AI automation services
Put AI inside a controlled business workflow
AI creates the most value when it performs a defined job inside a process that already has an owner, inputs, outputs and a measurable result.
AI Creative designs workflow automation that combines AI with rules, integrations and human review. The goal is not to add an assistant beside the process. It is to reduce the work required to move the process forward.
Example system view
- IntakeEmail, form, document or system event enters the workflow.
- Classify / extractThe AI task reads the unstructured input and returns structured fields.
- Confidence gateRules decide whether the result can proceed or needs a person.
- Low-risk actionPermitted actions execute against validated data.
- System updateThe approved result is written back to the system of record.
Human review
Triggered by low confidence, high impact, sensitive content or missing evidence.
Audit history
Source · output · reviewer decision · final action
Opportunity
Good automation starts with a bounded task
Useful AI tasks often include classification, extraction, summarization, drafting, matching, routing, recommendation and structured research.
They are easier to evaluate than vague goals such as “automate customer service” or “use AI in operations.”
- Enough volume or cost for the work to matter.
- Input data the workflow can actually reach.
- A clear definition of acceptable output.
- A recovery path when the system is uncertain or wrong.
Where automation is the wrong answer
Design
Combine AI with deterministic rules
Not every step needs a model. Business rules are often better for known thresholds, required fields, permissions, routing and validation.
AI can handle the unstructured part of the work, such as reading an email, extracting information from a document or drafting a response. The workflow becomes more reliable when each technology is used for the job it handles best.
Example system view
- RuleRequired fields presentValidation rejects incomplete intake before anything else runs.
- AI taskRead the documentExtraction returns structured fields from unstructured input.
- RuleMatch to an existing recordDeterministic lookup against the system of record.
- AI taskSummarise the exceptionA short rationale the reviewer can act on.
- RuleThreshold and permission checkValue limits, approvals and role permissions.
- AI taskDraft the responseA proposed reply the owner can edit before it is sent.
- RuleRoute and write backQueue assignment and the audited system update.
Control
Keep human judgment where the consequence requires it
A review checkpoint can be triggered by low confidence, sensitive content, financial thresholds, contractual impact or an explicit policy rule.
The reviewer should see the proposed action, supporting context and enough rationale to make a decision without reconstructing the workflow manually.
Example interface
Operations console / review queue
INV-2048 · Invoice extraction
Confidence: Below threshold
Post supplier invoice to the matching purchase order and route for payment.
Supporting context
- Supplier name matched
- PO number read from page 1
- Tax total did not reconcile
No decision recorded yet for this item.
Integration
Connect automation to the systems where work happens
An AI workflow becomes operational when it can read authorized context and write an approved result back into CRM, ERP, ticketing, document, ecommerce or custom systems.
Those integrations should be governed with the same validation, permissions and audit requirements as any other business integration. See how records stay consistent across systems →
Evaluation
Evaluate before scaling
A production AI workflow needs more than a successful demo.
Evaluation should include sample cases, expected outputs, error categories, confidence thresholds, reviewer feedback and the business consequence of failure. Metrics may include accuracy, review rate, cycle time, cost per case and the proportion of work that can move without manual handling.
| Measure | How it is calculated | Target |
|---|---|---|
| Accuracy on the sample set | Labelled cases reviewed by the process owner | Agreed per workflow |
| Review rate | Share of cases routed to a person | Agreed per workflow |
| Cycle time | Intake to completed action | Measured against the current process |
| Cost per case | Model, integration and review time | Compared with the manual baseline |
| Straight-through share | Eligible cases completed without manual handling | Grows only with evaluation evidence |
| Error categories | What went wrong and what it cost | Reviewed each cycle |
Use cases
Where AI automation can fit
Workflows where unstructured information creates repetitive work are usually the first place to look.
Document intake
Reading incoming paperwork and turning it into structured, checkable records.
Vendor onboarding
Collecting, validating and filing the information a new supplier has to provide.
Support triage
Classifying and routing requests so the right team sees them first.
Proposal drafting
Assembling a first draft from prior work, pricing rules and account context.
Invoice processing
Extraction, matching and exception handling against the system of record.
Inspection summaries
Turning field notes and photos into a consistent report and follow-up tasks.
Account research, internal requests, knowledge retrieval and proposal preparation follow the same pattern. Document-heavy processes have their own page →
Business case
Build the economics around the eligible workload
Not every case in a process can or should be automated.
The useful business case starts by identifying the portion of work that is repetitive enough, consistent enough and low-risk enough to handle with automation. Review and exception handling still consume time and should be included in the model.
That creates a more realistic measure of value: reduced touch time on eligible cases, faster cycle time and better consistency rather than an assumption that an entire role disappears.
FAQ
Frequently asked questions
We start with bounded workflows where the task, data and value can be evaluated. Larger automation programs can grow from proven use cases.
Yes, where integration access exists and the workflow defines what the AI may propose or execute.
By using constrained tasks, validation, permissions, confidence rules, human review where required, testing and auditability.
Model choice depends on the task, data, cost, performance and deployment requirements. The workflow should not be unnecessarily dependent on one model when alternatives can be supported.
Automate the work that is repetitive enough to measure and important enough to design properly
Bring one workflow with volume behind it. We will define the task, the review point and the measure of success before anything is built.