Prof Suraj Srinivasan's automation and augmentation scores

Research Question

What does Prof Suraj Srinivasan's research framework for measuring Automation and Augmentation (A&A) scores across job roles and industries reveal about which roles face full Artificial Intelligence (AI) replacement, specifically the finding that roles where automation score exceeds augmentation score are those AI will replace in totality, and what are the strategic implications for workforce and organisational planning?

Findings

Executive Summary

Srinivasan's accessible research does not show that occupations with automation scores above augmentation scores will be replaced entirely by AI; it shows that occupations in the top automation quartile lose postings and skill breadth, while occupations in the top augmentation quartile gain demand and AI-related skill requirements.

The framework combines an exposure-based automation index with a task-mix augmentation index, so the two numbers are complementary measures rather than a single binary cutoff.

The occupations most exposed to automation are clerical, transcription, translation, and other structured cognitive roles, while the occupations most exposed to augmentation are specialist roles that still depend on judgment, accountability, and mixed task portfolios.

For workforce planning, the practical implication is to redesign roles and training around task mix: automate repetitive sub-tasks, reskill workers leaving clerical pipelines, and deliberately strengthen AI literacy in occupations that remain human-led but AI-assisted.

Key Findings

1. The accessible Srinivasan research measures automation and augmentation as different constructs, so it does not present a formal crossover rule in which a higher automation score than augmentation score automatically means total occupational replacement.
2. The working paper finds that occupations in the top quartile of automation exposure experienced a 17% decline in job postings per firm per quarter after ChatGPT, while occupations in the top quartile of augmentation exposure experienced a 22% increase.
3. The later Harvard Business School Working Knowledge summary reports updated effects, including a 13% decline for structured repetitive occupations and 20% growth for more analytical, technical, or creative occupations.
4. The most automation-exposed occupations are concentrated in clerical and codifiable language work, including correspondence clerks, interpreters and translators, court clerks, medical transcriptionists, telemarketers, typists, and payroll clerks.
5. The most augmentation-exposed occupations are mixed-task specialist roles such as clinical neuropsychologists, medical dosimetrists, agricultural engineers, cartographers, microbiologists, and mediators, where AI can compress sub-tasks but not displace the need for human judgment or responsibility.
6. The study supports a role-redesign thesis more than an occupation-extinction thesis, because its outcome variables are posting volume and skill composition rather than observed full elimination of whole job families.
7. Srinivasan's results align with broader labor-market frameworks from the WEF and McKinsey, both of which also place the highest near-term risk on clerical or routine support work and place the strongest augmentation effects in higher-judgment knowledge work.

Assumptions

• The February 2026 Harvard Business School Working Knowledge article reflects a later revision of the same research program rather than a different model specification, because it cites the same authors, paper title, and qualitative conclusions while updating the sample window and headline percentages.
• The original infographic likely visualizes a larger set of occupations than the paper's published top-10 table, so this item relies on the table and article examples rather than claiming a complete reconstruction of every plotted point.

Analysis

The paper traces how generative AI changes labor demand across occupations by linking task composition and skill mix to posting changes.

That distinction matters because a firm can automate large parts of a role without making the role disappear, especially where non-automatable decision rights, interpersonal accountability, or physical-world execution remain attached to the job.

The best-supported strategic response is a portfolio approach that separates automatable tasks, non-automatable judgment tasks, and new AI-coordination tasks inside each occupation.

That interpretation also explains why Srinivasan aligns with WEF and McKinsey on clerical decline and high-skill augmentation without collapsing all three frameworks into the same claim, since Srinivasan is grounded in postings and task mix while the others emphasize survey expectations and macro transitions.

Risks, Gaps, and Uncertainties

• The accessible paper and the later Harvard Business School summary expose different sample end dates and different effect sizes, so the exact percentages should be treated as version-specific rather than timeless constants.
• The accessible sources do not provide a full industry-by-role coordinate dump for the infographic, so the occupation mapping here is strongest for the published top-ranked roles and weaker for exhaustive quadrant reconstruction.
• The McKinsey comparison is useful for directional triangulation but less precise than the Harvard Business School and WEF evidence in this item because this synthesis relies on McKinsey's official summary framing rather than line-by-line extraction of the underlying report text.

Open Questions

• How far do the updated 2025 and 2026 versions of the Srinivasan research move the occupation rankings once more post-ChatGPT hiring data is included?
• Can the full occupation-by-score dataset behind the Harvard Business School visualization be recovered from a public appendix or data release?
• Which entry-level pathways are most vulnerable when postings fall in automation-prone occupations before unemployment visibly rises?

sources

• [x] Chen et al. (2024) Displacement or Complementarity? The Labor Market Impact of Generative AI
• [x] Zakerinia et al. (2025) Displacement or Complementarity? The Labor Market Impact of Generative AI, Americas Conference on Information Systems proceedings abstract
• [x] Azpurua (2026) Enhance or Eliminate? How AI Will Likely Change These Jobs
• [x] Harvard Business Review (2026) Research: How AI Is Changing the Labor Market
• [x] McKinsey Global Institute (2023) Generative AI and the Future of Work in America
• [x] McKinsey (2023) The Economic Potential of Generative AI: The Next Productivity Frontier
• [x] World Economic Forum (2023) Future of Jobs Report 2023
• [x] World Economic Forum (2023) Future of Jobs Report 2023 Press Release