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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteTraditional automation is usually the better starting point for repeatable tasks with structured inputs, explicit rules and outputs that can be checked. Generative AI is worth evaluating when work involves varied language or other content and a useful draft, summary or interpretation can be reviewed. Many workflows can use both: automation handles predictable routing and checks, while AI helps with variable content.
Choose at the task level, not by job title. The right fit depends on how variable the work is, how costly mistakes would be, how much review is needed, and who remains accountable.
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How to tell which approach fits a task
Use these characteristics as a practical guide, not as a guarantee about a particular tool. A task can be a good candidate for one approach even when other tasks in the same role fit the other.
| Task characteristic | Traditional automation is a stronger starting point when… | Generative AI is worth evaluating when… |
|---|---|---|
| Inputs | Inputs are structured and predictable. | Inputs are varied language or other content. |
| Rules | Steps and exceptions can be specified clearly. | A rigid rule set is cumbersome, but a useful draft or interpretation can be reviewed. |
| Output | The required result is consistent and testable. | More than one response could be acceptable, and a person can judge usefulness. |
| Volume | The same operation recurs often enough to justify automating it. | Variable cases consume time in reading, writing, summarizing or synthesizing. |
| Error handling | Errors can be caught with deterministic checks. | Uncertainty can be surfaced and a person can review the result before consequential action. |
| Accountability | Ownership and authorization are clear. | Human oversight remains available for judgments and high-impact decisions. |
This distinction reflects the task-level comparison in the OECD’s 2024 analysis: technologies that predate generative AI tend to excel at one or a few specific tasks, while generative AI can affect a broader range. The comparison does not establish a universal boundary or certify that a given system can perform a task reliably.
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Tasks that tend to fit traditional automation
Traditional automation is a natural candidate when a workflow follows known steps and can be checked against explicit conditions. Examples include:
- Moving records between systems.
- Applying defined validation rules to form fields.
- Sending routine notifications when a known event occurs.
- Routing forms based on specified fields.
- Generating standard reports from structured data.
These are illustrative task types, not product recommendations or claims about measured results. The practical advantage is that the rules and expected output can often be made explicit. If a key exception cannot be described or a person must interpret the substance of every case, rule-based automation alone may not be enough.
Rank #2
Tasks where generative AI may help
Generative AI is worth assessing when inputs vary and the desired result is a useful draft or interpretation rather than one predetermined output. Possible tasks include:
- Drafting or revising routine text.
- Summarizing long material for a person to review.
- Making a first-pass classification of unstructured messages.
- Helping generate or transform media.
The International Labour Organization’s 2025 update notes expanding model capabilities in voice, image and video generation, which changes the range of tasks with potential exposure. Capability is not the same as reliability: performance depends on the actual system and implementation, and the examples above are not evaluations of specific deployments.
When combining both approaches makes sense
A workflow can have predictable and variable parts. For example, a team might use conventional automation to receive and route submissions, apply rule-based checks to required fields, and then use generative AI to prepare a summary or draft response from variable text. A person can review the result before it triggers an important decision or external communication.
This is a practical design pattern, not a published case study or proven productivity result. Keep the handoff clear: automation should enforce the conditions it can check, AI-generated content should be treated as a candidate result, and people should own consequential judgments.
How to choose responsibly
- Break the job into tasks. Identify the repeated steps, the variable content, the decisions and the points where a person is already needed.
- Specify the acceptable result. If success can be expressed as explicit rules and checked consistently, start by considering traditional automation. If multiple outputs may be useful and need human judgment, assess whether generative AI can assist.
- Weigh the cost of mistakes. Define which errors are tolerable, how they will be detected, and what must happen when a result is uncertain. Do not let an unreviewed output trigger consequential action without a justified control.
- Account for data and ownership. Consider data sensitivity, who can authorize use, who reviews results and who is responsible when something goes wrong.
- Evaluate the whole workflow. Include review effort and failure handling, not just the time spent producing an initial result. A faster draft is not automatically a net improvement if checking it takes substantial effort.
NIST’s AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI design, development, use and evaluation. Its Generative AI Profile describes risks across the AI lifecycle and possible risk-management actions. These are governance references, not guarantees that a system will be safe, accurate or appropriate for a particular use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What worker-exposure figures do—and do not—mean
Exposure estimates describe potential task impact, not a count of jobs certain to disappear. The OECD’s 2024 analysis estimates that around 26% of workers across OECD countries are exposed under its defined measure: at least 20% of an occupation’s tasks could be performed in half the time with generative AI. That is a measure of potential task-time impact, not a job-loss forecast; see the OECD analysis.
The ILO’s 2025 update says one in four workers worldwide are in occupations with some degree of generative AI exposure, and concludes that most jobs are more likely to be transformed than made redundant because human input remains necessary. Its refined index draws on task-level data, expert input and AI predictions covering nearly 30,000 tasks. The reported mean automation scores—0.29 in 2025 and 0.30 in 2023—are methodology-based exposure scores, not realized productivity gains or job-loss rates. See the 2025 update and the ILO working paper.
The ILO also cautions that employment outcomes depend on how technology is integrated into work and whether management retains people to perform or oversee tasks. Its artificial intelligence topic page frames this as a choice between automation and augmentation, rather than an outcome determined by capability alone.
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