What Is AI Automation?
AI automation combines artificial intelligence with automated workflows to perform tasks with less manual effort. Traditional automation follows predefined rules, while AI can handle tasks that involve language, interpretation, classification, summarization, or other forms of flexible decision making.
For example, a traditional workflow might move every new file into a specific folder. An AI powered workflow could read the contents of a document, identify its type, summarize it, and then route it to an appropriate location.
Traditional Automation vs AI Automation
Traditional automation generally follows explicit rules such as: when an event happens, perform a particular action.
AI automation can add an AI step that interprets information before deciding what should happen next.
For example:
- Traditional automation: When a form is submitted, send an email.
- AI automation: When a form is submitted, use AI to classify the request and then route it to the appropriate team.
Triggers and Actions
An automated workflow usually begins with a trigger. A trigger is an event that starts the workflow.
Examples of triggers include receiving an email, submitting a form, creating a file, receiving a new customer inquiry, or adding a row to a spreadsheet.
Actions are the tasks performed after the trigger. Actions may include sending an email, creating a record, updating a spreadsheet, generating a document, or sending information to another application.
Where AI Fits In
AI can be inserted into a workflow as one of the processing steps.
For example:
- A customer submits a support request.
- The workflow receives the request.
- AI identifies the topic and urgency.
- The workflow sends the request to the appropriate team.
- The system records the result.
AI is particularly useful when the input is unstructured, such as natural language, documents, or images.
Common AI Automation Tasks
- Classifying emails or support requests.
- Summarizing documents.
- Extracting information from text.
- Drafting responses.
- Converting unstructured information into structured fields.
- Routing requests based on their content.
- Generating reports from collected information.
- Creating follow-up tasks.
Email Automation
AI can help automate email workflows. A workflow might analyze incoming messages, identify their category, summarize the content, and create an appropriate follow-up task.
For example, customer inquiries can be classified as sales, technical support, billing, or general questions and then routed accordingly.
Document Automation
AI can process documents as part of an automated workflow. A system could receive a document, extract important information, classify it, and store the results in a database or spreadsheet.
This can reduce repetitive data entry when large numbers of similar documents need to be processed.
Customer Support Automation
AI can help classify support requests, summarize conversations, suggest responses, and identify common issues.
For sensitive or complicated requests, the workflow can send the case to a human employee instead of attempting to handle it automatically.
Content Workflows
AI automation can also support content workflows. For example, a new article could trigger a workflow that creates a summary, generates suggested tags, prepares a social media draft, and records the content in a publishing system.
Human review can be included before anything is published.
Data Entry Automation
AI can extract information from emails, documents, forms, or other unstructured sources and place the information into structured fields.
For example, an invoice processing workflow could extract a supplier name, invoice number, date, and amount before storing the information in a spreadsheet or accounting system.
AI Automation Platforms
Many automation platforms allow users to connect applications and create workflows without writing large amounts of code. Some platforms also provide AI actions or integrations that can process text and other information.
The exact features differ between platforms, but the underlying idea is similar: connect a trigger, process the information, perform actions, and record the result.
Human in the Loop
Not every task should be fully automated. A human review step can be added when an AI decision could have significant consequences.
For example, an AI system might prepare a customer response, but a person reviews it before it is sent. This can reduce the risk of incorrect or inappropriate automated decisions.
Testing AI Workflows
Automation should be tested before being used with real data. Developers or users should test normal cases, unusual cases, missing information, incorrect inputs, and failure conditions.
AI outputs can vary, so testing should focus not only on whether the workflow runs but also on whether the results are appropriate.
Monitoring
Automated workflows can fail after they have been deployed. An application can change, an integration can stop working, or an AI model can produce unexpected results.
Important workflows should therefore have appropriate monitoring and error handling.
Permissions and Security
Automation workflows may have access to email accounts, files, databases, customer information, or other systems.
Users should provide only the permissions that are necessary for the workflow. Sensitive information should be protected throughout the workflow.
Cost and Usage
AI automation can involve usage costs. A workflow that processes thousands of documents or messages may consume significantly more AI resources than a workflow used occasionally.
Users should understand the pricing model of the tools they use and design workflows efficiently.
Start Small
A good approach is to automate a small, repetitive task first. Once the workflow works reliably, additional steps can be added.
Starting with a simple workflow makes testing and troubleshooting easier.
A Simple AI Automation Workflow
- Identify a repetitive task.
- Define the event that should start the workflow.
- Identify the information the workflow needs.
- Add an AI step where interpretation or generation is useful.
- Define the actions that should happen after the AI step.
- Add validation or human review where necessary.
- Test the workflow with different examples.
- Monitor the workflow after deployment.
Example
Imagine a company receives many customer emails every day.
A possible workflow could be:
- New customer email arrives.
- AI summarizes the message.
- AI classifies the request.
- The workflow records the category and summary.
- The request is routed to the appropriate team.
- A human reviews the case when the confidence is low or the issue is sensitive.
Conclusion
AI automation can combine the reliability of workflow automation with the flexibility of artificial intelligence. It can reduce repetitive work involving emails, documents, data, customer requests, and content. The best workflows start small, are tested carefully, use appropriate permissions, include human review when necessary, and are monitored after deployment.