Career readiness
Is Your Role AI-Ready? A Task-by-Task Self-Assessment
To assess AI readiness, examine specific tasks, approved data and your ability to check the result. Use this worksheet to choose a learning priority; a job title alone does not establish replacement risk.
Readiness, exposure and replacement are different questions
A task can be exposed to AI assistance without disappearing. A tool may generate part of the output while a person remains responsible for decisions, relationships, quality and exceptions. Your readiness concerns how well you can use that assistance in your own context.
The World Economic Forum’s 2025 employer survey estimated that 39% of workers’ existing skill sets would change or become outdated during 2025–2030. This is a forecast about skills. It does not mean 39% of jobs will disappear, and it cannot establish an individual person’s replacement risk.
This worksheet is an editorial self-assessment created by AI BURRO. It is not a validated benchmark, hiring assessment or prediction. Use it to make your next learning decision more concrete.
List the work behind your job title
Write down ten recurring tasks from a typical week. Include the work that consumes time but rarely appears in a job description: checking information, coordinating people, resolving exceptions and preparing a decision for someone else.
For each task, name the output and the consequence of an error. Drafting an internal outline has a different risk profile from approving a contract, choosing a job candidate or giving a customer a commitment. This distinction matters more than a generic list of AI tools.
If you are exploring a new profession, use public role descriptions and conversations with people doing that work. Label assumptions instead of presenting them as your own experience.
- What do I produce or decide?
- What information do I need, and may I use it in an AI tool?
- Who relies on the result?
- What must a human review before the result is used?
Look for assistance before assuming automation
Consider whether AI could help organize, summarize, draft, compare or explain information. Then identify the parts that require your industry knowledge or judgment. “Could assist” is a useful hypothesis; “can replace the role” is a much larger claim requiring evidence.
For a business-development professional, a small experiment might be structuring a public company-research brief. The human still checks the sources, understands the account and decides whether the proposed next step makes sense. This example is illustrative and does not claim that any particular workflow improves conversion.
Some tasks should remain outside your experiment because of data restrictions, material consequences or lack of a reliable reviewer. Excluding a task can be sound judgment, not a lack of ambition.
Check the conditions around the tool
Readiness depends on more than your ability to write a prompt. Do you have approved access? Are the inputs permitted? Can you recognize an incorrect answer? Does the team have a process for escalating uncertainty?
Gallup’s workplace research associates AI use with organizational support and fit with existing work. Those findings support examining the environment around a task. They do not provide a formula for scoring an individual’s readiness.
Record a constraint when it blocks responsible use. A missing data policy is an organizational issue; a weak review method may be a learning need. Keeping these separate produces a more useful development plan.
Source: Gallup, April 12, 2026.
Use evidence levels instead of a made-up score
Assign a qualitative level to each task. “Not tried” means you have not tested it. “Practiced” means you completed a controlled exercise. “Repeatable” means you can explain and reproduce the process. “Reviewed in real work” means an authorized workflow has been used and checked in its actual setting.
These labels describe your evidence, not your intelligence, employability or value. Do not add the labels together to produce a percentage that looks scientific. Different tasks carry different responsibilities and may not be comparable.
| Task | Evidence level | Constraint | Next action |
|---|---|---|---|
| Public account-research brief | Not tried | Need a source-checking method | Practice with public sources |
| Internal process draft | Practiced | Needs team review | Agree a review checklist |
| Customer commitment | Not assessed | Material consequence | Keep human decision and approval |
Worked example: account research in business development
Illustrative completed row: task — prepare a public prospect brief; evidence — one practice brief; constraint — the draft confused a company’s reseller with its parent; next action — verify ownership and each factual claim against dated primary sources before using the brief.
The learning decision is to improve source checking, not to label the whole profession safe or at risk. Keep the task at “Practiced” until you can repeat the process and explain the corrections. Move it to “Reviewed in real work” only after an authorized workflow has actually been checked in that setting.
A successful repeat would provide a reviewed brief, traceable source links and a correction log. Those artifacts could support a stronger evidence level; a polished AI answer alone would not. This example describes a learning exercise, not Edgar’s performance or an observed customer outcome.
Choose one learning experiment for the next month
Select a task with useful potential and manageable consequences. Define what you will practice, which sources or tools are approved and how another person could check the result. Keep a record of errors and corrections as well as successful outputs.
A learning plan can combine a short course, a realistic exercise and feedback from someone who understands the work. Choose training based on the gap you identified. A general introduction may help a beginner; someone with a repeatable workflow may need deeper evaluation or industry-specific practice.
After the experiment, update the evidence level and decide what comes next. If a tool was not useful, record why. A clear limit is more actionable than an impressive-looking report with unsupported predictions.
Role and industry intelligence adds context when it distinguishes dated evidence, uncertain projections and personal interpretation. Keep those distinctions in your own profession brief, and use your AI BURRO account to review report access.