Will AI take my job?
A task ratio cannot predict what happens to an individual job. AI may change or remove roles, but outcomes depend on how the organization redesigns work, its demand and operating choices. Map your actual responsibilities, review what may change with your employer and prepare for the work you will need to do.
Explore the work. Discuss your role.
Task Intelligence helps explain potential AI contributions underneath roles and workflows. Browse the public data, then discuss your actual responsibilities, approved tools and human decisions with your organization. Detailed preparation plans are available in the tenant workspace.
People are already mapping their roles
Task changes do not predict a job outcome.
Look at the actual work and the decisions your organization is making about it.
A role combines activities, decisions and relationships. Classifying those activities can help propose where AI might contribute and which responsibilities people retain. The result depends on the task boundary and enterprise rules; trials are needed to establish what the selected tools can perform.
A role may change, be combined with another or disappear. Task analysis alone cannot settle that question. It can help you and your manager identify proposed changes and the capabilities you need to demonstrate for the work ahead.
Illustrative classifications for five roles
These examples propose different mixes of automation and human contribution; they are not employment forecasts.
These illustrative classifications assume an AI-enabled scenario. They are not capability ceilings, observed deployment rates or proportions of a person’s working time. A zero in the human-only category does not remove human approval or accountability from the workflow. Review the actual role, systems and evidence before assigning responsibility.
Roles to explore from relevant task experience
Five computed task-overlap examples, based on the July 2026 matching snapshot. They suggest questions about transferable experience and capability gaps.
Financial modeling and performance analysis can be relevant experience for finance AI projects. Review the technical building responsibilities and gaps.
Control testing and audit evidence can be relevant experience for AI governance. Review the policy and technical responsibilities of the target role.
Regulatory and policy experience can inform AI governance work. Compare it with the target role’s technical and operating responsibilities.
Customer and campaign analysis can provide context for marketing AI projects. Building and reviewing the solution require additional capabilities to assess.
Document review and case coordination can provide context for legal AI projects. Assess the technical, data-access and review requirements separately.
These are real matches, computed from each role's task list by the same role-matching engine. These computed examples identify potential task overlap, not an individual assessment or guaranteed transition. Preparation depends on the actual responsibilities and evidence your organization requires.
What to do about it
Discuss the work with the people responsible for it.
Use the public role and workflow data to identify questions about the work you do. Review the responsibilities and decisions with your team; a data classification does not determine your own readiness or authorize AI execution.
Review whether the changed role requires building, supervision, output checks or exception handling. Choose practice around the responsibilities your employer needs and the gaps in your current experience.
Agree what you need to demonstrate for the changed responsibilities. Simulations and assessment can provide evidence of capability; they do not determine hiring or staffing decisions.
Common questions
Questions about changing work and role preparation.