AI workflow automation

AI workflow automation means using AI to carry out a sequence of tasks within a workflow. While traditional workflow automation often works by defining fixed rules, AI workflow automation tools can adapt over time.

Use cases for AI workflow automation aren’t limited to simple admin work but also extend to complex customer service workflows, like implementing agentic AI to handle multi-step customer requests.

How does AI workflow automation work?

A typical AI workflow automation involves four steps:

1. Capture the request: A trigger, like an email or a support ticket, sets the process in motion. Say a frustrated customer replies to a delayed shipment notification to request a refund. This triggers a customer service AI agent to analyze the message along with the customer’s order history and account information.
2. Interpret the request: AI analyzes events to determine what they mean and what steps should come next, like routing a conversation to a human or drafting a re-engagement email for a high-value, dormant account. In the case of the customer’s refund request, the AI tool detects the frustrated sentiment, classifies it as a refund request, and drafts a polite response.
3. Loop in a human: AI workflows don’t need to be end-to-end. Workflows can include a mix of techniques: AI, deterministic steps, and human intervention. For sensitive issues like the refund example, a human team member may approve the request and review the drafted message before it’s sent.
4. Execute the action: Once a human gives their approval (when applicable), the AI agent carries out the specified action. This might be an isolated action within a single system or a multi-step process that spans multiple connected apps. In the case of the refund, it’s initiating the payment and replying to the customer’s email with the approved draft.

AI workflow automation best practices

To take advantage of AI workflow automation, you can choose an automation platform, like n8n or Make, or choose a customer service platform with built-in workflow automation, like Front. Once you’ve selected software, focus on maintaining visibility and context throughout your workflows, and measure the success of each regularly.

AI isn’t perfect, meaning teams must be able to monitor and control an agent’s behavior. Set clear boundaries around what an agent can do on its own and what requires human oversight.

The quality of an AI system’s decisions often depends on the context it receives. Where safe and feasible, connect AI to relevant sources of information — like your customer relationship management tool or knowledge base — so the tool can make informed decisions.

Finally, review and improve your processes over time. Define clear success criteria so you can evaluate how effective your AI workflows are. The volume of automated tasks doesn’t tell you whether those tasks are being resolved successfully.