Business Continuity Planners
In short: today's best AI models could do about 43% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 91% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 8% of the time, stays with people.
Develop, maintain, or implement business continuity and disaster recovery strategies and solutions, including risk assessments, business impact analyses, strategy selection, and documentation of business continuity and disaster recovery procedures. Plan, conduct, and debrief regular mock-disaster exercises to test the adequacy of existing plans and strategies, updating procedures and plans regularly. Act as a coordinator for continuity efforts after a disruption event.
$83,050 a year (for the broader BLS group this job sits in).
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work17 h
- Desk work AI can't do yet20 h
- Stays with people3 h
Business Continuity Planners, now: of a 40-hour week, AI could take on 0 hours, 17 hours become checking its work, 20 hours are desk work it can't do yet, and 3 hours stay with people.
The largest pieces: Identify risk improvements (about 3 h, a person checks); Analyze threat intelligence (about 3 h, a person checks); Write emergency management plans (about 2 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 83%
- 9%
- 7%
- 1%
- 0%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 43% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 2%. Today's estimate: 43%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.11% went to this job's tasks in May 2026, the 155th largest of the 447 jobs that showed up at all; 3 of its 21 tasks showed up. Within each of Anthropic's methods, never across a change: 0.12% to 0.1% (released February and March 2025); 0.1% to 0.075% (August 2025 to February 2026); 0.1% to 0.11% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.12% | 147 of 708 | 9 of 21 |
| Released March 2025 | 0.1% | 157 of 703 | 8 of 21 |
| The week of Aug 4, 2025 (released September 2025) | 0.1% | 149 of 655 | 7 of 21 |
| The week of Nov 13, 2025 (released January 2026) | 0.063% | 212 of 672 | 6 of 21 |
| The week of Feb 5, 2026 (released March 2026) | 0.075% | 200 of 677 | 7 of 21 |
| April 2026 (released June 2026) | 0.1% | 160 of 434 | 3 of 21 |
| May 2026 (released June 2026) | 0.11% | 155 of 447 | 3 of 21 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates almost none of this job's working time as physical work, so robots do not reach it.
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
7% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Write emergency management plans, the conversation, 4%
- Integrate contingency plans, the conversation, 2%
- Direct intelligence collection, the conversation, 1%
- Keep up with the field, the in-person work, 0.7%
What becomes checking the machine
89% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it.
- Write emergency management plans, severe if wrong, 16%
- Build risk mitigation tools, severe if wrong, 12%
- Analyze threat intelligence, severe if wrong, 12%
- Identify risk improvements, severe if wrong, 11%
- Set up interruption monitoring, severe if wrong, 8%
- Analyze business impact, severe if wrong, 8%
What to learn
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- Briefing a model and checking its drafts. About 83% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 21 tasks from .
| Task | ||||
|---|---|---|---|---|
| Develop emergency management plans for recovery decision making and communications, continuity of critical departmental processes, or temporary shut-down of non-critical departments to ensure continuity of operation and governance.Plans written at the desk, with the departments consulted.Done more than yearly by most who do it. | 20% | 2.1 work days6.7 h to 4.2 work days | Documents 80%, conversation 20% | 28%Desk part half by Jan 2027 |
| Design or implement products and services to mitigate risk or facilitate use of technology-based tools and methods.Design on paper and implementation in the systems.Done more than weekly by most who do it. | 13% | 3.3 work days6.7 h to 2.1 work weeks | Documents 60%, systems 40% | 10%Half by Oct 2027 |
| Analyze corporate intelligence data to identify trends, patterns, or warnings indicating threats to security of people, assets, information, or infrastructure.Analysis at the desk.Done more than monthly by most who do it. AI observed on this task. | 12% | 6.7 h1.7 h to 2.5 work days | Documents 100% | 55%Half by now |
| Identify opportunities for strategic improvement or mitigation of business interruption and other risks caused by business, regulatory, or industry-specific change initiatives.Analysis at the desk.Done more than yearly by most who do it. | 11% | 1 work day3.3 h to 2.1 work days | Documents 100% | 76%Half by now |
| Recommend or implement methods to monitor, evaluate, or enable resolution of safety, operations, or compliance interruptions.Methods designed on paper and set in the systems.Done more than monthly by most who do it. | 8% | 6.7 h1.7 h to 2.5 work days | Documents 80%, systems 20% | 55%Half by now |
| Analyze impact on, and risk to, essential business functions or information systems to identify acceptable recovery time periods and resource requirements.Analysis at the desk.Done more than monthly by most who do it. | 8% | 1.7 work days4.2 h to 4.2 work days | Documents 100% | 36%Half by Jan 2027 |
| Develop disaster recovery plans for physical locations with critical assets, such as data centers.Plans written, with the systems inventoried.Done more than yearly by most who do it. | 7% | 2.5 work days1 work day to 1 work week | Documents 80%, systems 20% | 25%Half by Mar 2027 |
| Conduct or oversee contingency plan integration and operation.Coordination with the departments and the plans aligned.Done more than yearly by most who do it. | 4% | 1.7 work days3.3 h to 4.2 work days | Documents 60%, conversation 40% | 19%Desk part half by Mar 2027 |
| Conduct or oversee collection of corporate intelligence to avoid fraud, financial crime, cyber attack, terrorism, and infrastructure failure.Directing the collection, with the analysis.Done more than monthly by most who do it. | 4% | 6.7 h2.5 h to 2.5 work days | Documents 70%, conversation 30% | 48%Desk part half by now |
| Create scenarios to reestablish operations from various types of business disruptions.Scenarios written at the desk.Done more than yearly by most who do it. | 3% | 1 work day3.3 h to 2.5 work days | Documents 100% | 69%Half by now |
| Write reports to summarize testing activities, including descriptions of goals, planning, scheduling, execution, results, analysis, conclusions, and recommendations.Writing at the desk.Done more than yearly by most who do it. | 2% | 6.7 h2.5 h to 2.1 work days | Documents 100% | 76%Half by now |
| Maintain and update organization information technology applications and network systems blueprints.Keeping the diagrams current.Done more than yearly by most who do it. | 2% | 6.7 h1.7 h to 2.5 work days | Documents 70%, systems 30% | 55%Half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
Where this job works
The industry groups that employ the most of it (BLS, May 2025). Counts are for the broader BLS group this job sits in.
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025). Counts are for the broader BLS group this job sits in.
Questions about this job
- How much of the work of Business Continuity Planners is within reach of AI now?
- An estimated 43% of the working time is within reach of AI models now and 91% by the end of 2028, on METR's long-run pace at four in five (84% to 92% across the three paces the site shows). Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Business Continuity Planners come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Identify risk improvements (76% of the task's time now, 100% by the end of 2028); Analyze threat intelligence (55% of the task's time now, 100% by the end of 2028); and Write emergency management plans (28% of the task's time now, 80% by the end of 2028).
- Which tasks of Business Continuity Planners stay with people?
- Every task has a desk part, so each can come partly within reach; the conversation, in-person and hands-on parts of each task stay with people, about 8% of the working time.
- What should Business Continuity Planners learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are reviewing work you did not do yourself; judgment where errors are severe; and briefing a model and checking its drafts. By the end of 2028 on the long-run pace at four in five, about 7% of the working time stays with people, 89% becomes checking a model's work and 1% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Business Continuity Planners?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.11% went to this job's tasks, the 155th largest share of the 447 jobs that showed up; 3 of its 21 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 18%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Business Continuity Planners?
- Anthropic's robot exposure rating (September 2026) finds almost none of the working time is physical work, so robots do not reach it.
- How is this estimated?
- From the 21 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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