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The ‘productivity paradox’ of AI adoption in manufacturing firms

Published

4 hours ago

July 9, 2025

Organizations have long viewed artificial intelligence as a way to achieve productivity gains. But recent research about AI adoption at U.S. manufacturing firms reveals a more nuanced reality: AI introduction frequently leads to a measurable but temporary decline in performance followed by stronger growth output, revenue, and employment.

This phenomenon, which follows a “J-curve” trajectory, helps explain why the economic impact of AI has been underwhelming at times despite its transformative potential.

“AI isn’t plug-and-play,” said University of Toronto professor Kristina McElheran, a digital fellow at the MIT Initiative on the Digital Economy and one of the lead authors of the new paper “The Rise of Industrial AI in America: Microfoundations of the Productivity J-Curve(s).” “It requires systemic change, and that process introduces friction, particularly for established firms.”

University of Colorado Boulder professor Mu-Jeung Yang; Zachary Kroff, formerly with the U.S. Census Bureau and currently an analytics specialist at Analysis Group; and Stanford University professor Erik Brynjolfsson, PhD ’91, co-authored the report.

Working with data from two U.S. Census Bureau surveys covering tens of thousands of manufacturing companies in 2017 and 2021, the researchers found that the AI adoption J-curve varied among businesses that had adopted AI technologies with industrial applications. Short-term losses were greater in older, more established companies. Evidence on young firms showed that losses can be mitigated by certain business strategies. And despite early losses, early AI adopters showed stronger growth over time.

Here’s a look at what the study indicates about the adoption and application of AI, and the types of firms that outperform others in using new technology.

1. AI adoption initially reduces productivity.

The study shows that AI adoption tends to hinder productivity in the short term, with firms experiencing a measurable decline in productivity after they begin using AI technologies.

Even after controlling for size, age, capital stock, IT infrastructure, and other factors, the researchers found that organizations that adopted AI for business functions saw a drop in productivity of 1.33 percentage points. When correcting for selection bias — organizations that expect higher returns are more likely to be early AI adopters — the short-run negative impact was significantly larger, at around 60 percentage points, the researchers write.

This decline isn’t only a matter of growing pains; it points to a deeper misalignment between new digital tools and legacy operational processes, the researchers found. AI systems used for predictive maintenance, quality control, or demand forecasting often also require investments in data infrastructure, staff training, and workflow redesign. Without those complementary pieces in place, even the most advanced technologies can underdeliver or create new bottlenecks.

“Once firms work through the adjustment costs, they tend to experience stronger growth,” McElheran said. “But that initial dip — the downward slope of the J-curve — is very real.”

2. Short-term losses precede long-term gains.

Despite companies’ early losses, the study found a clear pattern of recovery and eventual improvement. Over a longer period of time — there was a four-year gap in the study data — manufacturing firms that adopted AI tended to outperform their non-adopting peers in both productivity and market share. This recovery followed an initial period of adjustment during which companies fine-tuned processes, scaled digital tools, and capitalized on the data generated by AI systems.

That upswing wasn’t distributed evenly, though. The firms seeing the strongest gains tended to be those that were already digitally mature before adopting AI.

“Firms that have already done the digital transformation or were digital from the get-go have a much easier ride because past data can be a good predictor of future outcomes,” McElheran said. Size helps too. “Once you solve those adjustment costs, if you can scale the benefits across more output, more markets, and more customers, you’re going to get on the upswing of the J-curve a lot faster,” she said.

Better integration of the technology and strategic reallocation of resources is important to this recovery as firms gradually shift toward more AI-compatible operations, often investing in automation technologies like industrial robots, the researchers found.

3. Older firms see greater short-term losses.

Short-term losses aren’t felt equally across all firms, the study found. The negative impact of AI adoption was most pronounced among established firms. Such organizations typically have long-standing routines, layered hierarchies, and legacy systems that can be difficult to unwind.

These firms often have trouble adapting, partly due to institutional inertia and the complexity of their operations. “We find that older firms, in particular, struggle to maintain vital production management practices such as monitoring key performance indicators and production targets,” the researchers write.

“Old firms actually saw declines in the use of structured management practices after adopting AI,” McElheran said. “And that alone accounted for nearly one-third of their productivity losses.”

In contrast, younger, more flexible companies appear better equipped to integrate AI technologies quickly and with less disruption. They may also have less to unlearn, making the transition to AI-enabled workflows more seamless.

“Taken together, our findings highlight AI’s dual role as a transformative technology and catalyst for short-run organizational disruption, echoing patterns familiar to scholars of technological change,” the researchers write. They note that the results also show the importance of complementary practices and strategies that mitigate adjustment causes and boost long-term returns to “flatten the J-curve dip and realize AI’s longer-term productivity at scale.”

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AI Insights

Global Artificial Intelligence (AI) in Clinical Trials Market

Published

19 minutes ago

July 9, 2025

The Editors

According to DelveInsight’s analysis, The demand for Artificial Intelligence in clinical trials is experiencing strong growth, primarily driven by the rising global prevalence of chronic conditions like diabetes, cardiovascular diseases, respiratory illnesses, and cancer. This growth is further supported by increased investments and funding dedicated to advancing drug discovery and development efforts. Additionally, the growing number of strategic collaborations and partnerships among pharmaceutical, biotechnology, and medical device companies is significantly boosting the adoption of AI-driven solutions in clinical trials. Together, these factors are anticipated to fuel the expansion of the AI in the clinical trials market during the forecast period from 2025 to 2032.

DelveInsight’s “Artificial Intelligence (AI) in Clinical Trials Market Insights, Competitive Landscape and Market Forecast-2032” report provides the current and forecast market outlook, forthcoming device innovation, challenges, market drivers and barriers. The report also covers the major emerging products and key Artificial Intelligence (AI) in Clinical Trials companies actively working in the market.

To know more about why North America is leading the market growth in the Artificial Intelligence (AI) in Clinical Trials market, get a snapshot of the report Artificial Intelligence (AI) in Clinical Trials Market Trends

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Artificial Intelligence (AI) in Clinical Trials Overview

Artificial Intelligence (AI) in clinical trials refers to the use of advanced machine learning algorithms and data analytics to streamline and improve various aspects of clinical research. AI enhances trial design, patient recruitment, site selection, and data analysis by identifying patterns and predicting outcomes. It enables faster patient matching, optimizes protocol design, reduces trial timelines, and improves data quality and monitoring. AI also helps in real-time adverse event detection and adaptive trial management, making clinical trials more efficient, cost-effective, and patient-centric.

DelveInsight Analysis: The global Artificial Intelligence in clinical trials market size was valued at USD 1,350.79 million in 2024 and is projected to expand at a CAGR of 12.04% during 2025-2032, reaching approximately USD 3,334.47 million by 2032.

Artificial Intelligence (AI) in Clinical Trials Market Insights

Geographically, North America is expected to lead the AI in the clinical trial market in 2024, driven by several critical factors. The region’s growing burden of chronic diseases, substantial investments in R&D, and the rising volume of clinical trials contribute significantly to this dominance. Additionally, an increasing number of collaborations and partnerships among pharmaceutical and medical device companies, along with the advancement of sophisticated AI solutions, are accelerating market expansion. These developments are enhancing the ability to manage complex clinical trials efficiently, driving the adoption of AI technologies and supporting the market’s growth in North America throughout the forecast period from 2025 to 2032.

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Recent Developments in the Artificial Intelligence (AI) in Clinical Trials Market Report

• In May 2025, Avant Technologies, Inc. (OTCQB: AVAI) and joint venture partner Ainnova Tech, Inc. announced the initiation of acquisition discussions aimed at enhancing their presence in the rapidly growing AI-powered healthcare sector.

• In March 2025, Suvoda introduced Sofia, an AI-driven assistant created to optimize clinical trial management processes. Sofia aids study teams by providing quick access to essential trial data and real-time, intelligent insights. This tool boosts operational efficiency, minimizes manual tasks, and helps teams make faster, data-informed decisions throughout the clinical trial journey.

• In December 2024, ConcertAI and NeoGenomics unveiled CTO-H, an advanced AI-powered software platform designed to enhance research analytics, clinical trial design, and operational efficiency. CTO-H provides an extensive research data ecosystem, offering comprehensive longitudinal patient data, deep biomarker insights, and scalable analytics to support more precise, efficient, and data-driven clinical development processes.

• In June 2024, Lokavant introduced SpectrumTM, the first AI-powered clinical trial feasibility solution aimed at enhancing trial performance throughout the clinical development process. Spectrum enables study teams to forecast, control, and improve trial timelines and expenses in real-time.

• Thus, owing to such developments in the market, rapid growth will be observed in the Artificial Intelligence (AI) in Clinical Trials market during the forecast period

Key Players in the Artificial Intelligence (AI) in Clinical Trials Market

Some of the key market players operating in the Artificial Intelligence (AI) in Clinical Trials market include- TEMPUS, NetraMark, ConcertAI, AiCure, Medpace, Inc., ICON plc, Charles River Laboratories, Dassault Systèmes, Oracle, Certara, Cytel Inc., Phesi, DeepHealth, Unlearn.ai, Inc., H1, TrialX, Suvoda LLC, Risklick, Lokavant, Research Solutions, and others.

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Analysis on the Artificial Intelligence (AI) in Clinical Trials Market Landscape

To meet the growing needs of clinical trials, leading companies in the AI in Clinical Trials market are creating advanced AI solutions aimed at improving trial efficiency, optimizing patient recruitment, and enhancing clinical trial design at investigator sites. For example, in April 2023, ConcertAI introduced CTO 2.0, a clinical trial optimization platform that utilizes publicly available data and partner insights to deliver comprehensive site and physician-level trial data. This tool provides key operational metrics and site profiles to evaluate trial performance and site capabilities. Additionally, CTO 2.0 assists sponsors in complying with FDA requirements for inclusive trial outcomes, promoting a shift toward community-based trials with more streamlined and patient-centric designs.

As a result of these advancements, the software segment is projected to experience significant growth throughout the forecast period, contributing to the overall expansion of the AI in the clinical trials market.

Scope of the Artificial Intelligence (AI) in Clinical Trials Market Report

• Coverage: Global

• Study Period: 2022-2032

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Product Type: Software and Services

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Technology Type: Machine Learning (ML), Natural Language Processing (NLP), and Others

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Application Type: Clinical Trial Design & Optimization, Patient Identification & Recruitment, Site Identification & Trial Monitoring, and Others

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Therapeutic Area: Oncology, Cardiology, Neurology, Infectious Disease, Immunology, and Others

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By End-User: Pharmaceutical & Biotechnology Companies and Medical Device Companies

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Geography: North America, Europe, Asia-Pacific, and Rest of the World

• Key Artificial Intelligence (AI) in Clinical Trials Companies: TEMPUS, NetraMark, ConcertAI, AiCure, Medpace, Inc., ICON plc, Charles River Laboratories, Dassault Systèmes, Oracle, Certara, Cytel Inc., Phesi, DeepHealth, Unlearn.ai, Inc., H1, TrialX, Suvoda LLC, Risklick, Lokavant, Research Solutions, and others

• Porter’s Five Forces Analysis, Product Profiles, Case Studies, KOL’s Views, Analyst’s View

Interested in knowing how the Artificial Intelligence (AI) in Clinical Trials market will grow by 2032? Click to get a snapshot of the Artificial Intelligence (AI) in Clinical Trials Market Analysis

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Table of Contents

1 Artificial Intelligence (AI) in Clinical Trials Market Report Introduction

2 Artificial Intelligence (AI) in Clinical Trials Market Executive summary

3 Regulatory and Patent Analysis

4 Artificial Intelligence (AI) in Clinical Trials Market Key Factors Analysis

5 Porter’s Five Forces Analysis

6 COVID-19 Impact Analysis on Artificial Intelligence (AI) in Clinical Trials Market

7 Artificial Intelligence (AI) in Clinical Trials Market Layout

8 Global Company Share Analysis – Key Artificial Intelligence (AI) in Clinical Trials Companies

9 Company and Product Profiles

10 Project Approach

11 Artificial Intelligence (AI) in Clinical Trials Market Drivers

12 Artificial Intelligence (AI) in Clinical Trials Market Barriers

13 About DelveInsight

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This release was published on openPR.

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What Is Artificial Intelligence? Explained Simply With Real-Life Examples – The Times of India

Published

1 hour ago

July 9, 2025

The Editors

What Is Artificial Intelligence? Explained Simply With Real-Life Examples The Times of India

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Cal State LA secures funding for two artificial intelligence projects from CSU

Published

2 hours ago

July 9, 2025

The Editors

Cal State LA has won funding for two faculty-led artificial intelligence projects through the California State University’s (CSU) Artificial Intelligence Educational Innovations Challenge (AIEIC).

The CSU launched the initiative to ensure that faculty from its 23 campuses are key drivers of innovative AI adoption and deployment across the system. In April, the AIEIC invited faculty to develop innovative instructional strategies that leverage AI tools.

The response was overwhelming, with more than 400 proposals submitted by over 750 faculty members across the state. The Chancellor’s Office will award a total of $3 million to fund the 63 winning proposals, which were chosen for their potential to enable transformative teaching methods, foster groundbreaking research, and address key concerns about AI adoption within academia.

“CSU faculty and staff aren’t just adopting AI—they are reimagining what it means to teach, learn, and prepare students for an AI-infused world,” said Nathan Evans, CSU deputy vice chancellor of Academic and Student Affairs and chief academic officer. “The number of funded projects underscores the CSU’s strong commitment to innovation and academic excellence. These initiatives will explore and demonstrate effective AI integration in student learning, with findings shared systemwide to maximize impact. Our goal is to prepare students to engage with AI strategically, ethically, and successfully in California’s fast-changing workforce.”

Cal State LA’s winning projects are titled “Teaching with Integrity in the Age of AI” and “AI-Enhanced STEM Supplemental Instruction Workshops.”

For “Teaching with Integrity in the Age of AI,” the university’s Center for Effective Teaching and Learning will form a Faculty Learning Community (FLC) to address faculty concerns about AI and academic integrity. From September 2025 to April 2026, the FLC will support eight to 15 cross-disciplinary faculty members in developing AI-informed, ethics-focused pedagogy. Participants will explore ways to minimize AI-facilitated cheating, apply ethical decision-making frameworks, and create assignments aligned with AI literacy standards.

The “AI-Enhanced STEM Supplemental Instruction Workshops” project will look to expand and improve student success in challenging first-year Science, Technology, Engineering, and Math courses by integrating generative AI tools, specifically ChatGPT, into Supplemental Instruction workshops. By leveraging AI, the project addresses the limitations of collaborative learning environments, providing personalized, real-time feedback, and guidance.

The AIEIC is a key component of the CSU’s broader AI Strategy, which was launched in February 2025 to establish the CSU as the first AI-empowered university system in the nation. It was designed with three goals: to encourage faculty to explore AI literacies and competencies, focusing on how to help students build a fluent relationship with the technologies; to address the need for meaningful engagement with AI, emphasizing strategies that ensure students actively participate in learning alongside AI; and to examine the ethics of AI use in higher education, promoting approaches that embed academic integrity.

Awarded projects span a broad range of academic areas, including business, engineering, ethnic studies, history, health sciences, teacher preparation, scholarly writing, journalism, and theatre arts. Several projects are collaborative efforts across multiple disciplines or focus on faculty development—equipping instructors with the tools to navigate course design, policy development, and classroom practices in an AI-enabled environment.

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