Automation
AI workflow automation, designed so a person can check every step
Kriv AI designs and builds AI workflows on Zapier, Make, n8n and Power Automate for healthcare, financial services, insurance and small business teams. This page shows how a workflow is put together and where the controls go.
AI workflow automation is a business process where software moves the work from step to step and an AI model handles the steps a fixed rule cannot, such as reading a document, classifying a request or drafting a reply. A well-designed AI workflow also says where a person approves the output, what the AI step is allowed to see and what gets logged. The design matters more than the tool.
what it is
What is AI workflow automation?
A workflow is the path a piece of work takes: a request arrives, someone reads it, decides what it is, looks something up, writes a response and files a record. Classic automation handles the steps that follow a fixed rule. AI workflow automation adds a model for the steps that need judgement about language or documents, while the workflow around it keeps control of where data goes and who signs off.
The useful test is simple. If you could write the step as an if-then rule, it does not need AI. If the step needs someone to read and interpret something, an AI step can draft the answer for a person to approve. Our explainer on agentic AI versus traditional automation covers the difference in more depth.
anatomy
The seven parts of AI automation workflows
Every workflow we build is written down in these seven parts before anything is configured. If one is missing, the workflow is not ready to build.
1. Trigger
What starts the workflow: a new email, a form, a file in a folder, a record change or a schedule.
2. Inputs
Exactly which data the workflow reads, and which data it must never pass to the AI step.
3. AI step
The one task the model does, written as an instruction with examples of a good and a bad output.
4. Rules
Checks applied to the AI output before anything else happens, such as required fields, allowed values or a confidence threshold that sends the item to a person.
5. Approval
The point where a named person accepts, edits or rejects the output. Anything that leaves the organisation or changes a record of consequence passes here.
6. Action
What the workflow does after approval: send, file, update a system or open a task.
7. Log
A record of the input, the AI output, the approver and the time, kept where your reviewers can find it.
agent workflows
AI agent workflow automation: when to let an agent take a step
In a fixed workflow, the designer decides every step in advance. In AI agent workflow automation, an agent decides some of the next steps itself, such as which system to search or which tool to call. That flexibility suits tasks where the path varies from case to case, like assembling the documents for a review.
It also widens what can go wrong. An agent should get only the permissions its task needs, a limit on what it can do without approval, and a log of every action it took. We cover this on our pages on AI agent security risk and on governance for agent orchestration platforms. Where a fixed workflow does the job, we build the fixed workflow.
first workflows
Workflows worth automating first
Good first candidates are frequent, rule-heavy around one judgement step, and easy to check. These patterns fit that description.
| Workflow | The AI step | Where a person approves |
|---|---|---|
| Inbox and form triage | Classify each request and extract the key fields. | Items below the confidence threshold, and anything routed outside the team. |
| Document intake | Read invoices, forms or contracts and pull out named fields. | Before the fields are written into the system of record. |
| Draft replies | Draft a response from a template and the case details. | Every draft, before it is sent. |
| Case summaries | Summarise a long thread or file for the next reviewer. | The reviewer reads the summary alongside the source, not instead of it. |
| Recurring reports | Turn data you already hold into a written first draft. | The report owner signs off before distribution. |
regulated design
Designing AI workflows for regulated teams
Our clients work in healthcare, life sciences, financial services and insurance. For them the inputs and approval parts carry the most weight. Before a build, the compliance and IT owners decide which records the workflow may read, which AI services may process them under the agreements the organisation already holds, and which roles approve.
We write those decisions into the workflow's rules, to the policy and frameworks you already answer to, such as the NIST AI RMF or the NAIC Model Bulletin. Your compliance team keeps the final judgement. One more risk to plan for is staff building their own unapproved automations; our page on shadow AI risk management covers how to find and govern them.
tools
AI workflow automation tools: design first, then pick
Once the seven parts are written, the tool choice gets narrower. Where the data lives, who maintains the workflow and how approvals work decide most of it. Kriv AI is a Zapier Silver Solution Partner and also builds on Make, n8n and Power Automate, so we recommend the one that fits the workflow and the team that will own it, not the other way round.
how an engagement runs
How a workflow engagement runs
1. Discovery call
We pick one workflow, learn who owns it and what systems it touches. Work is scoped and priced after this short discovery call.
2. Workflow written in seven parts
We document trigger, inputs, AI step, rules, approval, action and log with the owner, and agree what success looks like.
3. Build and pilot
We build on the agreed tool and run it on real but limited volume, with a person approving every output.
4. Handover
Your team receives the workflow, its written design, the run log and a walkthrough, so they can maintain and extend it.
Related resources
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Straight answers
Frequently asked questions about AI workflow automation, designed so a person can check every step
What is AI workflow automation?
It is a business process run by software where an AI model handles the steps that need reading or judgement, such as classifying requests, extracting fields from documents or drafting replies. The rest of the workflow moves data between systems and routes the AI output to a person for approval where it matters. The value comes from the design around the AI step, not the model alone.
How is AI workflow automation different from RPA?
Robotic process automation follows fixed rules and screen steps, and it stops when the input changes shape. An AI workflow can read unstructured input, such as an email or a scanned form, and decide what it is. The two combine well: rules for the predictable steps, an AI step for the interpretation, and a person for approval.
What are the best AI workflow automation tools?
The best tool is the one that fits where your data lives, who will maintain the workflow and how approvals need to work. Connectors such as Zapier or Make suit cloud-app teams, n8n suits teams that need to self-host, and Power Automate suits Microsoft 365 organisations. Write the workflow down first; the shortlist follows from it.
Can AI agents run a whole workflow on their own?
They can run steps, but a sound design keeps a person approving anything that leaves the organisation or changes an important record. Agents also need permissions limited to their task and a log of every action. For many processes a fixed workflow with one AI step is simpler, cheaper to run and easier to check than an agent.
Which workflow should we automate first?
Pick one that runs often, follows clear rules around a single judgement step and is easy to check, such as inbox triage or document intake. Avoid starting with a process nobody owns or one where a mistake is hard to spot. A first workflow that works earns the trust the second one needs.
Do we need developers to build AI workflows?
Not for many of them. Connector tools and Microsoft-native automation are built for business users, and a trained operations person can maintain a documented workflow. Developer time is needed when the workflow calls internal systems without a ready connector, runs on your own servers or needs custom code around the AI step.
How much does it cost to automate a workflow?
Running costs depend on the tool's plan and your volume of runs and AI calls, so ask each vendor to price your expected volume. Our work to design, build and hand over a workflow is scoped and priced after a short discovery call, once we know which workflow and which systems are involved.
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