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An algorithm is a procedure, automation is a way of carrying out a task with less human intervention, and artificial intelligence (AI) describes capabilities such as making predictions or recommendations. They are related but not interchangeable: an automated task can follow fixed rules without AI, while an AI system can offer a recommendation for a person to review rather than act on its own.
What do algorithm, automation, and AI mean?
Algorithm: a specified procedure
NIST defines an algorithm as “A clearly specified mathematical process for computation; a set of rules that, if followed, will give a prescribed result.” In plain language, it is a procedure for producing an output from instructions or inputs. It can be simple or complex, and it does not have to be AI. A fixed set of steps for sorting a list or calculating a total is still an algorithm. NIST’s algorithm glossary gives the formal definition.
Automation: a task carried out with less intervention
Automation concerns how work gets done, not whether the system is intelligent. The European Labour Authority’s handbook defines it as “the creation and application of technologies to produce and deliver goods and services with minimal human intervention.” A person may set up or start an automated process even if technology handles the subsequent steps. The European Labour Authority handbook discusses algorithms, automation, and AI together.
AI: capabilities such as inference and prediction
There is no single definition of AI used universally. NIST’s glossary gathers multiple definitions, including systems that can perform tasks in varying circumstances, learn from data, or make predictions, recommendations, or decisions. The OECD likewise describes AI systems in terms of objectives, inputs, models, and inference. Its experts’ description says: “An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.” Read the NIST AI glossary, the OECD AI-system definition discussion, and the OECD AI Principles for these descriptions and context.
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How the three concepts relate
A useful shorthand is: algorithm = the procedure; automation = carrying out a task with reduced human intervention; AI = capabilities used to interpret inputs or produce predictions, recommendations, or decisions. This is a teaching aid, not a strict taxonomy. AI software uses algorithms, and automation can follow fixed rules without AI. Conversely, AI can support a person’s decision without automating the whole task.
| Term | What it names | What determines the output | Human role |
|---|---|---|---|
| Algorithm | A computational procedure | Specified rules or steps | A person may create, start, review, or use its result |
| Automation | Technology carrying out a task with minimal human intervention | May be fixed rules, AI, or a combination | People may set up, monitor, review, or override the process |
| AI | System capabilities such as inference, prediction, recommendation, or decision-making | A model or other system process applies inputs to objectives; not every definition requires learning | Can range from decision support to operation with greater autonomy |
The definitions do not establish a single level of autonomy or oversight for every system. The OECD notes that AI systems can operate with varying levels of autonomy. Whether a person initiates an action, checks the result, or can override it depends on the particular application.
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Email example: rules, AI, and automated action
Consider a general inbox illustration, not a claim about a particular email product:
- A rule that sends every message containing a chosen phrase to a folder follows an algorithm and automates the sorting task. It need not use AI.
- A classifier that estimates whether a message is spam uses a predictive capability commonly associated with AI or machine learning.
- If the system automatically moves messages after classifying them, the workflow combines AI capability with automation.
- If it instead flags likely spam for a person to inspect, AI is being used without fully automating the decision.
The example shows why the labels describe different aspects of a system. A procedure may determine how a result is calculated, AI may help interpret an input, and automation may determine whether the next action happens without a person doing it manually.
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How to tell what a system is doing
When evaluating a tool or workflow, ask four separate questions rather than relying on a label such as “AI-powered”:
- What is being described? Is the claim about a procedure, a task being performed, or a system capability?
- How is the output produced? Is it determined by fixed instructions, or inferred using a model? Do not assume that every AI system learns continuously; definitions include systems that make inferences without defining them as continuously learning.
- Where does a person fit? Does a person initiate the process, review its output, or have a way to override it? AI systems can have different degrees of autonomy.
- What happens when something unusual occurs? Consider how the process handles unexpected inputs, errors, and decisions with meaningful consequences, and whether a person can review or correct the outcome.
These questions are more informative than treating “algorithm,” “automation,” and “AI” as competing names for the same thing. A single application may involve all three, but each term answers a different question.
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