A data center technician’s job involves more than just inspecting equipment and responding to alarms. Today, businesses need to interpret streams of operational data, find early signs of risk, and make decisions across systems. And with the support of artificial intelligence, you can tackle more complex tasks with confidence by uncovering relevant information, flagging potential hazards, and guiding you through next steps in real-time.
This type of support represents a different way of thinking about AI in the workplace. Not just as a tool to help fewer people perform the same tasks, but as a way to help people handle work that is becoming more complex, data-rich, and ultimately more important.
This distinction is at the heart of the debate about how companies should use AI. Many AI deployments in today’s enterprises are aimed at automation. The goal is to perform existing tasks faster, cheaper, or with fewer people. However, in a February 2026 paper by the Brookings Institution’s Hamilton Project entitled “Building Pro-Worker Artificial Intelligence,” MIT economists state: with david orter They argue that AI can also go in the other direction: extending human judgment, creating new tasks, accelerating skill acquisition, and increasing the value of human expertise. (Mr. Acemoglu and Mr. Johnson, together with James A. Robinson, won the Nobel Prize in Economics in 2024.)
Johnson, the MIT Sloan professor, described pro-worker AI as AI that expands workers’ capabilities and increases the value of human skills, judgment, and expertise.
“Pro-worker AI means deploying AI in ways that increase demand for human expertise,” he said. “The value of your talent increases, the value of their expertise increases, and as a result, you pay them more.”
Not all productivity gains favor workers. Technology can make companies more efficient while reducing the need for specialized knowledge, narrowing job roles, and transferring more value from labor to capital. The authors define pro-worker AI as AI that goes in the opposite direction, expanding what workers can do and increasing the importance of expertise on the job.
However, many companies have not yet reached that target level. “It’s too easy to replace humans with machines,” Johnson says. “It’s the path of least resistance that doesn’t require as much imagination from management.” And this easier path may also leave more value on the table.
Proworker AI starts with deciding how to design, deploy, and integrate technology into your work. Productivity gains alone will not determine whether an AI system will benefit workers. More importantly, the path to those benefits is whether AI reduces the need for human expertise or helps increase the value of that expertise.
What qualifies as pro-worker AI?
The Brookings report provides a framework for understanding how different technologies impact workers, identifying five broad categories: workforce augmentation, capital augmentation, automation, leveling of expertise, and new task creation technologies. Only new task creation technologies, the authors write, “clearly favor workers,” because they create demand for new forms of human expertise, rather than reducing the need for existing expertise.
Technologies that may seem similar on the surface can have vastly different impacts on work. Workforce augmentation tools have the potential to enable workers to perform their current tasks faster. Automation tools can move tasks from workers to machines, and expertise-leveling tools can enable less experienced workers to perform tasks that previously required the skills of experts. While this may benefit some workers by opening up new opportunities or increasing the value of their skills, it may also reduce the scarcity value of the professionals who previously performed the job.
For example, the report points to pulse oximeters. Medical technicians can use this device to quickly read a patient’s blood oxygen level. This is a task that once required a phlebotomist, lab technician, doctor or nurse. This change may improve the capabilities of engineers while also changing the demand for other forms of expertise. That is why the authors do not explicitly classify it as pro-labor.
New task creation technologies are different in that they expand the range of valuable work that humans can perform. For example, in electrical construction, technologies such as Ethernet networks, fiber optic cables, and occupancy-aware heating and lighting systems have increased the complexity of modern buildings and created a demand for expertise to plan, install, and maintain these systems.
Johnson said this is an important test for AI: “Extending human capabilities to do new things that humans haven’t done before tends to increase the value of human expertise.”
Patent prosecution is an example of how this looks in practice. The U.S. Patent and Trademark Office has incorporated AI-based search tools into its prior art search software to help examiners find conceptually related documents faster and more accurately than traditional keyword-based search methods. Researchers are cautious in their assessment. If the tool only helps examiners do the same work more quickly, it could primarily reduce the examiner’s time needed for a particular task, resulting in increased efficiency through labor savings. But the ability to perform deeper analysis and better evaluate applications Enhancing the value of professional judgment.
This nuance is important for business practitioners. Asking whether AI “helps people” can obscure the long-term impact on work itself. While tools support employees in a narrow sense, they can devalue their expertise over time. A more useful test is whether the technology creates new opportunities for employees to apply judgment, build expertise, and take on more valuable tasks.
As such, pro-worker AI is partly a technical challenge and partly a business challenge, Johnson said. Leaders need to decide what kind of work they want AI to enable.
“What determines the outcome? [leaders] They want to do it with AI and they have a vision that they have of what can be done with AI,” he said.
What is the business case for ProWorker AI?
Johnson said understanding how pro-worker AI can benefit businesses starts with thinking about productivity differently. Automation often reduces the cost of existing work and increases productivity. While that’s valuable, technology is not the only way to create business value. He said new task creation technologies improve productivity by expanding what people can do productively, allowing companies to meet new needs, solve more complex problems, and offer services and experiences that were previously too difficult, expensive, or impractical.
“Do you want to wow your customers and expand what they can expect, or do you want to keep doing what you’ve always done?” asked Johnson. “AI creates the potential to create breakthrough customer experiences, and businesses should focus on that.”
Schneider Electric has designed an AI tool to support electricians and electricians when troubleshooting machines and circuits. The tool utilizes a database of images, hardware information, and documented issues to suggest next steps to field technicians. According to the report, engineers used this tool to cut the average time required to create maintenance reports in half. But the case for workers is more than just that the tools save time. That too Help workers use their expertise more effectively in the field.
MIT economist David Orter explains specific examples of professional worker AI
Schneider’s example also illustrates the broader business case for pro-worker AI. As AI tools become more widely available, competitive advantage may come not from the technology itself, but from how jobs are redesigned around it. Companies that use AI solely to compress existing processes may see efficiency gains, while companies that use AI to: expand What employees can do might find new ways to serve customers, improve quality, and tap into their expertise.
However, this does not mean that all AI systems should avoid automation. Automating some tasks can make goods and services cheaper, safer, and more accessible. The question is whether automation will become the default goal for AI deployments, especially in areas where human judgment, context, and discretion remain central to corporate performance.
“Companies can miss out on a lot of opportunities if they don’t take a pro-worker approach,” Johnson said. “An automation-first path could create problems for the entire labor market if companies don’t hire people or add value to their workforce’s skills.”
This will also change the way AI investments are evaluated. A narrow business case may focus on saving time and labor costs. The ProWorker business case asks additional questions about new capabilities created by AI, new jobs employees will be able to perform, more valuable expertise, and how the use of technology will change the quality of a product, service, or customer experience. The researchers argue that these questions are important because AI’s collaborative potential remains untapped.
Organizations that not only improve the capabilities of their employees, but also empower their employees, will be in a better position to innovate, retain talent, and build expertise that competitors cannot easily imitate, Johnson added. “I think businesses need to understand that AI has real transformative potential. But simply replacing humans with machines won’t fully realize that potential,” he said.
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Designing AI to augment human capabilities
Proworker AI will begin with selection within organizations, but its widespread adoption will also likely depend on changes in the incentives guiding AI research and investment. Johnson and others point to public investment in areas such as health care and education, stronger government capacity to evaluate AI, subsidies to support worker-centered tools, tax changes to reduce incentives to favor capital over labor, antitrust enforcement, intellectual property protections for worker voice and worker expertise, and changes to occupational licensing.
However, business practitioners do not have to wait until policy changes are implemented. The direction in which AI moves within your organization is determined by the choices you make today. This means how jobs are redesigned, what optimizations are required of AI systems, and whether workers are treated primarily as a cost to be reduced or as a source of expertise to be expanded.
It starts with asking questions at the beginning of any AI journey: Will the system enable employees to do more valuable things than before? Will it make human judgment more useful, less necessary, reduce their role, or create new responsibilities, capabilities, and learning avenues?
“The priority is to find new things for humans to do and find new ways to extend human capabilities beyond education and skill levels,” Johnson said.
Next steps:
About the experts:
Simon Johnson Ronald A. Kurtz is Professor of Entrepreneurship (1954) and Director of the Global Economics and Management Group at the MIT Sloan School of Management. At M.I.T. James M. and Kathleen D. Stone research inequality and shaping the future of work and research affiliates blueprint lab. Johnson, along with Gary Gensler,power and its consequences”, a podcast about policy, technology, and economics.
In December 2024, Johnson was jointly awarded the Nobel Prize in Economics with Daron Acemoglu and James A. Robinson.For the study of how institutions are formed and influence prosperity”
