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Glossary

Augmentation potential

The opportunity for AI to help workers complete a task faster or better while keeping humans accountable for judgment, context, and outcomes.

What this means in practice

Augmentation is the optimistic reading of exposure: the same capability that threatens one role increases another worker's output. The dividing line is accountability — when a human must still own the judgment, the client relationship, or the legal sign-off, AI tends to expand that person's leverage.

Software Developers and Market Research Analysts score high here: code generation and research summarization raise their throughput while architecture, study design, and stakeholder framing remain theirs. Augmentation is why high exposure does not automatically mean high displacement.

In the data

See this term in the occupation data

These published occupation pages show augmentation potential in action — with task-level exposure, automation and augmentation scores, wage context, and transition pathways.

SOC 15-1252

Software Developers

Code generation changes the junior task bundle, but architecture, debugging, security, product judgment, and system ownership keep the role augmentation-heavy.

Exposure
63
Automation
29%
Augment
74%
SOC 13-1161

Market Research Analysts

Summarization, draft segmentation, and desk research are exposed, but domain judgment, study design, stakeholder context, and synthesis make this a strong augmentation case.

Exposure
54
Automation
37%
Augment
61%
SOC 29-1224

Radiologists

Radiology is the specialty AI was predicted to eliminate first — and instead it became the specialty most transformed by augmentation. AI triages studies, drafts measurements, and flags findings, while radiologists handle ambiguous cases, interventional procedures, and final diagnostic accountability.

Exposure
72
Automation
42%
Augment
74%