AI automation.
Explained in plain English.

What is the difference between automation and AI?

Traditional automation follows defined rules: when a form arrives, create a record. AI can help interpret less structured information, such as a customer’s message or a document. A reliable system combines these approaches, using fixed rules where certainty matters and AI where interpretation is useful.

A simple example

A customer sends an inquiry. A workflow creates a CRM record. AI summarizes the request and suggests a category. Business rules route it to an owner, who reviews the context and responds. The system records failures and sends uncertain cases to a person.

Which tasks are good candidates?

Look for repeated work with clear inputs, a known destination, and a measurable outcome. Inquiry routing, document extraction, internal summaries, and routine support questions are useful starting points. A process with unclear rules or constantly changing requirements should be clarified before it is automated.

What should remain under human control?

Keep sensitive or consequential decisions with qualified people. Define approval steps for external messages, financial actions, and changes that are difficult to reverse. Give each connected tool only the access it needs and decide how exceptions will be handled.

What affects project cost?

Scope depends on the number of systems, the quality of existing data, integration availability, testing, and support. Ongoing expenses can include automation platform subscriptions, model usage, hosting, and maintenance. Agree on these before building.

How do you choose a first project?

1. Identify one repeated task.
2. Document its trigger, steps, tools, and owner.
3. Collect representative, anonymized examples.
4. Choose a success measure and an exception process.
5. Build a small workflow, test it, then expand based on evidence.

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