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AI automation uses artificial intelligence within a process to interpret information, suggest or make decisions, and sometimes carry out tasks. It can help a person handle one step—or let an AI agent take several steps across connected systems. The amount of human oversight varies, so “AI automation” does not necessarily mean a process runs on its own.
What is AI automation?
AI automation is the use of AI capabilities as part of a workflow. Depending on the task and system, AI might classify information, generate a recommendation, or take an authorized action. The term describes a broad approach rather than one specific technology or a single formal definition.
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The National Institute of Standards and Technology (NIST) glossary collects multiple definitions of artificial intelligence from different sources. One describes an AI system as a machine-based system that, for human-defined objectives, can make predictions, recommendations, or decisions that influence real or virtual environments. Definitions differ in how they treat autonomy and learning, so AI automation should not be taken to mean that every system learns independently or acts without human direction.
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Conventional automation typically follows defined rules: when a specified condition occurs, the system performs a specified action. AI-enabled automation can add the ability to interpret less-structured inputs or generate recommendations within that process. For example, a rules-based workflow might route a form based on a selected category, while an AI-enabled one might first interpret the request and suggest a category.
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This is a practical distinction, not a hard boundary. A workflow may combine fixed rules, AI-generated output, and human review. AI involvement alone does not establish how much control the system has.
Examples of AI automation
NIST describes organizations using AI agents for information retrieval, workflow automation, software development, and cybersecurity operations. These are examples of possible applications, not guarantees that a system will perform them accurately or deliver a particular productivity gain.
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Finding and organizing information
An AI-enabled workflow can help retrieve information or interpret a request so it can be routed to the appropriate person or process. A human may still verify the answer or decide what to do with it.
Supporting software development
NIST’s DevSecOps reference model describes AI assistance with code generation, test generation, static application security analysis, and interactions with software-development tools. Such assistance can contribute to a development workflow, but generated code and analysis still need appropriate checks.
Automating workflow steps
An AI agent can be used in a workflow that involves more than producing a suggestion. Depending on its permissions and design, it might retrieve information, update a connected system, or initiate another step. The more actions it can take, the more important it is to define where people review its work and what it is authorized to change.
What can AI automation help with—and when might it not fit?
AI may support efficiency, productivity, or decision-making, but the outcome depends on the task and how the system is deployed. NIST cautions that AI may not be the right solution for a particular business problem and recommends weighing expected benefits against risks and intended objectives.
Start with the problem, not the technology. If a task has clear inputs and rules, conventional automation may be sufficient. If interpreting information is central to the task, AI could be worth considering—but only if its likely benefit justifies the added risks and oversight requirements. There is no general productivity percentage that applies to every AI-enabled workflow.
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AI automation can produce inaccurate outputs, expose data, take unauthorized actions, or make behavior difficult to explain. In software development, generated code may also contain security problems. People may place too much trust in automated output and fail to challenge an error.
NIST’s Generative AI Profile describes “automation bias” as excessive deference to automated systems. It notes that this can worsen risks associated with confabulation and bias. In practice, a polished or confident-sounding output should not be treated as proof that it is correct.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an AI-automated workflow
Before using AI in a process, consider both what the system does and the consequences if it gets something wrong. Match human review and system permissions to the impact of a mistake. An agent that drafts an internal suggestion presents a different level of exposure from one that can change records, contact people outside an organization, or trigger consequential actions.
- Task fit: What problem is the workflow meant to solve, and does it require AI?
- Autonomy and review: Which steps can the system take on its own, and where does a person review or approve its work?
- Data and permissions: What information can it access, and what systems or records can it change?
- Quality and security: How will output quality, failures, and security issues be monitored?
- Understanding and correction: Can users understand, challenge, or correct the output?
- Impact of error: What happens if the system makes a mistake, and is that consequence acceptable?
These checks do not guarantee a safe outcome. They help make the workflow’s purpose, authority, and review needs explicit.
How NIST frames AI risk management
NIST’s AI Risk Management Framework (AI RMF) 1.0, published in 2023, organizes risk management into four functions: Govern, Map, Measure, and Manage. The framework emphasizes characteristics of trustworthy AI, including validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy, and fairness. Read NIST’s AI RMF overview.
NIST released its Generative AI Profile, NIST AI 600-1, on July 26, 2024. The profile provides additional risk guidance for generative AI. NIST says AI RMF 1.0 is being revised, so treat the framework as an evolving resource rather than a final answer for every use case. Read the NIST Generative AI Profile.
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