Feature
Research Report
The AI Transformation 100: Concrete Ideas for Fixing How We Work
Most conversations about AI in the workplace orbit around offices, screens and desk-based jobs. The knowledge worker who uses an AI assistant to analyze spreadsheets has become the default image of AI at work. But that picture leaves out the majority of the world's workforce.
Roughly 80% of the world’s workforce (2.7 billion people) are frontline workers. They stock shelves, deliver packages, fix equipment and keep supply chains moving. For decades, they were all but neglected by digital transformation; a phenomenon that’s changing fast.
A UKG global study of 8,200 frontline employees across 10 countries revealed that more than one-third (34%) now use AI in some capacity on the job. Those who do report significantly lower burnout rates (41%) compared to those who do not (54%). That finding tells you something important: rtificial intelligence at the frontline is not just about operational gains. Done right, it changes the daily experience of the people doing the work.
So what does this actually look like in practice? Here’s a closer look at the most impactful AI use cases that are reinventing work on the front lines right now.
Table of Contents
- 1. Retail: Smarter Inventory, Better Customer Conversations
- 2. Warehousing and Logistics: Speed and Accuracy Matter
- 3. Healthcare: Giving Clinicians a Second Set of Eyes
- 4. Field Service and Industrial: Solving Problems Before They Become Ones
- 5. Aviation and Delivery: AI in the Service of the Customer Moment
- What Actually Makes Frontline AI Work
- The Frontline Is Where AI Earns Its Keep
1. Retail: Smarter Inventory, Better Customer Conversations
You wouldn’t know it by looking at a store, but behind the scenes, there’s a lot of chaos in retail. Workers are always counting inventory, finding merchandise in the stockroom, processing returns and answering customer queries. AI is moving into each of these pain points.
For instance, computer vision systems can now scan shelves in real time and flag gaps before a frontline worker manually eyeballs every last row. Mobile AI assistants now give floor associates instant access to inventory data, product specs and even purchase history when a customer asks a question. The worker stops guessing and starts responding with actual information.
Customer experience benefits follow naturally. When a store associate can pull up real-time stock availability, suggest a complementary product based on buying patterns or flag a price discrepancy on the spot, the interaction becomes easy and more rewarding for both parties.
Related Article: 5 AI Case Studies in Retail
2. Warehousing and Logistics: Speed and Accuracy Matter
There’s not much room for error in warehouse operations. A mispicked item results in a return, a reshipment and an unhappy customer. And at scale, small error rates can add up to big money losses. AI is being deployed here with measurable results.
DHL: Smart Glasses That Navigate the Floor
DHL workers have been testing out AI-enhanced smart glasses that direct them to the right items during picking. The company reports that this has led to a 9.9% improvement in picking accuracy and has significantly reduced training time for new employees. Workers are less reliant on memorizing floor layouts and can now focus on accuracy and pace.
Amazon: Robots as Teammates
Amazon's fulfillment operations are among the most famous demonstrations of AI and robots working at the human scale. The company uses AI–guided robots that work collaboratively with humans to fetch and deliver carts, as well as move products from receiving to inventory.
According to Amazon, this collaborative configuration has accelerated order fulfillment while improving worker safety metrics. The human role has now shifted to oversight, exception handling and the more complex physical tasks that machines cannot perform.
3. Healthcare: Giving Clinicians a Second Set of Eyes
Healthcare is in a league of its own among frontline AI deployment cases. It’s an industry where the stakes are higher, the data more personal and the room for error measured in human lives. For all those reasons, adoption has also been more cautious, but quite meaningful.
The reported numbers have been telling a story of rapid change. Nearly 66% of physicians reported using healthcare AI in 2024, up sharply from 38% in 2023. For clinical staff working on the front lines, three categories make up most of what AI excels at:
- Flagging high-risk patients: AI systems monitor vital signs, lab results and patient history to flag when a patient is deteriorating before they actually crash.
- Prioritizing care tasks: AI scheduling tools help clinical teams triage their workload in real time, focusing on patients who need it most rather than relying on manual check-ins.
- Reading medical images: AI tools help radiologists and clinicians read scans, highlighting things that human eyes can’t see when tired or rushed off their feet. They don’t diagnose, but they do shine a light on areas in need of more scrutiny.
However, patients still trust healthcare professionals more than any other source on AI. In fact, a Philips Future Health Index report found that 86% of patients are more comfortable with AI in their care when their doctor informs them about it.
So, while AI can provide doctors and healthcare providers with a more informed second opinion, they can play a key role in helping patients understand how AI is used in their care.
4. Field Service and Industrial: Solving Problems Before They Become Ones
The last thing a field technician wants is to arrive at a location where critical equipment is already down. Unplanned industrial downtime is not an inconvenience to be minimized. Historically, maintenance schedules were time-based or reactive, but AI changes the logic:
Shell: Extending Asset Life with AI
Shell’s AI predictive maintenance program is one of the largest in the energy industry. The company used C3.ai and Microsoft Azure to create a cloud-based solution that monitors over 10,000 pieces of equipment worldwide, analyzing roughly 20 billion data points each week.
This helps detect anomalies in pumps, compressors, valves and other key components. Shell, according to its 2024 Sustainability Report has seen a 45% reduction in unplanned equipment downtime and maintenance cost reductions of 20-25% from their AI predictive maintenance program.
5. Aviation and Delivery: AI in the Service of the Customer Moment
Two industries where frontline workers face intense customer pressure in real time are commercial aviation and last-mile delivery. Both have found specific, high-impact applications for AI that directly support the people at the frontlines.
Delta Airlines: Faster Resolutions at the Gate
The moment a Delta flight is delayed or canceled, the impact on passengers is felt instantly. Gate agents and customer service staff need to rebook passengers while juggling a crowd of annoyed people.
AI innovation at Delta now ensures automatic rebooking in the background, based on passenger priority, ticket type or connection requirements. According to the company, staff can now resolve disruption issues 60% faster.
Delta has also applied AI to baggage routing and tracking, dubbed "Baggage AI," reducing mishandled bags and giving frontline staff better real-time visibility into where bags actually are.
Domino's: Route Optimization That Reaches the Customer
Domino's has integrated AI into its delivery operations. The system factors in traffic conditions, order readiness, driver locations and delivery windows to generate more efficient routes in real time. The result, according to the company, is an improvement of around 17% in on-time delivery rates and a measurable lift in customer satisfaction scores.
For delivery drivers, this means less guesswork and fewer judgment calls about which route to take at peak hours. The AI handles the routing logic while the driver focuses on execution.
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What Actually Makes Frontline AI Work
One pattern is consistent across the examples above: the AI that works best at the frontline is the kind that fits into an existing workflow rather than asking workers to change how they operate. A recent McKinsey report found that although almost 80% of companies use generative AI, more than 60% report no significant bottom-line impact.
The reason is not the technology itself, but the implementation. Too few workers have the skills or the context to collaborate effectively with AI tools.
Companies getting actual results from implementing AI at the frontline are not just deploying AI and expecting workers to figure it out. They are investing in how the tools are introduced, what training accompanies them and whether the tools actually address problems workers recognize as real.
Related Article: Your Board Wants AI ROI. Here’s Where to Start.
The Frontline Is Where AI Earns Its Keep
Offices and knowledge workers will continue to capture most of the headlines when it comes to AI in the workplace. But the scale of opportunity at the frontline is larger, the operational impact more immediate and the human stakes often more tangible.
AI is now widely acknowledged as the single most important force defining work and society over the next decade. Those forces are already evident across warehouses, hospitals, airport gates and delivery routes. The question for most organizations isn’t if frontline AI will matter, but whether they’ll apply it thoughtfully enough to enjoy the returns.
The real work is closing that gap between what AI can do and what workers can currently do with it. Deal with that gap, and the operational gains at the frontline become significant. Ignore the need, and you get the majority outcome: investment without impact.