How to Prepare Your Task List for the AI Proof Work Checker

How should I prepare my task list before using the AI Proof Work checker?

Before opening the checker, sketch the work you actually perform across a representative recent week or month. Include recurring duties and occasional responsibilities with high consequences. Describe each as an action and an output: for example, reviewing a request and producing a summary. Add rough frequency, then note context that changes the work, such as exceptions, checking, handoffs, physical steps, or accountability. Keep estimates approximate; the goal is a useful map, not a time study. Treat your job title as a clue, not a substitute for this inventory. O*NET OnLine Help: Job Duties Custom List describes selecting task statements you perform now or want in a future role from an occupation-specific database. Use a library to prompt memory and spot omissions, then edit generic wording to match your work. Generative AI and jobs: A 2025 update, an ILO brief, assesses exposure at occupational level across nearly 30,000 tasks and accounts for variation among them. That supports looking below the title, but does not identify your employer’s plans or predict your job outcome. Split an activity only when parts have different outputs, constraints, or checks; otherwise keep connected work together. Note observed AI use separately from possible capability.

Sources: O*NET OnLine Help: Job Duties Custom List; Generative AI and jobs: A 2025 update

Why is a job title a weak substitute for your own task list?

A job title is a useful starting label, but a poor substitute for the work itself. “Project manager,” “analyst,” or “operations lead” tells you roughly where a role sits; it does not show which outputs you produce, how often you handle exceptions, who depends on your work,. For a checker focused on task-level change pressure, start from what you actually do. Then consult an occupational task list to catch duties you forgot, not to let a generic profile stand in for your own workload. That distinction is built into the way O*NET OnLine describes its Job Duties Custom List. The U.S. occupational database says the feature draws on more than 19,000 occupation-specific task statements. A user starts with a current occupation title, selects statements they perform now or would like to perform in a next job, and uses those selections to find occupations with related tasks. This is a memory and comparison aid: the title helps you reach candidate duties, while selected tasks describe similarity. It is not a diary of your week, a personal exposure rating, or a forecast of what your employer will do. Use the same sequence when preparing your own list. First, write the task in language tied to an output: “prepare the monthly variance summary from approved figures” is more useful than “finance” or “reporting.” Then ask whether the phrase hides work that changes the output, its constraints, or the checks required before someone can rely on it. If preparing the summary and explaining an unusual variance involve different deliverables or review, record them as distinct tasks. If the steps are inseparable parts of one recurring output, keep them together and note the context. Make your work legible without turning every click into a separate item. The International Labour Organization’s “Generative AI and jobs: A 2025 update” shows why an occupation label can conceal meaningful variation. Its global assessment works at the six-digit occupational level across nearly 30,000 tasks, combining task-level data, expert input, and AI predictions. The brief says its four exposure gradients depend on both mean exposure and the degree of task variability within an occupation. The method does not treat tasks inside a broad category as interchangeable. So describe your role's tasks before interpreting an occupation-level signal. The ILO estimates potential occupational exposure; it does not measure your personal task mix, establish adoption at your workplace, or provide your probability of displacement. O*NET’s task statements likewise support classification and recall, not an AI assessment. For your list, translate broad profile language into action and output, preserve distinctions where the work or its checks differ, and mark uncertainty rather than filling it with a guess. The result is a more faithful description to inspect. It cannot make a checker an objective account of an unedited job profile or tell you what staffing decision will follow.

Sources: O*NET OnLine Help: Job Duties Custom List; Generative AI and jobs: A 2025 update

What makes one task description specific enough to inspect?

A task description lets another person picture the work. Start with an action verb, name the input or object, and state the output or purpose. The U.S. Office of Personnel Management’s Job Analysis presentation gives a practical formula: perform what, to whom or what, to produce what or why. It is a starting point, not a required checker format. For example, “reporting” is too broad to show whether you assemble figures, interpret an exception, or explain a result. Imagine a recurring task described as “prepare a variance summary from approved figures for a department lead, explain unusual entries, and flag items needing follow-up.” The verb and output identify the production step; the audience and exception note show what makes the result useful. This wording is illustrative, not evidence about any workplace. Add context only when it changes the work, its output, or how someone must check it. Ask what input the task uses, who relies on the result, what exception changes the next step, what must be verified, and who remains responsible. Brief phrases can answer them. Omit routine details, software clicks, and general traits such as “good communication” when they do not clarify the work. O*NET OnLine Help: Job Duties Custom List says its feature draws on more than 19,000 occupation-specific task statements and lets users select duties they perform now or want in a next job. Examples use action-and-object wording. O*NET can prompt you to find a missing duty, but its standardized entries do not describe the inputs, exceptions, and checks in your own setting. To decide whether to split an entry, look for a meaningful difference in output, method, constraint, or accountable review. Suppose a process includes extracting figures, interpreting an unusual result, discussing it with a stakeholder, and approving a final version. If stages produce different deliverables or have different checks, separate entries may clarify them. If separating them would hide the actual output, keep them together and name the stages briefly. Do not maximize row count. The U.S. Office of Personnel Management’s guidance recommends concise task statements, warns against combining multiple tasks, and cautions against entries so specific they name unnecessary mechanics. It advises replacing vague judgments with observable actions such as “verify.” Thus “carefully prepares reports” is less inspectable than “checks totals in a monthly expense summary before sending it to the finance lead.” The second names an action, object, and handoff without listing keystrokes or claiming the task is automated, exposed, or safe. Use a compact pattern: “I [action] [input or object] to produce [output] for [recipient or purpose]; when [material exception], I [check or decision], and [role] owns the result.” Omit parts that add no useful distinction. If a phrase describes a separate output or review responsibility, consider a separate task; if it only lists mechanics of one continuous activity, keep it concise. This pattern makes work easier to examine. It does not mean the checker requires this wording, accepts a particular template, or can infer employer plans from it.

Sources: O*NET OnLine Help: Job Duties Custom List; Job Analysis

How much frequency detail is useful without creating false precision?

For a preparation list, ordinary frequency language is usually enough: “several times a day,” “weekly,” “monthly,” or “occasionally.” If memory or a calendar supports a rough share of a normal week or month, label it approximate. Do not turn recollection into a precise percentage just because a number looks objective. This shows whether a duty is routine, periodic, or unusual. The checker’s product brief does not say frequency is an input or scoring weight; these notes are for reflection, not a claim about its formula. Keep frequency distinct from importance. The Office of Personnel Management’s *Job Analysis* presentation describes them as separate task-rating criteria: frequency asks how often work is performed; importance asks how much it matters to doing the job successfully. Its example frequency scale ranges from every few months or yearly to many times each hour or hourly, while a separate scale rates importance. These are examples for formal job analysis, not a required system for checker preparation. They show why “rare” does not mean “irrelevant.” An occasional approval, exception review, or incident response may carry substantial responsibility despite taking little time. Note its infrequency and why it matters, without inventing a score. The Bureau of Labor Statistics’ *Occupational Requirements Survey Collection Manual* also treats less-than-daily activities as worth representing. Its formal instructions discuss annual, seasonal, monthly, or weekly activities and estimating hours or time share when collecting duration data. That supports a useful distinction, not a requirement to conduct a time study here: BLS procedures serve a structured occupational survey, while your goal is a credible personal account. A short look at a representative calendar period can jog memory about recurring meetings or deadlines; a diary of every interval is unnecessary. Choose a period resembling your ordinary workload, such as a typical week, and mark it unusual if it included a launch, leave, incident, or seasonal rush. If work changes substantially by month or quarter, list the periodic duty separately rather than pretending one week represents it. Use broad labels when timing is unclear: daily, weekly, monthly, seasonal, or rare but consequential. If you have a reasonable estimate, state “about” and name the period, such as “roughly one afternoon in a typical month.” This communicates scale and uncertainty together. Describe the duty, note how often it normally recurs, and separately flag unusual work that matters because of its consequences. Do not treat frequency as importance, exposure, or job security, or present a percentage as the checker’s weighting. If your sample period was atypical, say so; a transparent estimate is more useful than unsupported precision.

Sources: Job Analysis; Occupational Requirements Survey Collection Manual

What work can disappear from the list when a task is described too broadly?

A label such as “prepare the monthly customer report” can make different work look like one repeatable activity. It may cover collecting approved figures, resolving gaps, drafting a summary, checking an unusual result, explaining it to a colleague, and routing the final version. Some steps may be easier to assist with than others. If the list records only “prepare report,” routine production is visible, while exceptions, review, and handoffs disappear. Keeping follow-on work visible does not prove those duties will remain unchanged. The inventory shows what to examine; it does not label tasks protected. The OECD report The Impact of AI on the Workplace: Main Findings from the OECD AI Surveys of Employers and Workers offers a check on the idea that an automated task simply vanishes. Fielded in early 2022, its surveys covered 5,334 workers and 2,053 firms in finance and manufacturing across seven countries. Among AI-adopting employers, 66% in finance and 72% in manufacturing reported that AI had automated tasks workers used to do; around half in each sector also reported tasks created by AI. These are employer reports from selected sectors, not counts of hours removed or jobs eliminated. The report says it did not know the relative time and importance of automated and created tasks, so the findings cannot be treated as a net balance. The report illustrates the point with a chatbot that answers simple customer requests, while an employee’s freed time goes to monitoring its output, maintaining and training the software, and solving problems. This does not establish that every chatbot creates those duties, that they take as long as the original requests, or that every worker receives them. It does show why “answer customer questions” can be too broad. A more revealing inventory distinguishes routine responses from reviewing uncertain answers, handling unresolved cases, and taking responsibility for the customer’s next step, when those parts belong to the role. The OECD Employment Outlook 2023 chapter “Artificial Intelligence, Job Quality and Inclusiveness” adds a related observation. Its review of the surveys and workplace case studies reports that workers often described fewer tedious or repetitive duties alongside more complex non-routine work. It discusses a UK financial firm where AI-assisted systems reduced simple administrative work and employees described more time supporting customers and colleagues, and doing research, planning, and project management. These are selected implementation examples, not predictions for another workplace. The chapter also cautions that automating easier tasks can leave remaining work more intense rather than simply making the day lighter. For your list, add a second layer beside each production task: what happens when input is incomplete, who checks the result, what is handed off, and who remains accountable. Include monitoring, maintenance, training, or problem-solving only when you perform or have directly observed them; do not add speculative duties because a report mentions them. This layer may show work that deserves separate inspection, or that connected steps are better kept together. It cannot establish whether your employer adopted a system, redesigned the workflow, or will change staffing. It sharpens the task question while leaving those separate questions open.

Sources: The Impact of AI on the Workplace: Main Findings from the OECD AI Surveys of Employers and Workers; Artificial intelligence, job quality and inclusiveness

Which context should you record beside a repeatable task?

Beside a repeatable task, record the conditions that change its inputs, method, checking, or consequences. A short note can cover whether information arrives complete and in a permitted form, whether local knowledge or a client conversation is needed, whether someone must act physically, what unusual cases arise, what quality threshold applies, and who remains responsible for the result. A routine digital step may be easier for a tool to assist with, but context is not a shield: human involvement and accountability do not make a task immune to change. A useful way to keep the note compact is to ask four questions: What comes in? What changes the normal path? What must be checked before the output is used? Who owns the decision or consequence? They make visible the boundary between producing an output and making it usable. For instance, preparing a summary from approved information may be repeatable; resolving an ambiguous entry, explaining its significance to a client, and confirming the final action can involve different constraints and review. The OECD’s 2023 Employment Outlook chapter on artificial intelligence, job quality and inclusiveness supports recording work conditions because reported effects were mixed. Its evidence synthesis includes 2022 online worker surveys in finance and manufacturing, employer surveys of firms with at least 20 employees, and 90 firm case studies selected for AI implementation experience. The chapter describes reports of tedious work being reduced alongside concerns such as work intensity, privacy, and autonomy. The findings describe reported effects on task content and work quality, not what an individual employer will adopt or how a specific role will change. Evidence is sector-specific and largely self-reported, not a representative forecast. The OECD’s report on its 2022 AI surveys of employers and workers adds a useful reason to keep adjacent work visible. Among surveyed AI-adopting employers in finance and manufacturing across seven countries, respondents reported both task automation and task creation. The report describes monitoring, maintenance, and problem-solving as work that can accompany AI use, while cautioning that it did not know the relative time or importance of tasks. Counts of employers reporting automation or new tasks therefore do not establish whether workload rose or fell, or whether new work offset automated work. Note added checking, handoffs, or follow-up you have observed, without assuming they offset another change. Use context to frame a better question about a task’s inputs, exceptions, verification burden, and ownership, based on what you know. Do not turn the notes into resilience points or a promise of protection. They help interpret a task-level change-pressure signal, not predict employer decisions or personal employment outcomes.

Sources: Artificial intelligence, job quality and inclusiveness; The Impact of AI on the Workplace: Main Findings from the OECD AI Surveys of Employers and Workers

How should you separate what you have seen from what a tool might do?

Keep two notes per task: what a capable tool might assist with, and what you have actually seen at work. For the second, write “observed,” “not observed,” or “unknown,” adding a date or safe example. This prevents technical possibility from becoming a claim about your employer’s plans. A checker signal prompts inspection, not a personal job-loss probability.

The terms describe different stages. Capability is what a system can perform in specified conditions. Exposure asks whether tasks could be affected. Actual use is what a person or team has used. Adoption is organizational use; redesign changes tasks, workflow, or responsibility. Labor demand is employer demand for workers; displacement means work is reduced or lost. They are not interchangeable. Exposure does not show local adoption, and adoption alone does not establish what will happen to staffing.

The International Labour Organization’s “Generative AI and Jobs: A 2025 Update” shows an exposure estimate’s unit. Its global assessment covers nearly 30,000 tasks at the six-digit occupation level, using task data, expert input, and AI predictions. Its four gradients combine average exposure with variation among tasks. The brief reports one in four workers worldwide are in an occupation with some exposure, while most jobs are more likely to be transformed than made redundant because human input remains necessary. These modeled estimates describe occupations, not an individual’s employer or future. They show why task mix matters, but cannot show whether a manager adopted a tool.

The OECD’s employer and worker surveys differ. Its project page says the first wave, in early 2022, surveyed 5,334 workers and 2,053 firms in finance and manufacturing in seven countries. Worker responses capture first-person experience; employer responses address how and why AI is implemented. The page says surveys were repeated in 2026 with revised questionnaires and wider coverage, but gives no results from that wave. Earlier findings clarify evidence types; they are not current adoption rates for every sector.

“OECD Employment Outlook 2023” shows why use needs context. Among surveyed finance workers interacting with AI, 85% of those subject to algorithmic management reported a faster work pace, compared with 74% of other AI users. In manufacturing, shares were 76% and 78%. These sector self-reports are not universal rates or proof AI caused each change. They show why “a tool is present” is incomplete: note known changes in task or conditions, and label the rest unknown.

A platform example draws another boundary. “How AI Is Expanding What People Do at Work” analyzes more than 800,000 messages from U.S. ChatGPT users. It reports 43.5% of non-generic, occupation-specific messages concerned other-occupation tasks. This describes usage on one service, not a representative worker census, approved deployment, or hiring change. It suggests titles can lag tasks explored with a tool; it does not show employers assigned or replaced duties.

Write notes such as “tool could draft a first summary; workplace use unknown,” or “team used an approved system in June; reviewer checks exceptions.” Include supportable observations; leave uncommunicated plans blank. Do not put confidential, client, personal, or regulated information into an unapproved system. Your observation clarifies workflow, not an organization’s next move. Keep the checker’s change-pressure signal separate from adoption, redesign, labor demand, and displacement.

Sources: Generative AI and jobs: A 2025 update; AI surveys of employers and workers; Artificial intelligence, job quality and inclusiveness; How AI Is Expanding What People Do at Work

Should I list every step, or only the duties in my job description?

Neither extreme gives a useful picture. A job description can miss work that has become routine, while a list of every click can make connected work look like dozens of unrelated duties. Start with task types that recur in a representative recent week or month, add occasional duties whose omission would conceal meaningful risk or responsibility, and include informal work such as troubleshooting or helping a colleague when it is genuinely part of the role. The aim is to describe the work well enough to inspect, not to create a minute-by-minute record. Group routine repeats, but retain unusual cases that change review. A title-first approach is quick, but it answers a classification question rather than “What do I actually do?” O*NET OnLine’s Job Duties Custom List illustrates a useful middle ground: it offers occupation-specific task statements and asks users to select those they perform now, or would like to perform in a next job, to find related occupations. That makes the library a recall prompt, not a replacement for the worker’s own inventory. A recent-work approach starts from evidence the worker can safely recall: a calendar, a task tracker, or a representative stretch of completed work. It can capture informal handoffs and recurring requests that a job description leaves out. But one unusual week may overstate a temporary project, while a quiet period may conceal seasonal responsibilities. Look across a normal period, then deliberately ask whether infrequent approvals, incident response, or other consequential duties belong on the list. At the other end, an exhaustive step log takes effort and can create false clarity. Split a duty when its parts produce different outputs or face meaningfully different constraints or review. Keep the parts together when separating them would disguise what is delivered or who is responsible. The U.S. Office of Personnel Management’s Job Analysis presentation describes tasks as activities performed regularly to carry out a job’s functions and offers a concise writing pattern: perform what, to whom or what, to produce what or why. Formal titles can also lag work that crosses role boundaries. In “How AI Is Expanding What People Do at Work,” OpenAI Economic Research analyzed more than 800,000 messages from U.S. ChatGPT users and reported that 43.5% of occupation-specific messages concerned tasks associated with another occupation after generic tasks were excluded. This is a bounded observation of messages on one service, not a representative estimate of all workers, employer adoption, or job change. For your list, ask: if I left this duty out, would I materially misdescribe my work, its output, its risk, or the time it takes? If yes, include it; if not, leave it out or group it with the connected task.

Sources: O*NET OnLine Help: Job Duties Custom List; How AI Is Expanding What People Do at Work; Job Analysis

What can the checker tell you after you prepare the list?

The free AI Proof Work checker is a starting point, not a forecast about your employment. The supplied product brief describes transparent task-level signals and first actions. The live checker page presents a U.S. job-market map and labels its estimates as rough indications of how AI may reshape work, with the boundary “Exposure is not replacement.” Use it to identify work worth examining, not to produce validated personal odds, infer an employer’s plans, or rate job security. Neither the page nor the brief establishes that you can upload a prepared task list or specifies a scoring formula. Treat your list as a preparation aid for interpretation, not as a promised checker feature. [AI Proof Work checker](https://aiproofwork.com/ai-job-risk-checker)

Capability concerns what a technology may be able to do; exposure asks whether that capability applies to tasks like yours. Neither establishes that your employer adopted a tool, changed a workflow, reduced staffing, or plans to do so. Even observed use would not show whether work was redesigned, quality improved, time was saved after checking, or labor demand changed. Displacement is a further outcome, not another name for exposure. The occupation map is a reference; a task-level signal, as described in the brief, is also a prompt for review rather than a measurement of your employer’s decision. A inventory can clarify your work, but it cannot supply evidence about decisions you cannot observe.

Use a result to select a question, not jump to a conclusion. If a recurring task appears relevant, ask what output it produces, what quality standard applies, how much checking or exception handling is required, and whether an approved tool is in use. If you do not know, keep that uncertainty explicit. A small, policy-compliant review of a non-confidential example may clarify the workflow; it does not establish a market-wide trend or guarantee that the task will change. Do not paste client, personal, regulated, or employer information into an unapproved system. If restructuring has been announced, seek direct workplace information and support; a public estimate cannot confirm what will happen to your role.

The practical boundary is simple: prepare a representative list, consult the checker’s stated signal, then verify consequential claims with evidence from your workplace and labor sources. When the question is broader than one task—whether to redesign your role, explore an adjacent move, or consider a larger change—the paid career roadmap is described as comparing scenarios against your experience and constraints and providing a 30/60/90-day plan. It does not guarantee employment or income. use the [free AI Proof Work checker](https://aiproofwork.com/ai-job-risk-checker); let its signal guide what to investigate, while grounding decisions in verifiable evidence.

Sources: AI Proof Work checker

What is the next proportionate step after the check?

Choose one recurring task from your list that you can examine without exposing sensitive information or disrupting work. Note its intended output and quality standard. Then identify a safe example, a public or synthetic test case, or a specific question for a manager or colleague: where could assistance fit, what needs checking, and who remains responsible? If a trial is permitted, compare the result with the standard and include time spent reviewing errors and handling exceptions. A faster first draft is not useful if follow-up erases the gain or quality falls. Follow workplace policy; do not put confidential, client, personal, or regulated material into an unapproved tool.

If restructuring is confirmed or imminent, seek information about the change and workplace support; a checker cannot tell you what your employer has decided. The free [AI Proof Work checker](https://www.aiproofwork.com/ai-job-risk-checker) offers task-level change-pressure signals and first actions, according to the publication’s product brief. Treat its result as a reason to investigate one task, not as validated odds of displacement or a reason by itself to abandon your experience or spend heavily on training. Decide what to do next when you have evidence about the task, its checks, and the workplace context.

Sources: AI Proof Work checker

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