AI workflow automation and AI agents Routine runs automatically. Your team decides on the exceptions.
Matching invoices, gathering inspection lot documents, checking change orders line by line: the data for all of it has long been in your systems. We take one of these workflows into daily operations, measured against a target we agree with you beforehand.
AI-supported workflows
AI Workflow Deployment by DataClue takes one clearly defined workflow, such as invoice processing, into productive use. Fixed rules handle the standard cases, AI the cases where rules are not enough. Exceptions and high-impact decisions go to your team with a reason, for human approval (human-in-the-loop). Every decision is logged.
When AI Workflow Deployment fits
- Specialists spend too much time reading, classifying, reconciling or transferring information between systems.
- Work moves between inboxes, spreadsheets and systems instead of flowing through one controlled process.
- Copilots help individuals, but business rules, integration, exceptions and ownership remain manual.
- A pilot works, but controls, monitoring and operating ownership are still open.
What you walk away with
- A workflow in daily operationsThe workflow is connected to the systems and data it depends on. It is measured against agreed success criteria. After the handover your team runs it.
- Rules and AI, clearly dividedFixed rules handle the standard cases. AI handles the cases where rules are not enough, such as unstructured documents and requests. Exceptions go to your team with a reason.
- Control and traceabilityClear review and approval points, exception handling and a manual fallback. Every decision is logged traceably, including for internal and external audit.
How automation and AI work together.
Traditional automation executes fixed steps. AI agents process unstructured information and plan next steps within their task. Depending on the workflow, the two work separately or together.
We connect these components to your systems in workflows that run end to end, which saves manual handoffs. Defined permissions apply before every action. Fixed rules complete the standard cases, AI handles the rest. Your team receives exceptions with reasons.
What it takes
- Who is involved
- On your side: the specialists who run the workflow today, one person responsible for approvals, and your IT for integration and operations. On ours: forward-deployed engineers on site.
- What data we need
- The data and documents of the workflow, such as invoices, purchase orders, goods receipts or emails. Plus access to the systems the result flows back into.
- Data protection and IT
- The data stays in your systems or in an environment you choose, processed under the GDPR. We use AI models hosted in the EU. Your data is not used to train third-party models. Access rights, operating model and deletion are settled before we access any data. The AI works under its own service account, limited to the rights the workflow needs. We review EU AI Act obligations up front and build them in, for example through documentation and human oversight.
This is how an AI-supported workflow goes live.
Define workflow and success
Map the current process, volumes, systems, exceptions and measurable success criteria.
Divide rules, AI and people
Define which standard cases fixed rules handle, which cases AI handles and what people approve.
Build and integrate
Connect systems and data, implement the workflow and integrate the AI components it needs.
Validate with real cases
Test normal cases, exceptions and failure modes against the agreed quality criteria.
Go live and hand over
Move into productive use, refine based on real outcomes and transfer the operating knowledge to your team.
Operations and control
From the first workflow to daily operations
For every process step, your team decides what the AI may do on its own. The system enforces these rules; they are not just written into an instruction to the AI.
Three approval levels
The example above is the level “With approval”: the AI prepares, purchasing approves.
- Automatic
The AI completes the step on its own
Reading and matching data, drafts and internal notes. Every run is logged.
- With approval
AI prepares, your team approves
Customer emails, changes in the ERP or CRM, quotes. Approval takes one click. The record shows who approved what and when.
- Your team only
No access for the AI
Payments, contracts, deletions and HR decisions. The AI has no access to these.
How automation grows
For each workflow we measure how many suggestions your team approves unchanged. Our own accounting agent further down shows what that looks like.
A step only moves from “with approval” to “automatic” once that rate holds over an agreed number of runs. If errors occur, it moves back.
Rejected cases become test cases. Every change to the rules runs against these tests before it goes live.
The rules, test cases and data belong to you, not to the model. The system runs in your Microsoft environment or hosted in the EU.
Case studies
- About 91%of bookings identical to the tax advisors’ final versionAI booking agent: documents coded, booked and handed over automatically every day
- Weeks earliercountermeasures instead of waiting for the monthly planning cycleAgentic S&OP: an AI workflow delivers recommendations ready for decision.
All client examples are anonymized.
Typical back-office workflows
Invoice processing and accounting
- Today
- Your team checks every invoice against the purchase order and goods receipt and assigns accounts by hand.
- Going forwardRuns automatically
- Whatever matches the purchase order, goods receipt and account assignment rule.
- Your team decides
- Variances above the approval threshold.
- Metrics
- Share of invoices posted automatically, lead time to posting.
Quotes and contracts
- Today
- Requests and contract reviews pile up with a handful of specialists.
- Going forwardRuns automatically
- Pre-checks against standards and checklists.
- Your team decides
- Price, risk and exceptions.
- Metrics
- Lead time from request to quote, share of contract reviews approved without queries.
Incoming email
- Today
- Requests arrive unsorted in shared inboxes and are distributed by hand.
- Going forwardRuns automatically
- Classifying incoming messages, drafting replies, routing tasks.
- Your team decides
- Approving replies and anything unusual.
- Metrics
- Time to first reply, share of replies approved unchanged.
Typical workflows in production and quality
Inspection lot documents
- Today
- Quality assurance gathers certificates, measurement reports and drawing revisions for every inspection lot.
- Going forwardRuns automatically
- Assigning documents from the MES and DMS to the lot and checking them for completeness.
- Your team decides
- Releasing the lot and every deviation.
- Metrics
- Lead time to release, share of lots prepared completely.
Delivery schedules
- Today
- Customer call-offs are checked against stock and capacity by hand.
- Going forwardRuns automatically
- Reading call-offs, matching them against stock and the capacity plan, flagging bottlenecks.
- Your team decides
- Commitments in case of conflicts, changes to the plan.
- Metrics
- Time to confirmation, share of call-offs confirmed without rework.
Incoming complaints
- Today
- Complaints arrive by email and portal and are logged by hand.
- Going forwardRuns automatically
- Logging them, linking part and delivery, attaching documents.
- Your team decides
- Assessment, actions and the reply to the customer.
- Metrics
- Time to logging, share of complaints logged completely.
Applied AI & Analytics
AI-supported workflows
Frequently asked questions
How does an AI agent differ from a chatbot and an AI assistant?
A chatbot answers in conversation but does not act in your systems. An AI assistant helps one person with a task that person starts. An AI agent works through a workflow in several steps on its own: it reads incoming items, uses your systems with defined rights and prepares an action. Together these systems are called agentic AI.
When does an AI agent make sense, and when a fixed rule?
A rule fits when the case is clear-cut, for example when purchase order, goods receipt and price match. AI pays off where information is unstructured or needs judgment, such as reading documents or emails. Often the AI reads the document first, and the rule then decides. High-impact or irreversible actions are always approved by a person.
What happens when the AI gets it wrong?
An error does not take effect unnoticed. Anything that leaves the company or changes data in the ERP is approved by your team first. Cases where the AI is unsure go to your team with a reason. Rejected suggestions become test cases. If errors pile up in a step, it needs approval again.
What if our system has no interface?
Then we connect it through what it offers: file export and import, a monitored inbox or the DMS archive. There is no need to replace your ERP or MES. In the first step we check on your systems which route works.
How is this different from an automation tool?
A tool provides building blocks. We take one concrete workflow into daily operations: integrated with your systems, with review and approval points, a log, measurement against an agreed target and handover to your team.
What does the works council say?
Systems that log decisions can be subject to co-determination. We involve the works council early and disclose what is logged and why.
Contact
Which workflow costs your team the most time today?
Bring one concrete workflow. We assess whether AI pays off there or fixed rules are enough. We also show what running it in daily operations requires.
