AI automation uses artificial intelligence within a process to interpret information, make or support decisions, and sometimes carry out tasks. It can mean anything from AI suggesting a reply for a person to an agent taking several steps across connected systems. The amount of human oversight varies; an AI-enabled process is not automatically autonomous.
What is AI automation?
“AI automation” is a practical label, not one universally agreed formal term. NIST’s AI glossary collects definitions 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.
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Put simply, AI automation applies AI capabilities to part of a workflow. The AI might interpret a message, recommend what should happen next, or take an action. A person may approve each result, review only exceptions, or oversee a more autonomous sequence. What the system can do depends on how it is designed and what information and permissions it receives.
How is AI automation different from conventional automation?
Conventional automation generally follows explicitly defined rules: when a specified condition is met, perform a specified action. AI-enabled automation can add a step that interprets less-structured input or generates a recommendation, such as identifying the topic of a message before routing it.
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This is a useful distinction, not a claim that all AI learns, makes decisions independently, or acts without review. AI systems differ in their capabilities and autonomy, and the surrounding workflow determines whether a person approves their output or the system proceeds to act.
What are examples of AI automation?
NIST describes organizations using AI agents for information retrieval and workflow automation, as well as software development and cybersecurity operations. These are examples of possible applications, not guarantees of accuracy or productivity.
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Finding and organizing information
An AI system can help retrieve or interpret information so a person can use it in a workflow. The system’s output still needs appropriate checking, especially when a wrong answer could lead to a consequential decision.
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. These capabilities can support development tasks; they do not establish that generated code is secure or correct without review.
Carrying out workflow steps
An AI agent may retrieve information or perform steps in connected systems as part of a workflow. The more it can change records, communicate with people, or trigger other actions, the more important it is to consider its permissions and the consequences of an error.
What can AI automation help with—and when might it not fit?
AI may support efficiency, productivity, or decision-making, but results depend on the task and how the system is deployed. NIST’s AI Risk Management Framework guidance cautions that AI may not be the right solution for a particular business problem. Assess whether AI serves the intended objective and weigh expected benefits against possible negative impacts rather than assuming automation will save time or money.
Before choosing an AI-enabled approach, consider:
- Task fit: What problem needs solving, and does it require AI, or would a clear rule-based process work?
- Autonomy: What can the system do on its own, and where does a person review or approve its work?
- Data and access: What information does it use, and which systems or actions can it access?
- Quality and monitoring: How will you spot inaccurate outputs, failures, or security issues?
- Understandability and correction: Can people understand, challenge, and correct its outputs?
- Error impact: What would happen if it made a mistake?
What are the risks, and how can they be managed?
Potential risks include inaccurate outputs, insecure code, unauthorized actions, data leakage, limited explainability, and excessive reliance on automated results. NIST’s Generative AI Profile (NIST AI 600-1), released July 26, 2024, describes “automation bias”: people’s excessive deference to automated systems. That deference can compound risks such as confabulation and bias.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteMatch safeguards to both the system’s capabilities and the consequences of mistakes. In particular, give an agent only the access its task needs, and set human review in line with the impact of an error. A workflow that can modify records, contact people outside an organization, or trigger consequential actions calls for careful oversight. These are practical risk-management measures, not a guarantee that a system will be safe.
NIST’s AI Risk Management Framework (AI RMF) 1.0, published in 2023, organizes risk work around four functions:
- Govern: Establish accountability and oversight for AI risks.
- Map: Understand the system’s context, intended use, and potential impacts.
- Measure: Assess relevant risks and system characteristics.
- Manage: Prioritize and address risks over the system’s lifecycle.
The framework also emphasizes characteristics such as validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy, and fairness. NIST says AI RMF 1.0 is being revised, so treat it as an evolving framework rather than a static final word.
Quick Recap
How should you decide whether to automate a task with AI?
- Define the task and intended outcome. Identify the problem and decide what a successful result means.
- Check whether AI is needed. Compare an AI-enabled workflow with a simpler rule-based process against the same objective.
- Set the system’s boundaries. Specify what information it can use, what actions it may take, and which steps require human approval.
- Consider failure consequences. Identify how an inaccurate output, data exposure, or unintended action could affect people or operations.
- Plan review and monitoring. Decide how to check output quality, detect failures, and correct or challenge results.
- Reassess as use changes. Review risks when the workflow, permissions, systems, or context change.
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