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The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)

April 2025

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The 10 most similar working papers to the working paper 'The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)' are listed below in order of similarity.

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  AI Adoption in America: Who, What, and Where
  

  September 2023

  Authors: Lucia Foster, Emin Dinlersoz, Kristina McElheran, Nikolas Zolas, Erik Brynjolfsson, Zachary Kroff, J. Frank Li

  Working Paper Number:

  CES-23-48R

  We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of 'superstar' cities and emerging hubs led startups' adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing 'AI divide' if early patterns persist.
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• Working Paper

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  Data in Action: Data-Driven Decision Making in U.S. Manufacturing
  

  January 2016

  Authors: Kristina McElheran, Erik Brynjolfsson

  Working Paper Number:

  CES-16-06

  Manufacturing in America has become significantly more data-intensive. We investigate the adoption, performance effects and organizational complementarities of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for 2005 and 2010, we observe the extent to which manufacturing firms track and use data to guide decision making, as well as their investments in information technology (IT) and the use of other structured management practices. Examining a representative sample of over 18,000 plans, we find that adoption of DDD is earlier and more prevalent among larger, older plants belonging to multi-unit firms. Smaller single-establishment firms adopt later but have a higher correlation with performance than similar non-adopters. Using a fixed-effects estimator, we find the average value-added for later DDD adopters to be 3% greater than non-adopters, controlling for other inputs to production. This effect is distinct from that associated with IT and other structured management practices and is concentrated among single-unit firms. Performance improves after plants adopt DDD, but not before ' consistent with a causal relationship. However, DDD-related performance differentials decrease over time for early and late adopters, consistent with firm learning and development of organizational complementarities. Formal complementarity tests suggest that DDD and high levels of IT capital reinforce each other, as do DDD and skilled workers. For some industries, the benefits of DDD adoption appear to be greater for plants that delegate some decision making to frontline workers.
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• Working Paper

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  Predictive Analytics and Organizational Architecture: Plant-Level Evidence from Census Data
  

  January 2019

  Authors: Eva Labro, Mark Lang, Jim Omartian

  Working Paper Number:

  CES-19-02

  We examine trends in the use of predictive analytics for a sample of more than 25,000 manufacturing plants using proprietary data from the US Census Bureau. Comparing 2010 and 2015, we find that use of predictive analytics has increased markedly, with the greatest use in younger plants, professionally-managed firms, more educated workforces, and stable industries. Decisions on data to be gathered originate from headquarters and are associated with less delegation of decision-making and more widespread awareness of quantitative targets among plant employees. Performance targets become more accurate, long-term oriented, and linked to company-wide performance, and management incentives strengthen, both in terms of monetary bonuses and career outcomes. Plants increasing predictive analytics become more efficient, with lower inventory, increased volume of shipments, narrower product mix, reduced management payroll and increased use of flexible and temporary employees. Results are robust to a specification based on increased government demand for data.
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• Working Paper

  
  
  Electronic Networking Technologies, Innovation Misfit, and Plant Performance
  

  February 2010

  Authors: Nigel Melville, Robert G. Fichman

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  CES-10-03

  Prior work on information technology (IT) adoption and economic impacts typically employs an instrumental logic in which firms lead with innovation when they possess characteristics that make it economically beneficial to do so and lag when they do not. However, firms may deviate from this idealized picture when they possess characteristics of an innovation laggard but exhibit the behavior of an innovation leader (or vice versa), with implications for the returns to IT investment. This study develops a conceptual framework and hypotheses regarding the implications of such deviations, which we call innovation misfits. Using a data set comprising measures of the adoption of electronic networking technologies (ENT) in over 25,000 U.S. manufacturing plants, productivity regression estimation reveals a consistent pattern that the association between IT and productivity is diminished in the presence of innovation misfit. We discuss the implications of innovation misfit for scholarship and management practice, which are numerous.
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• Working Paper

  
  
  Delegation in Multi-Establishment Firms: The Organizational Structure of I.T. Purchasing Authority
  

  October 2010

  Authors: Kristina Steffenson McElheran

  Working Paper Number:

  CES-10-35

  A rare large-scale empirical study of delegation within firms, this paper investigates how decision rights over information technology investments are allocated within multi-establishment firms. The core results indicate that a relatively high contribution to firm sales is highly correlated with authority being delegated to the local establishment. Firm-wide operational complexity and local information advantages are also associated with local discretion for IT purchases. Certain IT investments are also positively correlated with delegation. On the other hand, significant operational interdependencies evince a positive correlation with centralization, as do productive similarities among establishments. Surprisingly, absolute size of the firm and having a large IT budget are also correlated with centralized IT decision-making. With the exception of these latter effects, the results are consistent with models of organizational design that predict delegation where there is great demand for locally adapted choices and centralization where firm-wide coordination is most important. The findings document and make sense of widespread heterogeneity in decision rights across a range of firm and industry settings ' even among establishments belonging to the same parent firm. Finally, they suggest important considerations for future empirical and theoretical research into the determinants of delegation.
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• Working Paper

  
  
  Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey
  

  March 2024

  Authors: John Haltiwanger, Lucia Foster, Emin Dinlersoz, Catherine Buffington, Nathan Goldschlag, Zachary Kroff, Kathryn Bonney, Cory Breaux, Keith Savage

  Working Paper Number:

  CES-24-16R

  Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business.
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• Working Paper

  
  
  Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
  

  December 2020

  Authors: Lucia Foster, Emin Dinlersoz, Kristina McElheran, Nikolas Zolas, Erik Brynjolfsson, Catherine Buffington, Nathan Goldschlag, Zachary Kroff, David Beede

  Working Paper Number:

  CES-20-40

  We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES), provides comprehensive and timely information on the diffusion among U.S. firms of advanced technologies including artificial intelligence (AI), cloud computing, robotics, and the digitization of business information. The 2018 ABS is a large, nationally representative sample of over 850,000 firms covering all private, nonfarm sectors of the economy. We describe the motivation for and development of the technology module in the ABS, as well as provide a first look at technology adoption and use patterns across firms and sectors. We find that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher. This new data will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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• Working Paper

  
  
  The Microstructure of AI Diffusion: Evidence From Firms, Business Functions, and Worker Tasks
  

  April 2026

  Authors: John Haltiwanger, Lucia Foster, Emin Dinlersoz, Kathryn Bonney, Cory Breaux, Aditya Pande

  Working Paper Number:

  CES-26-25

  Using novel, nationally representative data from the 2026 AI supplement to the U.S. Census Bureau's Business Trends and Outlook Survey (BTOS), we characterize AI diffusion across three interconnected layers: overall firm use, deployment across business functions, and worker-task use. This multi-layered approach provides a nuanced picture of business AI adoption. During the supplement reference period (Nov 2025-Jan 2026), 18% of firms used AI in a business function, rising to 32% on an employment-weighted basis; adoption is expected to reach 22% within six months. AI use is substantially higher in large firms and knowledge-intensive sectors, with use rates reaching 50%-60% (60%-70%, employment-weighted) for very large firms in the Information, Professional Services, and Finance sectors. Among adopting firms, the scope of use remains limited: 57% of users integrate AI in three or fewer business functions, most commonly Sales and Marketing (52%), Strategy and Business Development (45%), and IT (41%). In 23% (41%, employment-weighted) of firms, workers use AI in work-related tasks. Writing, document analysis, and information search are the leading Generative AI use in tasks, though 65% of firms limit use to three or fewer tasks. The evidence points to both top-down and bottom-up diffusion channels: worker task use sometimes occurs without formal firm-level adoption, and firm-level adoption sometimes occurs without worker task use. Most users (66%) rely on AI solely to augment tasks, while AI-related employment decreases are rare, occurring in only 2% of firms. Regression analysis shows a robust positive correlation between firm commercial performance and the breadth of AI integration, including functional deployment, task-level use, and operational investment. A distinct divergence emerges, however, with respect to labor outcomes. Functional breadth and operational investment are positively associated with employment decreases, whereas worker-task integration shows no significant link to headcount reduction once functional integration and operational investment are taken into account.
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• Working Paper

  
  
  Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey
  

  April 2022

  Authors: John Haltiwanger, Lucia Foster, Emin Dinlersoz, Nikolas Zolas, Daron Acemoglu, Catherine Buffington, Nathan Goldschlag, Zachary Kroff, David Beede, Gary Anderson, Eric Childress, Pascual Restrepo

  Working Paper Number:

  CES-22-12R

  This paper describes the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the 2019 Annual Business Survey, conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES). The module collects data from over 300,000 firms on the use of five advanced technologies: AI, robotics, dedicated equipment, specialized software, and cloud computing. The adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and young firms. However, because larger firms are much more likely to adopt them, 12-64% of US workers and 22-72% of manufacturing workers are exposed to these technologies. Firms report a variety of motivations for adoption, including automating tasks previously performed by labor. Consistent with the use of these technologies for automation, adopters have higher labor productivity and lower labor shares. In particular, the use of these technologies is associated with a 11.4% higher labor productivity, which accounts for 20'30% of the difference in labor productivity between large firms and the median firm in an industry. Adopters report that these technologies raised skill requirements and led to greater demand for skilled labor, but brought limited or ambiguous effects to their employment levels.
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• Working Paper

  
  
  IT for Information-Based Partnerships: Empirical Analysis of Environmental Contingencies to Value Co-Creation
  

  December 2009

  Authors: Terence Saldanha, Nigel Melville, Ronald Ramirez, Vernon Richardson

  Working Paper Number:

  CES-09-42

  We empirically examine IT value co-creation in supply chains, incorporating key contingencies of the competitive environment. Prior research suggests that IT used for strategic informationbased partnerships may benefit supply chains facing higher volatility, enabling tightly coupled integration and enhanced strategic response to changing consumer preferences. Analyzing a unique dataset comprising over 6,000 U.S. manufacturing plants, we obtain three principal results. First, value co-creation using either IT for strategic information-based partnerships (ITIP) or merely IT for transaction efficiency (ITT) is positive and significant. Second, the co-created value from ITIP is larger than that for (ITT), suggesting that information-based partnerships, while perhaps requiring a greater investment, yield a higher return. Third and most importantly, co-created value from using IT for information-based partnerships is positively moderated by demand volatility, i.e., value is greater in higher demand volatility environments. However, we find the opposite is true for using IT for efficient transactions. This is a new contribution to the literature and has important theoretical and practical implications.
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