Across Asia's factories, hospitals, and retail outlets, AI is revolutionising frontline work by collaborating with employees in real-time—enhancing safety, personalising customer service, and providing instant access to knowledge.
This transformation promises to enhance operational safety, personalise customer service, and provide instant access to knowledge, thereby improving both productivity and employee experience.
However, the deployment of AI in these sectors faces significant challenges, including diverse and evolving regulations, labour laws, data privacy concerns, and issues related to trust, fairness, and digital literacy.
Successful AI adoption requires a human-centred approach that involves frontline workers in the design and feedback process, offers tailored training to bridge digital divides, and implements ethical governance to ensure fairness and accountability.
Beyond productivity, organisations must measure impact through employee engagement and customer experience, investing in people and fostering human-AI collaboration to build resilient, agile teams ready for Asia's dynamic future.
AI's impact on frontline work in Asia
Steven Kramer, CEO and President of WorkJam, believed that AI reshapes frontline roles primarily by improving clarity in communication and optimising task execution. He posits that AI enables head offices to construct clear, optimised instructions that are pushed down to frontline workers, reducing vagueness and supporting multiple languages, which is crucial in Asia's multilingual work environments.
"From a frontline employee's perspective, AI also has significant benefits to them as well. The AI allows them to optimise the execution of the task to be able to," he added.
For frontline employees, AI tools serve as intelligent assistants, providing a global search capability to find answers and optimise task performance quickly. Furthermore, AI-powered reporting offers valuable business insights that help organisations iterate and continuously improve productivity.
Promising AI tools for frontline teams
AI-enabled tools, such as agentic AI systems, empower frontline teams by understanding context, making informed decisions, and acting autonomously to support customer interactions and operational workflows.
In manufacturing and construction, AI enhances worker safety through predictive analytics and hazard detection. The retail and service sectors benefit from AI-driven, personalised customer assistance and automated scheduling, while healthcare frontline workers utilise AI for knowledge access and decision support to enhance patient care.
These tools differ by industry but share a common goal: augmenting human capabilities rather than replacing them. Kramer outlined three key AI-enabled tools empowering frontline teams:
AI agents tailored to industry and role: WorkJam integrates Google's AI tools, including Gemini*, to provide personalised AI agents that answer employee questions contextually based on their specific roles—whether in manufacturing, retail, or service industries. This role-based personalisation ensures that the information provided is both relevant and accurate.
* Gemini is not available in markets like Hong Kong.
AI-powered communication and translation: Given Asia's linguistic diversity, AI-driven translation services embedded in apps like WorkJam eliminate language barriers, fostering inclusivity and clear communication across teams speaking over 20 native languages.
AI-driven task optimisation: AI models learn from employee actions to continuously optimise workflows, saving time and reducing labour costs for organisations.
Navigating regulatory and ethical challenges
Regulatory environments across Asia vary widely, with some markets, such as Singapore, having mature AI frameworks while others are still developing their policies. Kramer explains that compliance is managed through configurable AI tools that respect local laws, such as Australia's "right to disconnect," ensuring employees are not disturbed outside working hours.
AI tools are designed to deliver answers strictly contextual to the user's job function, supporting compliance with diverse labour laws and safety standards. Organisations operating across multiple countries benefit from flexible platforms that allow region-specific configurations and integration with existing IT investments.
Deploying AI on the frontline introduces challenges around trust, reliability, and fairness. Frontline workers often face barriers, including insufficient training, unclear guidance on AI tool usage, and concerns about job security.
Ensuring AI systems are transparent, unbiased, and respect data privacy is critical, especially given the diverse regulatory landscapes in Asia that govern labour laws, safety standards, and personal data protection. Organisations must navigate these compliance requirements carefully to avoid legal pitfalls and foster worker confidence.
Building trust and ensuring fairness
Building trust in AI tools among frontline workers requires involving them in the design and feedback process. Kramer emphasises that trust grows when employees experience tangible benefits such as time saved, easier task execution, and improved customer service.
Organisations often start with pilot programs, gathering feedback and making iterative improvements while communicating changes transparently. This collaborative approach helps reduce AI errors ("hallucinations") and fosters acceptance and viral adoption within the workforce.
User trust primarily stems from frontline employees experiencing real benefits—such as saving time, serving customers more effectively, and executing tasks with ease. This positive experience spreads virally within organisations, accelerating adoption.
Organisations also use bidirectional communication channels to collect employee feedback and adjust AI tools accordingly, reinforcing trust and fairness.
Enhancing training, knowledge access, and inclusion
AI significantly enhances frontline training and ongoing learning ("everboarding") by providing intuitive, human-like conversational agents that span multiple organisational systems.
Employees can quickly access relevant information—such as HR policies or customer service procedures—without having to navigate complex intranets or manuals.
Kramer explains that WorkJam's AI is designed to feel natural and seamless, requiring no formal user manuals, much like popular social media platforms, thereby lowering the barriers to adoption for workers of varying digital literacy levels.
However, digital literacy gaps and language barriers risk excluding some workers from the benefits of AI. To prevent this, companies should design AI tools with accessibility in mind and offer inclusive training programmes that consider varying skill levels and cultural contexts.
Given the multigenerational nature of many Asian workforces, from workers in their 20s to those in their 70s and 80s, AI adoption must be inclusive.
This entails designing user-friendly interfaces, providing tailored training to bridge digital divides, and ensuring AI tools are accessible regardless of age or technical proficiency.
Human-centred design and ongoing support are critical to fostering digital confidence among all frontline employees.
Measuring impact and worker involvement
Organisations increasingly measure AI effectiveness not only through productivity gains but also by employee engagement and customer satisfaction metrics.
Frontline workers' feedback is becoming vital in refining AI tools, as their insights help tailor AI functionalities to real-world needs, driving better adoption and outcomes. Empowering frontline employees to co-create AI solutions fosters a sense of ownership and trust.
Acknowledging the multifaceted nature of AI, he posits that "by optimising the activities that frontline employees are doing, you're able to save significant labour dollars by providing less frustration and less friction on the job you're going to be able to reduce your attrition and have less turnover, which results in really high ROI."
Ethical and governance priorities
Ethical considerations, such as fairness, transparency, and accountability, must guide the scaling of AI across large frontline workforces. Governance frameworks should address potential biases, ensure data security, and maintain human oversight to prevent over-reliance on AI decisions.
"I think one of the most important things that an organisation can do is to set up an AI Board within their organisation," posits Kramer.
"The goal of that board is to make sure that the AI tools are compliant, that they're secure, they follow best practices and follow corporate policies." Steven Kramer
Balancing innovation with ethical responsibility is crucial amid global socio-economic volatility and borderless cyber threats.
Preparing for an AI-driven future
To prepare frontline workers for AI-driven transformation, organisations in Asia must invest in continuous upskilling, foster a culture of collaboration between humans and AI, and implement supportive policies that mitigate workforce attrition and burnout.
By doing so, they can harness AI's full potential to create resilient, agile frontline teams capable of thriving in a rapidly changing digital economy.
Click on the PodChats player to listen to Kramer outline in greater detail his opinion on the following questions:
- How is artificial intelligence (AI) reshaping the daily tasks and overall roles of frontline workers across Asia's diverse industries?
- Name 3 of the most promising AI-enabled tools currently empowering frontline teams and how these technologies vary between sectors such as healthcare, retail, manufacturing, and logistics.
- How do evolving regulatory frameworks in Asia—covering data privacy, labour laws, and safety standards—affect the deployment and design of AI solutions for frontline work?
- What are the key challenges organisations face in building trust, ensuring reliability, and maintaining fairness when integrating AI tools alongside tools used by frontline employees?
- In what ways is AI enhancing frontline training, knowledge access, and on-the-job learning, and what potential drawbacks should companies be mindful of?
- How can organisations ensure AI adoption is inclusive, addressing digital literacy gaps and language barriers to prevent frontline worker exclusion?
- How can frontline workers be more actively involved in shaping AI tools, and what ethical or governance considerations should companies prioritise as they scale AI across large, distributed workforces?
- Outside of AI boards to ensure better/proper use of the technology, what metrics will be of interest to the CFO and CIO in terms of metrics for measuring the effectiveness of the technology (the impact and return on investment of AI deployments on the frontline, balancing productivity with employee and customer experience)?
