Introduction
With every major technological evolution comes a certain level of both excitement and anxiety, and the AI revolution is no different.
At every crossroads of history, when industries changed how they functioned, one question kept resurfacing across manufacturing floors, warehouses, maintenance sites, and field operations: What happens to workers?
In the 1970s and 80s, when computers entered workplaces, many feared job loss and the pressure to adapt to unfamiliar technology. But instead of removing job roles, computers ended up creating new skills, new industries, and entirely new ways of working. The current anxiety around AI feels remarkably similar.
Yet, the truth is that most of us have been using AI for years without thinking twice about it. Every time we use Google Maps to find a faster route, recommendations from a streaming platform for a new show, predictions on an e-commerce site about what we might want to buy, or use facial recognition to unlock our smartphone - we are already using artificial intelligence.
For the most part, we welcomed these technologies because they made our lives easier while operating in the background, helping us with routine tasks, and making better decisions by processing information for us.
The conversation changed with the rise of generative AI, agentic systems, and Edge AI. Artificial Intelligence is no longer confined to search results, recommendations, or back-office analytics. It is beginning to influence how decisions are made and how work gets done in real time raising concerns around job displacement, privacy, and human agency.
While the concern may be well intended, we often forget that even in highly automated industries like manufacturing, logistics, energy, and utilities, operations still depend heavily on human judgement and experience. Which is why the real question today is not whether AI will enter frontline work, because it already has. The real question is: How can we use AI to not replace workers, but make them more capable?
Why This AI Revolution is Different for Work
For years, intelligence largely remained confined to dashboards, reports, analytics systems, and back-office automation. It helped organisations analyse information after work had already happened.
Today, AI is moving directly into the flow of work itself. A key driver behind this change is Edge AI. While historically, industrial automation has been about replacing human participation, Edge AI is about creating a different model of work where the AI system offers practical advantages that are especially relevant for frontline operations. By processing data on devices or closer to where work happens, it reduces latency and enables faster decision-making. Workers can access guidance, diagnostics, and support in real time without depending on constant connectivity or centralised systems.
But bringing intelligence closer to the worker also raises important questions around transparency, accountability, and data usage. As AI becomes more embedded in day-to-day operations, ethical considerations become just as important as technical capabilities.
The Frontline Reality: Why Human Expertise Still Matters
Walk into any factory, warehouse, or field operation and you will notice that experienced workers bring in a level of insight that is often not captured in any standard operating procedures. They can predict when a machine may fail only by the sounds it is making. They understand which procedures need adjustment based on weather conditions, equipment age, or operating context. They can identify patterns that never make it into standard operating procedures.
This experience based insight is often referred to as tribal knowledge, and many organisations depend on it far more than they realise. However, the challenge remains in retaining this critical knowledge. As experienced workers retire and workforce turnover increases, industries are facing a widening expertise gap. According to a Deloitte and Manufacturing Institute study, manufacturing alone could face 7.9 million unfilled jobs globally by 2030.
At the same time, operations are becoming increasingly more complex with more systems to manage, stricter regulations to comply with, and faster processes, leaving the workers exhausted and overwhelmed.
AI can help bridge this gap by making expertise accessible when and where it is needed most. But that does not mean replacing experts, but extending the ‘tribal knowledge’ across the workforce.
Productivity Without Workforce Inclusion Will Fail
With more than two decades of experience working with frontline workers in the learning domain, I have to offer a word of caution though. It is my strong belief that frontline AI adoption will fail if workers do not trust the systems that are being introduced.
Resistance from workers is inevitable, if AI is perceived primarily as a monitoring tool or a replacement strategy. No amount of technological sophistication can compensate for lack of workforce trust. The organisations that will succeed with AI are the ones that use it to reduce friction for workers, not increase pressure on them.
This is especially important because frontline work often comes with significant cognitive load. Workers constantly switch between systems, instructions, calls, documentation, and operational decisions while managing real-world environments.
The job of good AI should be to reduce this burden, which means:
The goal should be more capable workers who feel empowered and not redundant.
AI as a Skill Multiplier
As industries globally face an acute skilled labour challenge, AI can become genuinely valuable.
With agentic AI bringing contextual guidance and faster decision-making, it does not replace the workforce. It augments human capability, helping scale expertise.
Imagine a new technician performing a complex maintenance procedure with the support of contextual workflows and guided diagnostics. It will surely reduce onboarding time and improve confidence as knowledge becomes accessible with AI becoming the bridge between experience and execution.
In logistics, AI-driven forecasting and workflow systems are helping employees move into higher-value roles focused on planning and decision-making. A recent study by Politecnico di Milano and Amazon found that 80% of companies using AI successfully reassigned workers to higher-value tasks, while 40% reported improvements in employee digital skills.
In healthcare environments, wearable devices equipped with Edge AI can monitor vital signs and alert medical professionals to anomalies in real time. These systems are in turn helping medical practitioners make faster decisions and improve patient outcomes, particularly where patient to doctor ratios are skewed.
However, their success depends on more than technical performance. Questions around informed consent, data privacy, and secure data processing are equally important. Patient information must be handled responsibly and in compliance with regulations such as HIPAA.
I am highlighting this example to once again draw attention to a broader truth that trust can only be built when organisations combine intelligent systems with clear ethical guardrails.
Building Trust Through Ethical AI Design
Workers are far more likely to embrace AI when they feel it supports them rather than evaluates their performance. Organisations deploying AI on the frontline should ask a few important questions:
Transparency matters the most because only when workers understand the purpose of AI and experience its benefits directly, adoption will become easier and more meaningful. The most successful AI deployments are often the ones workers stop noticing because the technology becomes a seamless part of how work gets done.
Prioritising ethical AI practices shall also deliver tangible business benefits. Be it any technology or policy, it has always been seen that transparent systems build workforce trust and improve adoption. Clear accountability reduces resistance to change from the workers and ethical design also helps minimise the risk of bias and discrimination while supporting compliance with evolving regulatory frameworks.
Ignoring ethical considerations can have dire consequences as biased algorithms can reinforce existing inequalities and lack of transparency can erode workforce confidence, slowing adoption even when the technology itself is effective. In frontline environments, ethical lapses can also create concerns around excessive monitoring and the misuse of sensitive operational data. . Poor privacy protections can lead to data breaches leading to compliance issues.
When it comes to ethical deployment of AI, organisations need clear boundaries around:
Most importantly, workers need to know that human judgement still matters. The best systems will always leave room for human override, contextual judgement, and operational flexibility.
Leadership Responsibility: Building the Future of AI at Work
Technology is after all just science until we decide how to use it and therefore, the future of ethical AI will depend as much on leadership decisions as it does on technological capabilities.
As organisations accelerate AI adoption, leaders have a responsibility to ensure that productivity goals do not come at the cost of workforce confidence. One has to remember that the success of AI initiatives will be determined not only by how well the technology performs, but by how effectively people embrace and trust it.
Therefore, before investing in AI systems, leaders shall have to first invest in the frontline workers who will use them every day.
Key priorities should include:
Key Takeaways for Industry Leaders
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Measure outcomes beyond productivity. Track improvements in worker confidence, safety, knowledge transfer, and operational resilience.
The organisations that succeed will be those that treat AI adoption as a long-term workforce transformation initiative, not simply a technology deployment. To scale sustainably, companies will need to build systems that strengthen human capability because that is where the next productivity leap will come from.
I truly believe the future of work is human capability, not replaced, but augmented by artificial intelligence.
About the Author
Ankush Jagga is the CEO & Co-founder of UnfoldXR, a company dedicated to bringing AI-powered productivity tools to the world's 2.8 billion deskless workers. With over 23 years of experience in enterprise technology, he has built and scaled businesses across learning, technology, and digital innovation.
Prior to UnfoldXR, Ankush led the growth of Tenneo, scaling its platform to over 4 million users, driving a strategic rebrand, expanding global operations, and helping deliver a 5x return to shareholders within three years. A serial entrepreneur, he has founded and scaled ventures across multiple industries, including food-tech, education technology, and digital platforms. Recognized as an ET Business Leader 2023 and a 40 Under 40 entrepreneur, he is passionate about building human-first AI solutions that augment human capability. Through UnfoldXR, he is focused on unlocking productivity and empowering frontline workers at scale.