For both customer-facing and behind the scenes workers, AI is a key tool to help them increase productivity, improve the quality of their work, and achieve faster time to value.
By adopting AI tools and technology at the frontline worker level, organizations can realize productivity improvements, efficiencies, and other benefits. This section explores who is using AI and how they're using it, who is seeing cost reductions from AI, and who is committing to increasing their AI use in the future.
In 2025, over half of frontline workers now use AI in their day-to-day work. Fifty-six percent say they use AI applications in their day-to-day work, with 50% using one to three applications, and 6% using four or more applications.
Of those who do use AI daily, 41% are in retail, another 41% are in manufacturing, 9% are in building supply, and 9% are in distribution.
Daily AI users work mostly in a warehouse or factory (55%), with the rest working in a store or other on-site operation (45%). They also mostly perform hands-on operational tasks like assembly, production, maintenance, or stocking (67%), with the rest working directly with customers in customer service, sales, or client support (33%).
The big idea: Overall, daily AI use has found its way to frontline workers across industries in both customer-facing and behind-the scenes roles.
Do you currently use AI in your day-to-day work?
Seventy-three percent have realized cost reductions due to AI-driven automation, while 15% have not. Twelve percent are unsure if their company has or not. This question was only asked of those who use AI in their day-to-day work, meaning not all of those who use AI have seen it lead to cost reductions in their company.
Who's realizing cost reduction? Of those who say they are, 58% work in a warehouse or factory, and 69% perform hands-on operational tasks. Additionally, 80% of those committing to increasing their AI use have realized cost reductions as well.
The big idea: Cost reductions are a big result of AI use and are most likely contributing to increasing AI use and investments.
Has your organization realized cost reductions due to AI-driven automation?
In addition to over half using AI in their day-to-day work, half (51%) say that their company has shared plans to increase AI use in the next one to three years. Fort-five percent say no plans have been shared yet, and 4% know that their company plans to decrease AI use in that time frame.
But the plans for increased AI use aren't coming out of nowhere. Of those who use AI tools every day, 79% say their company plans to increase AI use in the next few years. More importantly, of those who have realized cost reductions due to AI-driven automation, 87% plan to increase AI use.
Conversely, of those who do not use AI in their daily work, 83% say their company has not shared plans about future AI investments, and only 16% say their company plans to increase AI use in the future.
The big idea: It may seem simple, but AI use and realizing value from that use results in more AI investment. However, of those who have not realized cost reductions due to AI-driven automation, 58% are still committing to increase their AI use over the next one to three years — perhaps discerning they need more time.
Has your company shared plans to increase or decrease use of AI?
Frontline workers are using AI for inventory management (45%), quality control (37%), forecasting (29%), workflow optimization (26%), and process automation (23%). Across the industries surveyed, these top use cases remain the same, though those who work directly with customers say they also use AI for quote automation.
What they're not using AI for is route optimization for drivers (5%), predictive machine maintenance to identify quality issues (6%), product suggestions for items commonly purchased together (8%), optimizing product placement (9%), and predictive machine maintenance (14%).
The big idea: Overall, respondents are using AI to deliver high-level business value. The processes they're using AI for directly impact cost savings, efficiency, and improve quality. It's not that the tasks they're not using AI for won't impact the bottom line; it's that they won't have as much of an impact as these other big-picture tasks.
How is AI used in your day-to-day work?
Is one industry or job role using AI more than another, and that's why half are using AI daily and half aren't? Not exactly.
Of those in retail who do use AI daily, 68% work in a store and 57% work directly with customers. Of those in manufacturing who do use AI daily, 77% work in a warehouse or factory, and 88% perform hands-on operational tasks.
The assumption would be that those who don't use AI every day would fall into opposing job duties. However, those who don't use AI also fall into the same job roles.
Of those in retail who don't use AI daily, 90% work in a store, and 68% work directly with customers. Of those in manufacturing who don't use AI daily, 89% work in a warehouse or factory, and 92% perform hands-on operational tasks.
The big idea: Why is it that one retail worker in a store working directly with customers is using AI in their day- to-day tasks, yet another isn't? Same with manufacturing: Why is one frontline worker using AI while another in a similar role isn't? This seems to indicate that there's not an issue with applicable technology being available but rather with other hurdles limiting the adoption of, investment in, and commitment to AI.
Looking closer at AI use in day-to-day work shows that while revenue doesn't seem to matter when it comes to AI investments, company employee size does.
Of those who use AI in their day-to-day work, the largest segment (29%) work at companies with 50 to 249 employees, while the second largest segment (25%) have 250 to 999 employees. Similarly, of those who say their company is increasing their AI investments over the next one to three years, the largest segment (29%) works at companies with 50 to 249 employees, while the second largest segment (26%) works at companies with 250 to 999 employees.
Of those who do not use AI in their day-to-day work, the largest segment (29%) works at companies with 5000 or more employees, while the second largest segment (21%) works at companies with one to 49 employees. Similarly, of those who say their company is decreasing or has no plans for AI investments, the largest segment (27%) works at companies with 5000 or more employees, while the second largest segment (23%) works at companies with one to 49 employees.
Smaller businesses may not have the capital to invest in AI, but may also be smaller operations that may not want to give up hands-on interactions to AI functions. Or they may deem themselves too small to find any AI use case.
The big idea: Is the AI adoption sweet spot with medium- sized businesses? This size would indicate having the capital to invest in AI tools, but having a small enough staff to deploy those tools to, along with the training needed.
It seems that massive companies would have a lot of use for AI. However, the monetary investment may be too big, or the hurdle of training many staff on AI tools may be too vast. Larger companies may also be relegating AI tools to the home office, and haven't yet trickled them down to the front line.
As AI tools and technology evolve, the question is whether users will even need specialized training like they once did, or if AI can be intuitive enough to simply plug-and-play. This section looks at who is receiving training on AI tools and how frontline workers are achieving results without formal training.
There seems to be vast AI use yet little training. Overall, 40% of respondents say their organization has created programs or initiatives to train or upskill workers on how to use AI. But 60% say there is no training available: 32% do not have any AI training in place, and 28% say it's been discussed but they haven't seen anything yet.
However, of those who use AI tools in their day-to day work, 69% say their company offers training. Sixty-eight percent of those who use one to three applications say their organization provides training, while 75% of those who use four or more applications say their organization provides training.
For those who do not use AI every day, 97% either don't have training or it's been discussed but hasn't been implemented.
The big idea: It should be no surprise that companies whose frontline workers use AI every day have, for the most part, implemented training programs to help their workers use those AI tools. This still shows that 31% of those who use AI in their day-to-day work are doing so without any formal training — meaning they're either self-taught or the tools may be intuitive enough to not necessarily require any formal training.
Has your organization created training or upskilling for using AI?
Of the companies that plan to increase their AI use over the next one to three years, 69% currently have training or upskilling programs in place.
The big idea: 31% will then need to look at investing in AI training or upskilling programs to keep pace with their planned increase in AI use and investment. Or they can plan to invest in tools that are intuitive and easy to use, minimizing the need for training programs.
In looking at those respondents who use AI in their day-to-day work but whose companies do not have training programs, 49% say their company has still realized cost reductions due to AI-driven automation.
The big idea: Even without training, companies using AI are still realizing value from their AI tools and investments. This could indicate that these tools are easy enough to use without training.
Has your organization realized cost reductions due to AI-driven automation?
Not everyone thinks the same way about AI's rapid adoption. This section explores what frontline workers think about AI and its increasing implementation.
Which of the following best describes your perspective on AI investment by your company?
Over half of frontline workers now use AI in their day-to-day work, marking a tipping point into majority adoption. And while the rest say they don't use AI daily, they may still use it, just less frequently, resulting in an even higher percentage of AI use by frontline workers.
Ultimately, though, the story essentially splits into two narratives: organizations who are moving forward with AI adoption and investments, and those who are standing still. Those who actively use AI are seeing more AI use and investments, more training, and cost reductions from their efforts. The hesitation could be due to thinking they're too small or too big for AI integration down to the frontline worker level. It could also be a lack of understanding of appropriate use cases, or it could be simply resistance. It seems that once tried, organizations will see the benefits and returns of AI and want to keep going; the hurdle, however, is the initial investment or finding the right use cases that align with frontline worker needs.
Outside of confidence hurdles, use and adoption hurdles seem to be falling, as demonstrated by the fact that many of those who use AI and see returns from it don't have formal training in their companies. This hints at AI tools becoming more intuitive and easy to use right out of the box. As such, organizations can look for AI solutions that have intuitive and familiar interfaces so that new technology adoption isn't such a hurdle. Bringing down any resistance around the learning curve can help with more widespread adoption and decrease time to value.
Those who invest in AI tools that help with big-picture savings and efficiency, all the way down to their frontline workers, will have the competitive advantage moving forward. Those who continue to hesitate or who don't commit to planned AI increases are now already in the minority.