A restaurant employee packs up a to-go order taken by an artificial intelligence (AI) voice in the drive-thru. A contact center agent calms a disgruntled customer using tips provided by AI observing their live interaction. A grocery clerk restocks shelves using instructions provided by an AI robot rolling through the aisles monitoring inventory. This isn’t the future of front-line work. This is all happening today.

Companies are rapidly implementing AI-enabled technology to boost productivity, reduce costs and improve efficiency. AI has been a part of the workplace for years, but the emergence of generative artificial intelligence (AI) via applications like ChatGPT, Midjourney and Bard is helping organizations identify new use cases and accelerate their digital roadmaps. According to Boston Consulting Group (BCG), AI adoption jumped from 22% in 2018 to nearly 50% in 2023. This is evident on the front-lines like retail, grocery, health care and hospitality, where companies are augmenting human workforces with AI-powered support.

Organizations are reshaping how work gets done on the front-line. Therefore, learning and development (L&D) departments must assess how they enable the people who do this work every day. This is an opportunity to overcome longstanding challenges with front-line learning, since 29% of front-line employees say they lack adequate training, according to Axonify’s 2023 The Deskless Report. Comprising 80% of the global workforce — along with their distribution across locations and geographies — reaching front-line workers with traditional learning methods can be challenging.

They don’t have time to sit in a traditional classroom or participate in virtual workshops, and they lack the autonomy to step away from the operation to take an eLearning course. It’s no wonder front-line employees are already falling behind in the AI-powered workplace: Just 14% of deskless workers receive training on relevant skills as compared to 44% of managers, according to the report.

Thankfully, AI isn’t just a new challenge. It’s also a timely solution. This article will evaluate how organizations can apply AI to transform front-line enablement with an example from Sol, a 22-year-old grocery worker.

On-Demand Support

Consider this example:

Sol’s been on the job for eight months. They’re a quick learner, but there’s still plenty they don’t know. For example, a customer approached them this morning while stocking the cheese case and inquired of the difference between brie and gorgonzola cheese. Sol had no idea. But instead of dismissing the customer or finding someone else to help, Sol pulled out their smartphone and asked the store’s digital assistant: “¿Cuál es la diferencia entre brie y gorgonzola?” The generative AI-powered chatbot responded with a comparison of the two cheeses as well as recommended wine pairings (Champagne for brie, moscato for gorgonzola). It also answered Sol’s question in Spanish, their preferred language, even though the source information in the company knowledge base was written in English. 

Front-line employees are on the store floor serving customers, on the manufacturing line working with heavy machinery or in trucks delivering products. Access to reliable information can make or break their performance. AI eliminates the need to sift through paper binders or rely on the person next to them. Instead, the collective knowledge of the organization is available on demand via any device: point of sale (POS), handheld, tablet or personal smartphone.

AI can help employees overcome another major source of workplace inequity: language barriers. Translating content into multiple languages usually requires time and resources. Most organizations decide to limit translated offerings to only the most commonly used languages. This means employees with alternative preferences are left struggling to comprehend the training and new information. AI can repair this common predicament with machine translation to allow employees to interact with digital experiences in their preferred language. Furthermore, generative AI can automatically adapt content to a person’s reading level, making complex job information easier to understand. For example, text-to-speech technology can make information more accessible for employees with limited reading skills.

Personalized Learning

Consider this example:

Sol wants to improve their knowledge and skill so they can do a good job, get a positive performance review and maybe be considered for the next supervisor promotion. They learn a lot on the job, but is so busy during eight-hour shifts to take classes or complete online courses. Instead, they signed up for a daily microlearning program. Every shift, they get a notification on their smartphone to complete a five-minute training activity. Today’s session featured a scenario on fresh produce rotation. Sol cross-trained in the produce department last week, so the microlearning automatically adapted to focus on their new interest. 

How can an L&D team with 100 people provide training that meets the needs of 100,000 front-line workers? It’s always been an impossible math problem. L&D has limited resources and a never-ending supply of training requests. We end up addressing the most-requested topics with one-size-fits-all training.

AI can help deliver personalized learning at scale. Adaptive learning technology applies AI and data to identify each worker’s knowledge and skill gaps. Employees spend their limited training time working on timely, relevant topics instead of checking the same boxes as everyone else. This accelerates skill development and boosts engagement because employees know they’ll find something useful every time they engage with L&D programs.

Generative AI addresses another essential part of a personalized learning strategy: content development. To meet individual development needs, organizations need lots of content. Instead of hundreds of generic courses, you need thousands of targeted enablement assets: articles, videos, modules, job aids, assessments, etc. Generative AI can scale content development by automatically generating content from source materials. According to the Deskless report, using generative AI is 30% faster at generating content than starting from scratch. Then, they make the necessary adjustments to the first draft before pushing content to employees.

Enabled Managers

Consider this example:

Sol relies on the manager to provide feedback on their performance. Sol typically meets with the manager for a few minutes every shift and is scheduled for monthly check-ins. Today, the  manager stopped by for a quick chat about safety. She mentioned Sol was observed lifting with the incorrect form during a recent safety audit. She asked Sol to demonstrate the proper form and mentioned they would see a refresher module on safe lifting during tomorrow’s microlearning session. The manager then thanked Sol for their hard work. 

Front-line managers wear a lot of hats. Often, they’re a manager, mentor, coach, teacher, counselor and friend. They’re also overworked and under-supported. In fact, according to Axonify’s report, 49% of front-line managers feel burned out every day. Employees rely on their managers to provide feedback and support, especially when things get tough. When someone trusts their manager, they’re less likely to quit. But managers can’t be everywhere all the time. They need help, too.

AI can augment a manager’s ability to support their team. Coaching technology eliminates the need to sift through reports to find potential problems. AI generates a list of prioritized action items, including employees who will most benefit from coaching interactions and which topics to address. Predictive analytics can help managers proactively address small issues, like shifts in safety behaviors, sales results or customer satisfaction, before they become major problems. This enhanced technology toolkit can reduce the time managers spend on administrative tasks. This gets them out of the office and into the operation where they can build relationships with their team members.

The Future is Already Here — It’s Just Not Evenly Distributed

It’s easy to feel like a number when you work on the front-line. Thousands of people do the same job. You rarely see the corporate team that makes all the decisions. You’re usually the last to find out about big workplace changes. With all the noise around AI, front-line workers are already less optimistic than management when it comes to its potential to improve the workplace (42% vs 62%).

L&D can bring a new perspective to AI on the front-line. Rather than leave people to worry about the unknown, we can demonstrate the benefits AI can bring to their work experience. We can make sure everyone has access to the information needed to solve problems — no matter where they work, which language they speak or how long they’ve been on the job. We can make sure everyone has the opportunity to build new skills — even if they only have a few minutes to focus on training during their shifts. We can make sure everyone gets the feedback they need to improve their performance and feel confident in their ability to do a good job.

Front-line workers like hypothetical Sol deserve this version of the workplace — one that’s equitable, personal and human. And we can give it to them today.