Rules-based automation
Structured inputs and logic you can write down. Fast, cheap and fully predictable - no AI needed.
- Choose when
- Inputs come from forms or systems, not people's writing.
AI workflow automation services for the processes your team repeats every day
AI business process automation for inbound documents, requests and approvals: a model does the reading and sorting, rules check the result, people handle the exceptions, and everything is written to the systems you already use.
- inputs
- Documents, requests
- exceptions
- Go to a person
- output
- Written to your systems
- Inbox: Receivedinvoices@ inbox · PDF attached · 09:12- done
- AI model: Extracted · 9 fieldssupplier, totals, VAT, lines, PO ref- done
- System: Matched to PO-5521quantities and prices agree- match
- ApprovalPerson decides: Above the 5,000 approval limitassigned to the operations manager
- Action: Post to accountingafter approval- next
Exception queue2 items
- Approval: INV-91 · scan unreadableconfidence below threshold- check
- Approval: INV-93 · no PO foundsupplier not in ERP- check
Example · fictional data
01 / What changes
The same process, before and after AI automation
Rule-based automation stops at the first email that doesn't match the template. AI process automation copes with real inputs - different layouts, free text, attachments - and knows when to ask a person. For fixed-rule processes that need no AI, see business process automation.
Definition · AI workflow automation
Automating a business process in which some steps need judgement that fixed rules cannot provide - reading a document, classifying a request, drafting a reply - with a model handling those steps, rules and integrations handling the rest, and people deciding where stakes or uncertainty are high.
Today: supplier invoices by hand
manual- Someone opens each email and downloads the attachment
- Fields are typed into the accounting system
- The PO is looked up in another screen to compare
- Approvals are chased by email and chat
- Mismatches are found at month end
With AI workflow automation
automated- Attachments are read and fields extracted on arrival
- Each invoice is matched to its PO in the ERP automatically
- Clean matches below the limit go straight to posting
- Approvals are requested with the evidence attached
- Unclear items wait in an exception queue, with the reason
02 / Where to start
Processes worth automating with AI
The best candidates are frequent, rule-heavy at the end but messy at the start, and annoying enough that people already work around them.
Not sure which process to pick first? AI consulting includes a ranked list of candidates.
- 01
Document intake
Invoices, orders, claims, applications and certificates read and entered into your systems. Good fit when documents vary in layout but the fields you need are known.
- 02
Request triage
Emails, tickets and form submissions classified, prioritised and routed with a draft response. Good fit when a shared inbox is sorted by hand.
- 03
Data entry between systems
Information copied from one tool into another, with checks on the way. Good fit when the same data is typed twice.
- 04
Approval preparation
Evidence collected and summarised so the approver decides in one screen. Good fit when approvals stall waiting for context.
- 05
Checks and reconciliation
Records compared across systems with mismatches explained in plain language. Good fit when errors are found late.
- 06
Report assembly
Figures gathered from several sources with a written summary of what changed. Good fit when someone builds the same report every week.
03 / How it runs
How an AI automation handles each item
Six steps, the same for invoices, requests or applications. Only the "understand" step uses a model; everything else is ordinary, testable software.
- 1 · Trigger
Item arrives
An email, an uploaded file, a form or a record change starts the workflow.
- 2 · Understand
AI reads it
The model extracts fields, classifies the item and states its confidence.
- 3 · Validate
Rules check it
Values are compared with your systems: does the PO exist, do totals agree?
- 4 · Decide
Auto or person
Clean, confident, below-limit items continue; the rest go to a person.
- 5 · Act
Write back
Records are created or updated through the systems' APIs.
- 6 · Log
Record it
Input, extracted values, checks and decisions are stored for audit.
Exception queue. Anything the AI is unsure about, or that fails a check, waits here with the reason and the AI's suggestion. The person's decision is recorded and used to improve the workflow - the automation never guesses to keep the line moving.
04 / Choosing the approach
Rules, AI-assisted automation or an agent?
AI automation solutions are not always the answer. Whether you work with us or another AI automation agency, the first question should be whether a model is needed at all - we recommend the simplest approach that handles your real inputs.
AI-assisted automation
A fixed process where some steps need reading or judgement. The focus of this page.
- Choose when
- Inputs vary, but the steps after understanding them are known.
Agentic workflow automation
The path itself depends on what is found along the way. See AI agent development.
- Choose when
- Each case needs investigation, not just processing.
05 / Integrations
Connected to the systems where the work already lives
Automations read from and write to your existing tools through their APIs. Where a system has no API, we agree a safe alternative during discovery. For AI inside your own software, see AI integration services.
Sources
- Inbox: Email inboxrequests, invoices, forms
- Document: Document storagePDFs, scans, contracts
- Channel: Web forms & portalsstructured submissions
AI automationwhat we build
- AI model: Readextract and classify- AI
- Rule check: Checkrules against your systems
- Approval: Approvalsexceptions and items above limits- person
- Log: Audit logsource file, values, decisions
Destinations
- System: CRMaccounts, cases, contacts
- System: ERP / accountingorders, invoices, postings
- System: Helpdesk & chattickets, notifications
Example · fictional data
06 / Control
Controls that keep people in charge
Confidence thresholds
- Set per field and per decision
- Tuned on pilot data
Exception queue
- Reason and suggestion shown
- Decisions fed back
Approval limits
- By amount, type or customer
- Same rules as today, enforced
Audit log
- Source file, values, checks
- Who approved, when
Fallbacks
- Manual path if a system is down
- Nothing lost, nothing duplicated
Change process
- Rule and prompt changes tested
- Versioned and reversible
07 / Results
What we measure, from the pilot onwards
We agree the measures and capture a baseline before automating, so the result is a comparison rather than an impression.
m1 · time
Cycle time
From arrival to completion, per item and per type.
m2 · share
Straight-through share
Items completed without anyone re-keying data.
m3 · exceptions
Exception rate
Items sent to people, and why - the main tuning signal.
m4 · quality
Error rate
Corrections found after completion, compared with the manual baseline.
08 / How a project runs
One workflow first, measured, then the next
Our AI automation services start with a single workflow because it gives measured results quickly, and those numbers decide what comes next.
- stage
Process mapping
Walk through the process with the people who do it; collect samples, edge cases and the baseline.
out →Process map, baseline
- stage
Feasibility on samples
Extraction and classification tested on your documents before any build commitment.
out →Accuracy by field, go/no-go
- stage
First workflow pilot
End-to-end automation with exception queue, approvals and logs, used by one team.
out →Working pilot
- stage
Rollout
Thresholds tuned on pilot data, more input types and users added.
out →Production workflow
- stage
Monitoring & tuning
Dashboards on volumes, exceptions and errors; changes made under version control.
out →Runbook, next candidates
09 / Small teams
AI automation for small businesses
Smaller teams often benefit most from AI workflow automation services, because one person loses hours to the same inbox. The approach is the same, scaled down: one workflow, the tools you already pay for, and a clear owner. If a platform tool such as Zapier or Make already covers your case, we'll say so.
10 / Questions
Frequently asked questions
What is AI workflow automation?
It is automation of a business process in which some steps need judgement that fixed rules cannot provide - reading an unstructured document, classifying a request, drafting a reply. A model handles those steps, ordinary rules and system integrations handle the rest, and a person decides wherever the stakes or the uncertainty are high.
What are examples of AI-automated workflows?
Supplier invoices read from email, matched to purchase orders and posted after approval; customer requests classified and routed with a drafted reply; contracts checked against a list of required clauses; onboarding documents verified and entered into the HR or CRM system; weekly reports assembled from several sources with a written summary.
What is the difference between automation and AI?
Classic automation follows rules you define: if a field equals X, do Y. AI handles inputs those rules cannot, such as free text, scanned documents or ambiguous requests, and returns a result with a level of confidence. Good AI business process automation uses both - rules wherever they work, AI only for the steps that need it.
Will AI replace RPA?
Not entirely. Robotic process automation is still a reasonable way to drive systems that have no API. AI removes much of RPA's brittleness on the input side, because it can read documents and messages that vary. In practice we prefer API integrations, use AI for understanding inputs, and keep screen automation only where no better interface exists.
Should we use n8n, Make or Zapier, or build a custom automation?
Platform tools are a good choice for simple flows between popular apps, and we will say so. Custom automation is worth it when the process needs strict access control, complex validation against your systems, high volume, audit requirements, or AI steps that must be tested and monitored like software. Some projects use a platform for orchestration and custom services for the AI steps.
What happens when the AI is not sure?
The item goes to an exception queue instead of continuing. Each AI step reports a confidence level and is checked against rules and system data - for example, an invoice total must match the purchase order. Anything below the threshold, or failing a check, is shown to a person with the AI's suggestion and the reason it stopped. Their decision is recorded and used to improve the workflow.
How much does AI automation cost?
The main factors are the number of input types and exceptions, the systems to integrate, the approval logic and the volume. Starting with one well-defined workflow keeps the first investment contained and gives you measurements from live use before deciding on the next. We estimate after reviewing the process with sample documents.
Start node · your brief
Which process does your team repeat every day?
Describe the process, the documents or messages involved, and the systems at each end. We'll tell you which parts suit AI, which suit plain rules, and what a first pilot would cover.