Skip to content
⌘ K
Log inSign up

By Intermission· 891 words

ResearchEvidenceQuestion

What industries will become most positively affected productivity-wise by AI?

Evidence(16)

  1. AI Jobs Barometer | PwC

    [1]

    PwC links AI exposure with productivity growth, wages, and hiring rather than job cuts. Productivity growth is 40% higher at highly exposed companies, while skills change more than twice as fast. Professionalized jobs grow twice as fast as democratized jobs, and technology, media, and telecom leads hiring intensity across sectors.

    1

    AI-positive productivity effects appear strongest where companies use AI to amplify human expertise and pursue growth. Technology, media, and telecom, manufacturing, financial services, consumer markets, and professional services merit attention, but the page offers uneven sector detail. The evidence is observational and links exposure with outcomes without proving causation.

  2. Ambient AI Scribes in Clinical Practice: A Randomized Trial | NEJM AI

    [2]

    A pragmatic randomized trial assigned 238 outpatient physicians across 14 specialties to two ambient AI scribes or usual care. Nabla reduced time-in-note 9.5%, while DAX showed no significant change. Both tools improved reported physician well-being and task-load measures, but occasional inaccuracies remained. Multicenter trials are needed to confirm secondary outcomes.

  3. Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives - Federal Reserve Bank of Atlanta

    [3]

    A survey of nearly 750 corporate executives finds uneven AI adoption, with more than half of firms investing. Reported labor-productivity gains vary by sector and are expected to strengthen, with the largest effects in high-skill services and finance. Gains reflect revenue-based total-factor productivity, innovation, and demand, while results lag perceptions.

  4. A sectoral taxonomy of AI intensity | OECD

    [4]

    The OECD proposes a taxonomy measuring AI intensity through AI human capital, AI innovation, AI exposure, and AI use. It finds heterogeneity: IT services score highly across dimensions, while pharmaceuticals combine high AI human capital with low AI innovation. The taxonomy maps readiness and diffusion, rather than estimating productivity gains.

  5. ChatGPT in lesson preparation - Teacher Choices trial | EEF

    [5]

    A trial across 259 teachers in 68 English secondary schools tested ChatGPT-assisted KS3 science lesson preparation. Participating teachers spent 56.2 minutes weekly, versus 81.5 minutes for controls, saving 25.3 minutes or 31%. Expert reviewers found no apparent quality reduction. The evidence received a high-security rating for the reported time result.

  6. doi.org

    [6]

    Nationally representative U.S. surveys find 32% of workers used GenAI at work in late 2024, saving 1.4% of total work hours. Estimated productivity gains range from 0.1% in real estate to 1.7% in professional, scientific, and technical services. Information and finance lead use, but low labor shares moderate modeled gains.

  7. Economic potential of generative AI | McKinsey

    [7]

    McKinsey estimates $2.6 trillion–$4.4 trillion in annual value from 63 GenAI use cases, with roughly 75% concentrated in customer operations, marketing and sales, software engineering, and R&D. Banking, high tech, life sciences, retail, and consumer packaged goods rank prominently. The analysis models potential value, not realized productivity or adoption outcomes.

  8. Firm Data on AI - Federal Reserve Bank of Atlanta

    [8]

    Representative surveys of almost 6,000 executives across the United States, United Kingdom, Germany, and Australia find widespread but shallow workplace AI use. Around 70% of firms use AI, yet over 80% report no recent productivity or employment effect. Executives forecast 1.4% productivity growth, 0.8% output growth, and 0.7% employment decline.

  9. Generative AI at Work* | The Quarterly Journal of Economics | Oxford Academic

    [9]

    The captured article page provides the title, journal navigation, and a link to a PDF, but no abstract, methods, results, or industry breakdown. Therefore, the frozen snapshot supplies no substantive evidence about the study’s specific productivity effects. Any summary would exceed the available source material and risk importing unsupported information.

  10. Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence | OECD

    [10]

    The OECD combines micro productivity estimates, AI exposure, expected adoption, and a multi-sector general equilibrium model. It estimates annual AI-driven total-factor productivity growth of 0.25–0.6 percentage points over a 10-year horizon, or 0.4–0.9 points for labor productivity. The paper emphasizes policy levers that determine whether potential gains eventually fully materialize.

  11. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality | Organization Science

    [11]

    A preregistered experiment with 758 consultants evaluated 18 knowledge tasks with GPT-4. AI users completed 12.2% more tasks, worked 25.1% faster, and produced higher-quality solutions on tasks within the capability frontier. On a complex task beyond the frontier, AI users were 19% less likely to answer correctly, revealing uneven effects.

  12. The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers | Management Science

    [12]

    Three randomized field experiments at Microsoft, Accenture, and a Fortune 100 company covered 4,867 software developers. Combined results show AI coding assistance increased completed tasks 26.08%, although individual experiments varied. Less-experienced developers adopted the tool more and gained more productivity, indicating strong future potential for software engineering productivity within firms.

  13. The Fed - The AI Buildout and the Economy: Publicly Available Data to Assess AI's Impact

    [13]

    This monitoring note organizes AI indicators into capabilities and costs, investment and adoption, and productivity and labor. It identifies information, finance, professional and business services as high-exposure sectors, but finds no broad productivity break yet. Micro gains may be delayed by shallow adoption, integration costs, measurement limits, and workflow bottlenecks.

  14. Work, Workforce, Workers Age of Generative AI Report | Accenture

    [14]

    Accenture frames GenAI as a value-chain technology that can raise productivity, revenue, creativity, and worker adaptability. Its people-centric scenario projects $10.3 trillion in additional economic value by 2038. Reinventors are twice as likely to expect productivity gains of 20% or more, but only 5% of organizations currently reskill at scale.

  15. WP NBS EN 13 2025 cover

    [15]

    A randomized experiment with 101 National Bank of Slovakia employees tested GPT-4o on generalist and specialist workplace tasks. Access raised quality 33%–44% and reduced completion time 21%. Specialist-task performance doubled, versus a 50% generalist improvement. Non-routine tasks benefited more than routine tasks, while reallocating workers could raise organizational output 7.3%.

  16. www.ey.com

    [16]

    EY models GenAI’s sectoral and regional effects through 2033 using sector-specific productivity assumptions and a computable general equilibrium framework. Health care leads projected global TFP gains at 1.2%–2.5%, followed by advanced manufacturing at 1.0%–2.4%. Education, public administration, professional services, and trade also benefit, while agriculture and construction lag behind overall.

Sources

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
  15. 15.
  16. 16.

Comments

LatestPopular
Write a comment
Loading comments…

Request a Thesis

Tell us what you’d like Roadstar to investigate.

New question

What would you like to know?

Context guides the research and is not shown on the finished page.