Manufacturing Blog: Measuring and Job-Sharing Integral to AI Adoption
Google may be using artificial intelligence (AI) to write 25 percent of its code, but for manufacturing engineers, rapid AI advancements are transforming workflows. However, lagging implementation and skills mismatches may be stopping most organizations from realizing AI’s full potential. In fact, 92 percent of the respondents to a recent survey reported they feel that cultural and change management challenges are the primary barrier to becoming data- and AI-driven.
As global digital transformation budgets are set to reach $3.9 trillion by 2027, leadership in the House of Representatives released a bipartisan task force report on AI that explores how Congress can ensure the United States “continues to lead the world in AI innovation while ensuring appropriate guardrails to safeguard the nation against current and emerging threats.”
IDC reported, global expenditures on agentic AI have already jumped from zero dollars in 2023 to almost $400 million in 2024. According to the report, “How the STEM world works: Navigating the new era of AI and trust,” most STEM employees (60 percent) are now keen to work with the latest generative AI (Gen AI)-enabled tools and large language models (LLM).
Foundational models such as LLMs may be extracting insights and generating content across text, audio, images, and video, but the next stage of Gen AI is expected to be even more transformative. An evolution is happening where we are moving from “knowledge-based, Gen-AI-powered tools—say, chatbots that answer questions and generate content—to Gen AI-enabled ‘agents’ that use foundation models to execute complex, multistep workflows across a digital world. In short, the technology is moving from thought to action,” wrote McKinsey Digital.
Gartner has named Agentic AI to its top technology trends for 2025. The innovative technology represents a shift from task-based AI to autonomous goal-driven systems. The innovative structures are able to process vast amounts of data, reason independently, and adapt to real-time changes in their environment. It also means that this AI technology can make decisions and take actions without direct human intervention.
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Gartner predicts that by 2028, agentic AI will make 15 percent of daily work decisions, “introducing opportunities as well as governance challenges for IT leaders.” Vendors are currently selling this newest AI product as a tool to do real work instead of just generating content, “even though nobody is entirely sure how it will all work,” reported MIT Sloan Management Review.
According to UiPath, its customers in the manufacturing sector use AI to monitor the flow of Internet of Things (IoT) data 24/7 in supporting equipment maintenance, product quality, and supply chain optimization. The automation platform company reported that most technology executives believe that these autonomous and collaborative AI programs will be primarily based on focused Gen AI bots that will perform specific tasks.
More specifically, 71 percent of executives report to UiPath that AI agents will drive more automation, and 52 percent plan to use agents this year.
A study from OpenAI estimated that AI could take on half the work of almost 20 percent of all workers. McKinsey is even more specific. The global consultancy estimates that within the next five years, machines will perform 30 percent of all work hours. Amid this situation, the directive should be “redesign and reassign.” From now until the end of the decade, UiPath reported that enterprises will focus on “reinventing operating models, rescoping jobs, retraining people, and reallocating tasks and processes between virtual and human workers.”
More for You: Podcast: How AI Could Impact Manufacturing
Millions of U.S. workers will undergo what McKinsey calls “occupational transitions”—that would be 12 million in the United States or about 7.4 percent of U.S. workers. This will consist of the monumental task, UiPath said, “to retrain and upskill tens of thousands of employees in using new AI tools and partnering effectively with agents. They’ll need to find new workers with the right combination of technological skills and core capabilities in critical thinking, problem solving, and creativity. They’ll have to rethink hiring plans and redo evaluation and reward systems.”
As Richard E. Baldwin, economist and IMD Business School professor, speaking at the 2023 World Economic Forum’s Growth Summit, said, “AI won’t take your job. It’s somebody using AI that will.”
Despite the hype, very few companies are actually measuring productivity gains carefully or figuring out what the liberated knowledge workers are doing with their freed-up time. And only a handful of academic studies have measured Gen AI productivity gains, and when they have, they’ve generally found improvements, but not exponential ones.
Pointedly, Nobel Prize winner in economics, MIT’s Daron Acemoglu, has commented that we haven’t seen real productivity gains from AI thus far, and he doesn’t expect to see anything dramatic over the next several years—perhaps a 0.5 percent increase over the next decade. In any case, if companies are really going to see and profit from Gen AI, they’re going to need to measure and experiment to see the benefits.
Goldman Sachs is one of the rare companies that has measured productivity gains in the area of programming. Developers there reported that their productivity increased by about 20 percent. Most similar studies have found contingent factors in productivity, whereas either inexperienced workers gain more (as in customer service and consulting), or experienced workers do better (like in code generation).
Cathy Cecere is membership content program manager.
As global digital transformation budgets are set to reach $3.9 trillion by 2027, leadership in the House of Representatives released a bipartisan task force report on AI that explores how Congress can ensure the United States “continues to lead the world in AI innovation while ensuring appropriate guardrails to safeguard the nation against current and emerging threats.”
IDC reported, global expenditures on agentic AI have already jumped from zero dollars in 2023 to almost $400 million in 2024. According to the report, “How the STEM world works: Navigating the new era of AI and trust,” most STEM employees (60 percent) are now keen to work with the latest generative AI (Gen AI)-enabled tools and large language models (LLM).
Agentic AI
Foundational models such as LLMs may be extracting insights and generating content across text, audio, images, and video, but the next stage of Gen AI is expected to be even more transformative. An evolution is happening where we are moving from “knowledge-based, Gen-AI-powered tools—say, chatbots that answer questions and generate content—to Gen AI-enabled ‘agents’ that use foundation models to execute complex, multistep workflows across a digital world. In short, the technology is moving from thought to action,” wrote McKinsey Digital.Gartner has named Agentic AI to its top technology trends for 2025. The innovative technology represents a shift from task-based AI to autonomous goal-driven systems. The innovative structures are able to process vast amounts of data, reason independently, and adapt to real-time changes in their environment. It also means that this AI technology can make decisions and take actions without direct human intervention.
Discover the benefits of ASME membership
Gartner predicts that by 2028, agentic AI will make 15 percent of daily work decisions, “introducing opportunities as well as governance challenges for IT leaders.” Vendors are currently selling this newest AI product as a tool to do real work instead of just generating content, “even though nobody is entirely sure how it will all work,” reported MIT Sloan Management Review.
According to UiPath, its customers in the manufacturing sector use AI to monitor the flow of Internet of Things (IoT) data 24/7 in supporting equipment maintenance, product quality, and supply chain optimization. The automation platform company reported that most technology executives believe that these autonomous and collaborative AI programs will be primarily based on focused Gen AI bots that will perform specific tasks.
More specifically, 71 percent of executives report to UiPath that AI agents will drive more automation, and 52 percent plan to use agents this year.
Job sharing
A study from OpenAI estimated that AI could take on half the work of almost 20 percent of all workers. McKinsey is even more specific. The global consultancy estimates that within the next five years, machines will perform 30 percent of all work hours. Amid this situation, the directive should be “redesign and reassign.” From now until the end of the decade, UiPath reported that enterprises will focus on “reinventing operating models, rescoping jobs, retraining people, and reallocating tasks and processes between virtual and human workers.”More for You: Podcast: How AI Could Impact Manufacturing
Millions of U.S. workers will undergo what McKinsey calls “occupational transitions”—that would be 12 million in the United States or about 7.4 percent of U.S. workers. This will consist of the monumental task, UiPath said, “to retrain and upskill tens of thousands of employees in using new AI tools and partnering effectively with agents. They’ll need to find new workers with the right combination of technological skills and core capabilities in critical thinking, problem solving, and creativity. They’ll have to rethink hiring plans and redo evaluation and reward systems.”
As Richard E. Baldwin, economist and IMD Business School professor, speaking at the 2023 World Economic Forum’s Growth Summit, said, “AI won’t take your job. It’s somebody using AI that will.”
Time to measure
Despite the hype, very few companies are actually measuring productivity gains carefully or figuring out what the liberated knowledge workers are doing with their freed-up time. And only a handful of academic studies have measured Gen AI productivity gains, and when they have, they’ve generally found improvements, but not exponential ones. Pointedly, Nobel Prize winner in economics, MIT’s Daron Acemoglu, has commented that we haven’t seen real productivity gains from AI thus far, and he doesn’t expect to see anything dramatic over the next several years—perhaps a 0.5 percent increase over the next decade. In any case, if companies are really going to see and profit from Gen AI, they’re going to need to measure and experiment to see the benefits.
Goldman Sachs is one of the rare companies that has measured productivity gains in the area of programming. Developers there reported that their productivity increased by about 20 percent. Most similar studies have found contingent factors in productivity, whereas either inexperienced workers gain more (as in customer service and consulting), or experienced workers do better (like in code generation).
Cathy Cecere is membership content program manager.
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