automation
Automation means that a task is performed by a machine or software without human involvement or with minimal human supervision.
In plain terms
The essence of automation is that what was previously done manually by a human is taken over by a machine or program. A washing machine is a good example: in the past, people soaked, scrubbed, and rinsed by hand; today you set the program, and the machine carries out the steps.
It is most worthwhile where work is repetitive, rule-based, and high-volume, such as on the production line, in invoicing, payroll, or warehousing. The machine does not get tired, does not get bored, and performs the same step the thousandth time exactly the same way.
Traditional automation follows pre-written rules: if this happens, do that. Artificial intelligence enables the automation of tasks that previously required human judgment, such as recognizing the content of a letter and routing it to the correct destination, summarizing a document, or analyzing an image.
The latest stage is the AI agent, which does not carry out a single step but is given a goal and plans for itself which steps will achieve it. This has given automation new momentum in knowledge work too, although office back-end processes were already widely automated earlier.
How it works
- First, someone assesses the task: what steps it involves, what the input is, what the expected outcome is, and where decisions are needed.
- The steps are translated into machine form: this can be a sequence of movements for a robotic arm, a software rule set, or instructions given to an AI model.
- The system starts upon a triggering event, such as an order arriving, a deadline expiring, or a sensor signaling, and proceeds through the steps without human intervention.
- Unusual cases that do not fit the rules are stopped by a well-designed system and handed to a human, while routine tasks are handled automatically.
- The results are measured and logged, and based on this, the process is refined or the scope of tasks delegated to machines is expanded.
An everyday example
When you order online and receive a confirmation email, an invoice, and a package tracking link within seconds, none of these were written for you by a human. The online store system automatically triggers these steps upon order placement, while the task also appears in the warehouse.
Who is affected
A common misconception
Limits and risks
- An automated system repeats errors just as quickly and in large batches as good steps, so a poorly configured process can cause significant damage before anyone notices.
- Rule-based solutions handle unexpected, unusual situations poorly, while AI-based ones can be confidently wrong, so human oversight is needed for important decisions.
- Implementation costs money, time and organizational effort, and a process that is flawed to begin with does not get better through automation; it just runs badly, only faster.
- If you rely entirely on the machine, you may lose the knowledge needed to perform the work manually if the system fails.
Similar, but not the same
- artificial intelligence – Artificial intelligence can be an important tool for automation, but automation can also be achieved with simple, pre-written rules without learning. AI is not used only for automation; for example, it can also assist human decision-making.
- digitalization – Digitalization makes paper-based data and administration electronic, while automation means that steps are no longer carried out by humans.
- AI-agent – Traditional automation follows a fixed sequence of steps, whereas an agent receives a goal and decides itself which steps and tools to use to achieve it.
Related concepts
technological change, Productivity, digital transformation
robotic process automation, ipari robot, workflow automation, agent-based automation
artificial intelligence, AI-agent, digitalization, reskilling, labor market
A brief history
Mechanization dates back to the Industrial Revolution, but the word 'automation' itself spread in the American automotive industry: Ford established an automation department in 1947, and the term was popularized by Delmar S. Harder, one of the company's executives. Software-based, then AI-based automation built upon this factory tradition.
Where things stand
By the end of 2026, automation is standard practice in industry and in office back-office processes. Solutions built on language models and agents are spreading rapidly in knowledge work, but their reliability and oversight remain open issues in many places.
In the news
- Siemens speeds up industrial AI scale-up and China localization, presents Eigen engineering agentNov 4, 2026 · Ipar
- SUSE to cut about 10% of its workforce, citing AI and automationNov 2, 2026 · Work & HR
- Walmart extends Symbotic warehouse automation deal to 25 distribution centers in $6B agreementNov 2, 2026 · Ipar
- SPS 2026 puts industrial AI and software-defined automation into practiceNov 2, 2026 · Ipar
- Robotics and industrial automation startups raise major funding: Bleu, Foundry, NeuronNov 2, 2026 · Ipar
- TKH to acquire industrial AI firm Wolkabout to expand its automation offeringNov 2, 2026 · Ipar
- Universal Robots' Hungarian head presents trends in the country's industrial roboticsNov 2, 2026 · Ipar
- Lao Gan Ma denies mass layoffs after backlash over AI automation claimsOct 29, 2026 · Work & HR
Also known as
automation · automation · automation · mechanization · AI automation · intelligent automation · process automation · workflow automation
Sources
This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.