audit trails, role checks and escalation paths.
- Volume: many identical requests per month rather than occasional ones.
- Dollar threshold: amounts finance is comfortable with for fast automated sign-off.
- Exception rate: low enough that humans rarely intervene.
- Explainability: every decision maps to a line in a readable rulesheet.
- Regulatory risk: exclude checks that carry regulatory penalties or contract obligations.
Which approvals you should automate first
Pick approvals that follow a firm decision tree and deliver the biggest time savings. Look for cases where inputs are structured—supplier, amount, GL code and a receipt match—and where a missing document simply rejects the request. Keep a human ready for exceptions and escalation.
- Repeatable decisions driven by a small set of structured inputs.
- Cases where absence of a document leads to automatic rejection, not judgement.
- Approvals with short, auditable rules in a one-page decision matrix.
Non-negotiable controls and evidence required
Finance accepts automation only when you can show who approved what, when, and why. Build role-based authorization, an immutable audit log, human-readable reasons for each automated decision, and configurable human escalation into your acceptance criteria and deployment plan.
- Role-based authorization mapping: exact approvals each signer role may make and the conditions.
- Immutable audit log: timestamped record with input snapshot, rule cited, and actor label.
- Explainability: a human-readable reason for every automated decision that cites a rule.
- Human-in-the-loop escalation: thresholds that force human review near boundaries or on exceptions.
- Non-repudiation: digital records that make the recorded system decision traceable and verifiable.
How to architect automation so it survives model changes and staff turnover
Design for change by separating decision rules from the model that reads documents. Keep rules as the source of truth, treat the model as an interchangeable reader, make actions idempotent, and add reconciliation jobs to detect and correct mistakes. That separation prevents model updates or staff churn from breaking approvals.
- Direct connectors to accounting, expense and PO systems so approvals update automatically.
- Decision layer stored in a human-editable format—tables or a rulesheet—in your document store.
- Idempotent actions: the same approval can be applied safely multiple times without duplication.
- Nightly reconciliation jobs that compare automated approvals against system records and flag mismatches.
- A documented routine that captures and stores the evidence snapshot used for each decision.
If you automate a broken process you will bake the inefficiency into future headcount.
Fixed-price project scope and acceptance checklist you can reuse
Use a fixed-price build that hands over an owned system and clear acceptance evidence. The package should include discovery, a representative test dataset, the automation build, UAT, and a documented handover with a warranty period. Make acceptance about verifiable evidence, not speculative promises.
- Discovery deliverables: mapped process, approval matrix, exception examples, and a signed decision rulesheet.
- Test dataset: a representative set of anonymised requests covering approvals and exceptions for UAT.
- Build deliverables: connectors to systems, the decision layer in editable format, the model reader configured, and the reconciliation job.
- UAT and success metrics: automation accuracy on the test dataset, exception routing working, and audit logs verified.
- Handover artifacts: runbook, source of truth for decision rules, training for approvers, and change log.
- Warranty: a defined warranty period with agreed SLAs on incident response and fixes.
What acceptance looks like in practice
Acceptance is practical and repeatable. Finance should run three checks: inspect the immutable audit log, replay sample approvals from the test dataset showing the rule applied, and trigger an escalation to verify the human workflow. If any check fails, the project is not accepted.
- Audit trail contains who/what/why and cannot be edited after the fact.
- Sample replay of approvals with input snapshots and human-readable reasons.
- A triggered escalation that reaches the named approver and creates a traceable decision.
How this fails in practice, and the day it goes wrong
Failures follow a predictable script: scope is cut, the audit is skipped, and a broken process is automated. The day it goes wrong is when a rare exception passes, money moves, and nobody can trace or undo the action quickly. That sequence destroys trust and forces a rollback.
- Rushed discovery omits edge cases and rulesheet entries.
- No representative test dataset lets blind spots slip into production.
- No reconciliation job means incorrect actions go uncaught until a human spot-check months later.
- No named owner after handover leaves the system unmanaged when the model drifts.
Operational monitoring and governance you must put in place
Put governance in place before go-live. You need drift detection, a revalidation cadence, an incident playbook, and a named owner with budget authority. Monitoring must be actionable: alerts and weekly reports that trigger human review when rules stop matching incoming requests.
- Drift detection: alerts when the model's behaviour changes or when exceptions rise suddenly.
- Regular revalidation: sample recent automated approvals and verify the decision rules still apply.
- Incident playbook: documented steps to pause automation, reassign approvals to humans, and run reconciliation.
- Named owner: an operations or finance person with responsibility and budget for maintenance and updates.
Finance will not accept automation that cannot produce an immutable who/what/why record on demand.
Identify your biggest operational bottleneck
Most owners already know which process is the problem. What they do not have is the annual figure attached to it. That is a 15-minute conversation, not a project.
Vendor and technology tradeoffs you need to choose between
Choose between agent-capable models that can read, reason and act, and rule-based automation with document readers. I favour a hybrid: rules decide, models read. Match the route to your appetite for vendor management and your tolerance for occasional model behaviour changes.
- Agent-capable models: handle messy, free-text inputs but require regular revalidation and monitoring for behaviour changes.
- Rules plus robotic automation: predictable, easier to audit, and cheaper to maintain for structured approvals.
- Retrieval-assisted references: useful when decisions need external documents; treat them as a reference library the system checks, not a replacement for hard rules.
A step-by-step first project you can buy for a fixed price
Buy a single approval type end-to-end under a fixed fee. A narrow scope forces early decisions on rules and acceptance and makes delivery predictable. My standard quote covers discovery, data collection, build, UAT, a controlled pilot, handover artifacts, warranty support and a revalidation checkpoint after handover.
- 1. Discovery workshop and rulesheet sign-off.
- 2. Data collection: anonymised examples and exceptions.
- 3. Build: connectors, decision layer, reader configuration and reconciliation job.
- 4. UAT on the test dataset until acceptance criteria are met.
- 5. Pilot live for a controlled sample of requests.
- 6. Handover: runbook, training and decision rule source of truth.
- 7. Warranty support and a revalidation checkpoint after handover.
Where most projects go wrong: three real mistakes
I see the same avoidable mistakes: automating a broken process, skipping a representative test dataset, and omitting reconciliation. Each has a clear fix: correct the process first, collect real examples for UAT, and run reconciliation jobs to catch errors early.
- Mistake: Automating a broken process. Fix: correct the process and freeze rules before automation.
- Mistake: No test dataset. Fix: collect and anonymise real requests for realistic UAT.
- Mistake: No reconciliation. Fix: schedule nightly reconciliation to surface mismatches early.
The metrics you must measure from day one
Measure operational metrics you can act on. Keep the dashboard short and operational: coverage, exceptions, speed, reconciliation mismatches and audit completeness. Use those to trigger rule updates, revalidation and incident responses, not vanity reports.
- Automation coverage: percent of eligible requests handled automatically.
- Exception rate: percent of eligible requests routed to humans.
- Mean approval time: time from request creation to final approval.
- Reconciliation mismatches: nightly count of mismatches requiring correction.
- Audit completeness: percent of approvals with a complete who/what/why record.
How to price and budget for implementation and ongoing maintenance
Treat implementation as a fixed upfront cost and maintenance as an ongoing line item for rule updates, revalidation and incident handling. Price the build narrowly and budget for a part-time owner who does weekly monitoring and periodic revalidation. Plan a small contingency for policy-driven rule changes.
- Fixed-price build for one approval workflow, including discovery and warranty.
- Ongoing budget for a part-time owner: weekly monitoring and periodic revalidation.
- Contingency budget for rule changes when business policy updates occur.
Next action: map one approval and get a fixed-price plan
Map the single approval that costs you most in hours and get a fixed-price plan. A short scoping call produces a priced SOW you can sign. Start with one narrowly defined workflow, prove it, then hand it off as an internal product. See how I work on Services and book a call on How to work together.
- A focused scope makes the project deliverable, testable and handoverable.
- Handover must include an editable decision rulesheet and a named owner.
Where businesses waste the most time
- Re-keying invoice dataWastes hours and introduces transcription errorsMake the system read the invoice and fill the fields; keep your accounting software as the single source of truthA person still checks: A person spot-checks flagged invoices and exceptions
- Chasing approversCreates unpredictable delays and pile-ups on certain daysAutomate routing and reminders so requests arrive in the approver's normal queue rather than emailA person still checks: Approvers still confirm edge-case approvals when escalated
- Manual reconciliationCorrections happen weeks later, creating accounting noiseRun an automated nightly compare between approvals and the ledger and surface mismatches in a simple reportA person still checks: An operations person resolves mismatch reports each morning
How this compares with what you already run
| Operational area | The manual way | The automated way | Annual business impact |
|---|---|---|---|
| Invoice approvals | People re-key invoices and chase approvers by email | System reads invoices, applies rules, and auto-approves routine ones | Frees up hours across the finance team each year |
| Expense claims | Employees submit receipts and finance checks dozens manually | Routine compliant claims auto-clear; exceptions route to humans | Reduces cycle time and late payments annually |
| PO approvals | Manual sign-off for repeat suppliers causes delays | Known suppliers and amounts auto-approved under thresholds | Fewer stuck POs and faster purchasing |
| Subscription renewals | Renewals reviewed individually and missed in calendars | Automated checks trigger renewals or flag for review | Avoids missed renewals and late fees |
| Ad-hoc supplier onboarding | Forms emailed and documents validated manually | System extracts key data and flags missing items; humans approve final | Faster onboarding and earlier transactions |
| Reconciliation | Monthly manual matching of approvals to payments | Nightly reconciliation surfaces mismatches for review | Fewer late corrections and rework |
What the end-to-end workflow looks like
- Invoice arrivalInvoice arrives and is captured into your system automaticallyAutomation reads the invoice and extracts key fields
- Rule matchingExtracted fields are compared to the decision rulesheetAutomation determines whether the invoice qualifies for auto-approval
- Authorization checkSystem checks the mapped approver roles and thresholds for validityAutomation enforces role-based authorization
- Approval actionIf the invoice fits the rules, it is approved and recorded; otherwise it is escalatedAutomation executes the approval action in the accounting system
- ReconciliationNightly job compares approvals to ledger records and flags mismatchesAutomation produces a mismatch report for review
- Human review and exceptionsA human resolves mismatches or handles escalated approvalsStays human
The metrics that tell you it worked
| Metric | What it tells you |
|---|---|
| Automation coverage | Percent of eligible requests handled without human intervention; measures scope of automation |
| Exception rate | Percent of handled requests routed to humans; indicates rule adequacy |
| Mean approval time | Average time from request submission to final approval; shows speed improvements |
| Reconciliation mismatches | Count of nightly mismatches requiring manual correction; signals correctness of automated actions |
| Audit completeness | Percent of approvals with a complete who/what/why record; measures audit readiness |
| Incidents per quarter | Number of incidents that required pausing automation; tracks operational risk |
| Time reclaimed | Hours freed across the team per week by automation; translates into payroll savings |
When this is the wrong move
- When decisions carry regulatory penalties or legal obligations requiring human legal sign-off
- When the decision relies on nuanced contract interpretation or complex negotiation
- When the decision rules cannot be written down unambiguously or exceptions are frequent
- When you cannot produce a representative test dataset for UAT
- When you do not have a named owner with budget to operate and revalidate the automation
The mistakes that cost the most
- Automating a broken processThe team automates the current workflow with its workarounds; the system enforces poor practice, exceptions explode, and the pilot is rolled back.
- Skipping the audit trailLogs are partial or editable. An error occurs and nobody can prove what the system did, so finance disables the automation.
- No reconciliationSmall mismatches are ignored until they accumulate into a month-end correction that erodes trust in the system.
Frequently asked questions
Can I fully automate all approvals?
No. Automate only high-volume, low-ambiguity approvals under conservative dollar thresholds. Complex or high-risk approvals must stay human or use a human-in-the-loop; automation should reduce volume and preserve necessary oversight.
What if finance refuses to trust the automated approvals?
Give them an immutable audit trail and replayable evidence. Let finance run the acceptance checks: audit log inspection, replayed approvals from the test dataset, and a triggered escalation. When they can inspect the evidence, trust follows.
How much ongoing work will this need?
Plan for a part-time owner who spends weekly time on monitoring and periodic revalidation. Expect occasional rule updates when policies change; the goal is predictable maintenance, not zero maintenance.
Which is cheaper long term: rules or models?
Rules are cheaper and more predictable for structured approvals; models handle messy inputs better. A hybrid—rules for decision logic and models for reading messy documents—usually balances cost and reliability.
How do I get a fixed-price plan for one workflow?
Map the workflow, gather representative requests, and book a scoping call. I offer a fixed-price build and handover plan for one workflow; see How to work together for the next step.
Stop paying salary for robotic work
Bring your messiest process to a 15-minute audit and we will map where the hours actually go, live on screen. You leave with the number whether or not you work with me.