There are an estimated 180 million utility poles currently in operation in the U.S., and every so often, they need to be inspected. Historically, crews of specialized workers would go from pole to pole, climbing to the top and evaluating the integrity of the structure, regardless of whether or not the pole had a known problem. Today with AI, sensors, and drones, teams can detect the state of this critical infrastructure without physically being there, sending a worker on site only when there’s an issue that needs to be addressed. What’s more, the data made possible by these remote monitoring systems means workers are more informed and prepared for the job when they are deployed to a pole.  

“There’s a lot of diagnostic time to figure out what’s going on, but now imagine that you just show up on a site with the information. So you’re sending somebody to the right spot when there’s an actual issue, and then they’re much more likely to have the right part, or the right truck, or the right materials they need in that moment,” said Alex Hawkinson, CEO of BrightAI, a company using AI solutions to address worker challenges in the energy sector and other blue-collar industries including HVAC, water pipeline, construction, manufacturing, pest control, and field service. 

It’s just one example of how AI-enabled technologies are increasingly helping workers in blue-collar industries do their jobs, saving them time and energy, and reducing their exposure to risky situations (like having to climb to the top of utility poles). The new wave of AI is also allowing workers across these fields to get more out of the technologies they’ve already been using and data they’ve been collecting. AI’s long-term impact on jobs is an increasingly important topic of debate, as analysts and economists look for clues by examining hiring practices at different companies. But in many of these blue-collar fields that are currently struggling with labor shortages, AI is a welcome helper.

Labor shortages drive blue-collar appetite for automation 

Blue-collar industries that require specialized trade skills are some of the most labor-squeezed parts of the workforce, particularly as aging workers who were trained for them years ago start to retire. Between 30% and 50% of water pipeline workers are expected to retire in the next decade, for example, and there aren’t enough younger workers entering the field to replace them. It’s a similar situation in farming: The average age of the U.S. farmer is 58.1 years old, and there are four times as many farmers who are 65 or older than those younger than 35, according to the 2022 U.S. Census of Agriculture. Farming also has to deal with the seasonality of its labor needs, which sway dramatically throughout the year.

“Another big misconception is that autonomy is about labor replacement,” said Willy Pell, CEO of John Deere subsidiary Blue River Technology, regarding AI in the farming industry. “In many cases, it just isn’t there to begin with. So it’s not replacing anything—it’s giving them labor.”

Whether it’s a utility worker inspecting a pole or a farmer harvesting crops, doing more with less time is paramount when there aren’t enough people to get the work done. 

“One of the biggest things is that farmers never have enough time. When we can give them their time back, it makes their lives meaningfully better. They get to spend more time with their family. They get to spend more time running the higher-leverage parts of their business, the higher-value parts of their business, and they have less stress,” said Pell. “There’s an incredible amount of anxiety that comes with not knowing if you can run your business because you’re relying on an extremely sparse, fragile labor force to help you do it. And autonomy helps farmers with this problem.”

Crucially, it’s not just industry leaders who are on board, but workers too. A study on workers’ openness to automation performed by Massachusetts Institute of Technology researchers (and backed by Amazon) found that those without college degrees, or “blue-collar” workers, are more open to automation than those with degrees. According to the study, 27.4% of workers without a college degree said they believe that AI will be beneficial for their job security, compared to 23.7% of workers with a college degree.

AI supercharges the data and technologies workers are already relying on 

For many blue-collar workers, the problems they’re facing on the job are increasingly measurable. For example, Blue River Technology has neural networks that integrate directly into field-spraying machines, detecting the crops and weeds in order to spray herbicides only on the weeds. Technologies like sensors and drones have been around for years, but recent progress in AI is allowing them to derive more benefit from these technologies and the data they produce.

“A lot of factories and other industrial environments have had data around for a long time and haven’t necessarily known what to do with it. Now there are new algorithms and new software that’s allowing these companies to be a lot more intelligent with using that data to make work better,” said Ben Armstrong, coauthor of the study on worker attitudes surrounding automation and an MIT researcher who focuses on the relationship between technology and work, especially in American manufacturing.

BrightAI’s Hawkinson echoes this, saying that “a simple sensor reading isn’t enough to give you the pattern that you care about” and that it’s the maturation of AI that’s made the difference. For example, the company has tapped large language models (LLMs) for voice interaction to allow workers to interact with sensor data via wearable devices, which is crucial for workers who need to have their hands free, as is common in the fields BrightAI operates in. Hawkinson said that companies working with BrightAI’s platform are seeing productivity lifts between 20% and 30% within three to six months of getting up and running.

Overall, a lot of the potential benefits hinge on using AI to improve organization and access to the information that’s vital to get these jobs done. Blue River Technology, for example, is tapping LLMs to turn the very complicated information around equipment error codes into a more readable format with easy-to-understand troubleshooting tips. 

“In a lot of the companies we’re studying, there are these companies’ specific tools that workers can use to solve problems in their job by either doing a different kind of research or trying to organize information in a new way,” Pell said. “And I think for blue-collar workers who have a lot of knowledge about the particular processes and technologies that they work on, that can be really exciting.”

This story was originally featured on Fortune.com

© Illustration by Simon Landrein

Proficiency with AI tools has quickly become a top skill, and companies are working to train their employees how to use it. At the same time, AI is also emerging as a useful training tool in its own right.

Across industries, AI is helping companies create training materials faster and more efficiently, as well as allowing them to design new, more interactive methods to train workers. Artificial intelligence technology is also enabling a shift toward on-the-job instruction that can guide employees in real time. The benefits can be wide-ranging, from massive cost savings for the companies to providing a safer place to simulate tasks in which the cost of an error could be severe. 

Creating training content just got easier

BSH Home Appliances, a subset of the multinational technology Bosch Group, has been using an AI-generated video platform called Synthesia to create material ranging from compliance trainings to technical trainings. The platform allows users to quickly generate videos from prompts or documents and include generic avatars in their videos or even AI avatars of themselves. The videos can range from two minutes to 45 minutes, and the company has been significantly scaling its use of the platform after seeing a 70% cost savings in external video production.

Previously, the company’s learning and development teams had to purchase training video content from a vendor or repeatedly host and record training sessions. Lindsey Bradley, learning and development partner at BSH, says the platform has reduced instruction hours for facilitators and made it possible for a wide variety of stakeholders across the company to create training videos and seamlessly update them as often as needed. The other major benefit has been the ability to instantly translate and localize training content, which is typically a costly yet necessary task for a multinational company with employees in several countries. 

“One of our training sessions that covers energy, environment, and health compliance was created with the platform. In the learning hub for employees, the training session is offered in more than 10 languages, and all the trainer has to do is switch the language in the system,” says Bradley. “The content and script can remain the same. No language experts were required, no actors, etc., because the platform offers a wide range of languages already available that our learning and development teams can choose from for our videos.”

While customers are increasingly using the platform to create videos for all different purposes, employee training and learning and development has been the most common use case so far, says Synthesia cofounder and CEO Victor Riparbelli. The company is continuing to take advantage of the advancements in AI to make videos even more engaging, moving beyond broadcast to interactive choose-your-own-adventure-style videos that provide training paths customized to individual needs.

“An interactive AI video in Synthesia might start off the same for everyone, but it might branch into a more detailed explanation for more advanced viewers, for example,” Riparbelli says. 

Welcome to the simulation

Sometimes, watching a video isn’t enough. That’s where simulation-style training comes into play. 

For example, researchers at New Jersey Institute of Technology, Robert Wood Johnson Medical School, and software company Robust AI have developed an AI-powered program to teach and simulate the basic tenets of laparoscopic surgery. Using the actual tools used in surgery, medical students complete exercises to transfer rings between pegs without dropping them and within short time constraints, mimicking the delicate movements surgeons need to complete with swift precision. 

The team used convolutional neural networks to train the model to recognize the different components. Another neural network, trained on the correct sequence of actions a user should follow, then detects when a user is out of sequence, enabling the program to give feedback to correct their action.

A recent study from this year showed the program is as good and even slightly better than faculty human evaluators when rating surgical skills. Currently, students are using the program informally, but it is headed to become an official part of the curriculum. Since surgical training involves significant oversight and input from senior surgeons who are typically already inundated with responsibilities, and since mistakes come with significant costs, improvements that allow students to do more realistic training in lower-pressure settings have enormous potential.  

“An app like ours helps to reduce medical errors. Students can practice as much as they like in the app before they enter the operating room,” says Usman Roshan, an associate professor of computer science at New Jersey Institute of Technology who’s been collaborating on the program.

Benefits of improved AI-enabled simulation-style training stretch beyond the operating room, however. Strivr, a company combining AI and virtual reality to create immersive training experiences, is serving customers across logistics, transportation, retail, and other industries, such as Walmart, Verizon, and Amazon. Strivr uses AI to create custom content for customers (to build out avatars for 3D environments, for example) and also to power user-facing capabilities that make up the training experience, such as AI-powered dynamic conversation abilities. Previous trainings included only scripted dialogue, but recent advancements in AI are making it possible for users to engage in more naturalistic, nonlinear conversations with the avatars in their training simulations. 

“AI allows for a more realistic, real-world applicable training experience,” says Strivr founder and CEO Derek Belch.

The pursuit of real-time training

Thanks to AI, Strivr is also making progress on its next frontier: augmented-reality-powered experiences that guide workers in real time and connect them to information they need while performing a job. The company is working with 10 design partners to build out early versions of its platform for real-time guidance, called WorkWise.

“The end result of all of this is going to be someone—let’s just say a warehouse worker—putting packages on a truck. They’re going to be wearing smart glasses, and the glasses are going to be telling them what to do in real time. This is kind of ironic, given what we’ve been doing for the last 10 years, but you’re probably not going to have to train people, or you’re going to significantly reduce the amount of training time required,” says Belch.

While smart glasses are still in their infancy and this vision is still a work in progress, AI is already powering real-time guidance experiences via smartphones and other wearable devices, and reducing the need for upfront training as a result. Alex Hawkinson, CEO of BrightAI, a company creating AI solutions for blue-collar industries, for example, worked with a manufacturer of custom pool covers to do just that. Traditionally, two workers would spend an entire afternoon manually measuring a pool and creating a CAD model to design the cover. The company developed an autonomous scanning system and accompanying copilot, or assistant tool, that gives real-time guidance to lead the process, build the models on the spot, and then creates the cost estimate for the job. 

The real-time guidance dramatically speeds up the manufacturing process and reduces measuring errors, Hawkinson says, but it also makes these jobs more available to lesser trained workers. Across the various fields BrightAI works in, like HVAC and energy infrastructure, he says real-time guidance makes it possible to decrease the training requirements and productively deploy new hires.

“It doesn’t have to be a highly trained person to go out and measure. It talks directly to the manufacturing system,” he says. “So while the person is sitting there with the customer at their house, it shows the quote and what the cover is going to look like. It helps them visualize that with a copilot that we built for that worker, and then the customer can say ‘yes’ and it can be there in three days.”

This story was originally featured on Fortune.com

© Illustration by Simon Landrein

As companies race to incorporate AI into their workflows, it’s not only models and tools they’re relying on for a competitive advantage but, increasingly, people. Across industries, 66% of business leaders said they would not hire someone without AI skills, according to the 2024 Work Trend Index Annual Report by Microsoft and LinkedIn.

Company leaders and professionals in the hiring space say they’re now specifically considering candidates’ proficiency with AI tools and sometimes even prioritizing these skills over professional experience. They’re also reimagining their hiring processes, developing new ways to screen for candidates’ familiarity with and ability to use AI tools. Their approaches range from focusing interview conversations on AI—providing an opportunity to gauge a person’s familiarity with and attitude toward the technology—to having candidates complete tasks with AI tools and observing how they use them.

“Every organization is—no matter what the skill set might be—looking to see if they can find someone that potentially has some experience with AI, and specifically generative AI, and now you’ve got agentic AI on the horizon, so they’re definitely looking for people who have experience in those areas,” said Thomas Vick, senior regional director for technology at talent and consulting firm Robert Half.

Skills take center stage

Vick said he noticed the emphasis on AI skills in hiring emerge about a year ago and continue to accelerate ever since. The clear trend is that AI skills are now deemed as important as experience and education.

In the LinkedIn and Microsoft report, which included insights from a survey of 31,000 people from 31 countries, 71% said they would hire a less experienced candidate with AI skills over a more experienced candidate without them. PwC’s 2024 AI Jobs Barometer states that skills sought by employers are changing at a 25% higher rate in occupations most able to use AI, such as developers, statisticians, and judges. Additionally, a study on hiring trends in the U.K. found that candidates with AI skills are landing wages 23% higher compared to those without, making a greater difference than higher degrees up until the PhD level.

Alyssa Cook, a senior managing consultant at hiring and staffing firm Beacon Hill, has also observed that hiring teams are more willing to hire candidates with AI skills. What’s more, she said, skills with specific AI tools a company is using or wants to adopt can even take precedence over an overall greater depth of experience with AI. 

“Companies would rather hire a candidate who has hands-on experience with a particular tool they are implementing if they have the ability and interest to train up on other skills,” she said 
The newfound focus on AI skills in hiring is happening across the various departments of companies. Vick said he’s seen it across accounting, finance, creative roles, and especially technical roles. According to job listing data cited by the Wall Street Journal, one in four U.S. tech jobs posted so far this year are looking for people with AI skills.

The AI test

Automation firm Caddi is one company where this is playing out across the organization. CEO Alejandro Castellano said interviewers regularly ask candidates about their experience using AI tools; for technical candidates, the firm encourages individuals to use AI coding assistants like Cursor, Claude Code, or Copilot during code analysis and technical exercises.

“We want to see how they work in real conditions,” said Castellano.

The approach flips on its head the way companies have traditionally tested candidates for software engineering jobs. Typically, coding tests have been designed to isolate candidates from their real workflows in order to assess their fundamental knowledge. In a world where AI tools are increasingly used to help employees accomplish particular tasks, however, this old approach hardly makes sense. In their day-to-day duties, developers and engineers must be able to work effectively with these systems to enhance their own productivity—not delve into the realm of theory and concepts. 

“We’re moving toward exercises that reflect how engineers actually work, how they search, use AI suggestions, and debug. We care as much about how they solve a problem as we do about the end result,” Castellano said.  

Ehsan Mokhtari, CTO of ChargeLap, a company that creates software for electric-vehicle charging, said encouraging candidates to use AI tools has become a formal part of the firm’s hiring process. The effort started a year ago after it was noticed that candidates were avoiding using AI tools, assuming they would be penalized for it. So the company revamped its hiring process and its broader operations to embrace AI tools, starting with restructuring take-home challenges for technical candidates and then rolling out the effort for positions across the company.

“We started with engineering, but we’re now pushing it org-wide. Sales came next—they were surprisingly fast to adopt AI. Tools like ChatGPT are now common for them for research and outbound comms. We’ve made AI literacy part of departmental OKRs,” Mokhtari said. “That means every function—support, product, sales, engineering, operations—is expected to include it in their hiring considerations.”
In working with clients on their hiring, Robert Half’s Vick has seen a variety of approaches to screen candidates for AI skills. Some companies are turning to their contractors, Vick says, asking those with AI experience to help them evaluate candidates during the interview process.  One of the most popular techniques he’s seen is bringing job candidates into a “sandbox” environment and having them actually show how they would utilize AI within that environment to complete various tasks. It’s the same idea as the reimagined coding assessments, but applicable to any role in the organization.

Attitude goes a long way

While company leaders generally say they would hire a candidate who is proficient with AI over one who isn’t, they also stress that there’s more to it than skills: Attitude also plays a significant role. 

ChargeLab’s Mokhtari explained that he looks at AI proficiency in two layers: skill set and mindset. While skill set is highly desirable, it can also be easily taught. Mindset, however—being proactive in using AI, curious about where it can add value, and not being combative toward it—“is harder to coach and more important long-term,” he said.

Castellano echoes this idea. He’s found that understanding how someone thinks about and works with AI is one of the strongest signals the company has found to gauge that person’s ability to keep delivering value in a fast-changing environment.

“We’re not just looking for people who know the tools,” he said. “We’re looking for those who are curious, adaptable, and thoughtful about how they use AI. That mindset makes the biggest difference.”

This story was originally featured on Fortune.com

© Illustration by Simon Landrein

AI experimentation inside companies has been moving swiftly, but it’s not always going smoothly. The share of companies that scrapped the majority of their AI initiatives jumped from 17% in 2024 to 42% so far this year, according to analysis from S&P Global Market Intelligence based on a survey of over 1,000 respondents. Overall, the average company abandoned 46% of its AI proofs of concept rather than deploying them, according to the data. 

Against the backdrop of more than two years of rapid AI development and the pressure that has come with it, some company leaders facing repeated AI failures are starting to feel fatigued. Employees are feeling it, too: According to a study from Quantum Workplace, employees who consider themselves frequent AI users reported higher levels of burnout (45%) compared to those who infrequently (38%) or never (35%) use AI at work. 

Failure is of course a natural part of R&D and any technology adoption, but many leaders describe feeling a heightened sense of pressure surrounding AI compared to other technology shifts. At the same time, weighty conversations about AI are unfolding far beyond the workplace as AI takes center stage everywhere from schools to geopolitics. 

“Anytime [that] a market, and everyone around you, is beating you over the head with a message on a trending technology, it’s human nature—you just get sick of hearing about it,” said Erik Brown, the AI and emerging tech lead at consulting firm West Monroe.

Failure and pressure drive “AI fatigue”

In his work supporting clients as they explore implementing AI, Brown has observed a significant trend of clients feeling “AI fatigue” and becoming increasingly frustrated with AI proof of concept projects that fail to deliver tangible results. He attributes a lot of the failures to businesses exploring the wrong use cases or misunderstanding the various subsets of AI that are relevant for a job—for example, jumping on large language models (LLMs) to solve a problem because they’ve become popular, when machine learning or another approach would actually be a better fit. The field itself is also evolving so rapidly and is so complex that it creates an environment ripe for fatigue. 

In other cases, the pressure and even excitement about the possibilities can cause companies to take too-big swings without fully thinking them through. Brown describes how one of his clients, a massive global organization, corralled a dozen of its top data scientists into a new “innovation group” tasked with figuring out how to use AI to drive innovation in their products. They built a lot of really cool AI-driven technology, he said, but struggled to get it adopted because it didn’t really solve core business issues, causing a lot of frustration around wasted effort, time, and resources.

“I think it’s so easy with any new technology, especially one that’s getting the attention of AI, to just lead with the tech first,” said Brown. “That’s where I think a lot of this fatigue and initial failures are coming from.”

Eoin Hinchy, cofounder and CEO of workflow automation company Tines, said his team had 70 failures with an AI initiative they were working on over the course of a year before finally landing on a successful iteration. The main technical challenge was around ensuring the environment they were building for the company’s clients to deploy LLMs would be sufficiently secure and private, so they absolutely had to get it right.

“There were certainly moments when we felt like we’d cracked it and, yes, this is it. This is the feature that we need. This is going to be the big-step change—only for us to realize, actually, no, we need to go back to the drawing board,” he said.

Aside from the team that was actually working out the technical solutions, Hinchy said other parts of the organization were also fatigued by the ups and downs. The go-to-market team in particular was trying to do its job in a competitive sales environment where other vendors were releasing similar offerings, yet the pace of getting to the finalized product was out of their hands. Aligning the product and sales team turned out to be the biggest challenge from an organizational standpoint, said Hinchy. 

“There had to be a lot of pep talks, dialogue, and reassurance with the engineers, product team, and our sales folks saying all this blood, sweat, and tears up front in this unglamorous work will be worth it in the end,” he said.

Let functional teams take charge

At cybersecurity company Netskope, chief information security officer James Robinson has felt his fair share of disappointment, describing feeling underwhelmed by agents that failed to deliver on various technical tasks and other investments that didn’t deliver after he got his hopes up. But while he and his engineers have largely stayed motivated by their own inner desires to build and experiment, the company’s governance team is really feeling the fatigue. Their to-do lists often read like work that’s already been completed as they have to race to keep up with approving new efforts, the latest AI tool a team wants to adopt, and everything in between. 

In this case, the solution was all in the process. The company is removing some of the burden by asking specific business units to handle the initial governance steps and setting clear expectations for what needs to be done before approaching the AI governance committee. 

“One of the things that we’re really pushing on and exploring is ways we can put this into business units,” said Robinson. “For instance, with marketing or engineering productivity teams, let them actually do the first round of review. They’re more interested and more motivated for it, honestly, so let them take that review. And then once it gets to the governance team, they can just do some specific deep-dive questions and we can make sure the documentation is done.”

The approach mirrors what West Monroe’s Brown said ultimately helped his client recover from its failed “innovation lab” effort. His team suggested going back to the business units to identify some key challenges and then seeing which might be best suited for an AI solution. Then they broke into smaller teams that included input from the relevant business unit throughout the process, and they were able to experiment and build a prototype that proved AI could help solve one of those problems within a month. Another month and a half later, the first release of that solution was deployed.

Overall, his advice for preventing and overcoming AI fatigue is to start small. 

“There are two things you can do that are counterproductive: One is to just succumb to the fear and do nothing at all, and then eventually your competitors will overtake you. Or you can try to do too much at once or not be focused enough in how you experiment [with] embedding AI in various parts of your business, and that’s going to be overwhelming as well,” he said. “So take a step back, think through in what types of scenarios you can experiment with AI, break into smaller teams in those functional areas, and work in small chunks with some guidance.”

The point of AI, after all, is to help you work smarter, not harder.

This story was originally featured on Fortune.com

© Illustration by Simon Landrein