AI & Mabasa

Estimates of occupational exposure describe potential task overlap with a technology. They are not direct measurements or guarantees of job losses, wage changes, or the timing of adoption.

Key takeaways

• Analyze tasks within jobs.
• Separate exposure from observed job changes.
• Include job quality and review work in evaluation.

Analyze tasks rather than treating an occupation as one indivisible activity. A job can include drafting, judgment, coordination, physical work, accountability, and relationships with customers or colleagues. Automating one task does not establish that the whole role can be replaced. Distinguish technical feasibility from actual adoption. Cost, reliability, regulation, integration, customer expectations, and organizational choices can affect whether a capability is used. The same technology can augment one workflow and replace part of another. Evaluate job quality as well as task speed. Review workload, autonomy, error responsibility, accessibility, training, and whether workers can challenge inaccurate outputs. Time saved in one stage can become extra review work elsewhere. When considering skills, begin with the work’s domain knowledge and the ability to evaluate outputs, handle exceptions, and communicate clearly. Use current labor-market evidence for a specific location or occupation rather than presenting a global exposure estimate as a personal career forecast.

1. Imagine a role spending two hours drafting and six hours on coordination, review, and customer conversations.
2. A tool halves drafting time, saving one hour before any new review burden is counted.
3. Assess the complete workflow and organizational response rather than claiming that half the job or the entire role has disappeared.

The invented time allocation illustrates the distinction between task assistance and employment outcomes.