AI and the Great Work Reallocation in Asia Pacific

What proportion of your future coworkers will be humans, robots and agentic AI? How will roles change and will humans stay in control?

“AI won’t take your job. It’s somebody using AI who will.”This oft-recurring statement in recent days reveals a growing reality: organizations are being reshaped by the balance of workloads and roles between AI and humans.

It also reinforces the fact that humans will still have a major role to play in the future of work. According to BCG, more than half of executive are considering integrating agents into their AI strategies in 2025, but only 7% believe AI and automation will reduce headcount.  

Ultimately, however, this will also lead to agentic AI and automation further driving companies to reallocate jobs and workflows to better leverage the unique strengths of humans, AI agents, and robots.

This paradigm shift calls on leaders to determine the tasks that are best suited for automation, and those that require human creativity, strategic thinking, and relationship building. How can leaders use agentic AI tools to shape their future workforce?  

DigiconAsia finds out more from Tomur Ho, Director of Engineering, South Asia, UiPath.

Agentic AI and automation are driving companies to reallocate jobs and workflows. A key customer and employee experience issue is that with more ‘hi-tech’, there may be less ‘hi-touch’. Is this a legitimate concern, especially in Asia Pacific?

Agentic automation is fundamentally transforming how work gets done. They allow companies to improve accuracy, and free employees from repetitive tasks, and scale with speed.

However, technology, when deployed without clear intention, can risk creating impersonal experiences – at odds in Asia, where service cultures are often deeply rooted in human connection and local nuance. The “hi-tech” versus “hi-touch” trade-off is especially relevant as some customers still expect a layer of human interaction over automated systems, when dealing with complex or sensitive issues.

According to a PWC survey, consumers in Asia Pacific (APAC) are less comfortable using AI to replace human interaction for higher risk activities, such as executive financial transactions, or providing medical diagnosis, over lower risk activities, such as providing information and recommendations for products and services, drafting emails and tracking orders.

At UiPath, we firmly believe that agentic automation isn’t about replacing people but augmenting them. We’ve seen customers who have successfully adopt AI and automation to improve productivity and efficiencies, while allowing their human employees to focus on more strategic work that requires essential skills like critical thinking, problem solving and creativity.

more strategic work that requires essential skills like critical thinking, problem solving and creativity.

Take WEX as an example. The provider of payment processing and information management services is using enterprise-grade agents from the UiPath Platform to transform business processes, including sales team preparation and document processing.

In its call centers, agentic automation consolidates automations, streamlines processes and empowers agents to interact with systems using natural language. The solution can recognize requests, trigger the appropriate automations, and ease the workload on employees, allowing the company to adapt quickly to business changes and scale operations more efficiently.

In what ways can agentic AI and automation help employees be more human in the world’s most populous region? Wouldn’t AI and automation make more sense if there is a talent shortage here?

The challenge for businesses is finding talent with the right skills for their roles. According to ManpowerGroup, 77% of APAC employers report difficulty finding the talent they need in 2025. But despite this challenge, many are turning to reskilling rather than replacing people, with 35% of leaders in APAC prioritizing upskilling their existing workforce. This signals a clear intent to keep humans in the loop and at the heart of decision-making.

By learning how to use agentic automation, humans can focus on what they do best: solving complex problems, thinking strategically, and building meaningful relationships with customers, while AI agents handle the repetitive, rules-based tasks autonomously.  

Take healthcare, for example. AI agents can rapidly analyze medical data, identify potential diagnoses, and recommend personalized treatments. However, the final decision still lies with the doctor, who now has more time to engage with patients. That’s not just about efficiency; it enables meaningful, human-centered care for every patient. Similarly, for insurance, agents can triage and process straightforward claims instantly, allowing adjusters to focus on complex cases that require nuance and empathy.

How do you see the future of work being reallocated among humans, AI agents, and robots in organizations to leverage each party’s strengths?

As we enter the agentic era, we will see a redistribution of tasks between humans, AI agents and robots. The ultimate goal isn’t just automation for efficiency’s sake, but true augmentation in creating a workplace where people are empowered to focus on what they do best: strategic thinking, creative problem-solving and innovation.

For instance, traditional robots can help to automate rule-based and repetitive tasks, improving efficiency and reducing manual effort. With agentic automation, AI agents can then interpret the data, make decisions in real-time, and manage more complex processes. Humans, who have always been part of the automation loop, can now “monitor the loop” with stronger governance and orchestration, stepping in only when judgment is needed.

Imagine an employee from an insurance company who processes insurance claims. With robots automating tasks like data entry and document processing, an AI agent can accelerate the process by assessing the claim validity, retrieving information from multiple sources and provide recommendations on the claim outcome and coverage gaps if any. The employee can then personalize the experience by using the recommendations to engage with the customer and share other products that may reduce the coverage gaps — focusing

on higher-value work by bringing context, empathy and critical judgement that machines can’t replicate.

When orchestrated well, the seamless collaboration between humans, AI agents and robots can drive greater levels of efficiency, scalability, innovation and smarter decision-making.

For AI strategies to be effective, employees need to level up in AI (literacy, skills, ethical and responsible use etc.). What steps should be taken to enable and empower employees to more effectively work alongside autonomous agents?

There are several ways to empower employees to collaborate better with AI agents.

Firstly, organizations can implement agentic systems in phases, integrating them into existing automated workflows rather than replacing them all at once. They can start with internal, medium-scale processes that pose minimal risks — such as automating employee onboarding FAQs with an AI agent, for example — instead of immediately deploying agents in customer-facing, high-stakes environments like financial transactions or cybersecurity incident response.

This allows teams to build trust, refine governance protocols, and gradually scale capabilities in a controlled manner. A phased approach also facilitates smoother adaptation and minimizes disruption during the transition.

Further, many organizations already possess the foundational elements for successful implementation. For instance, Centers of Excellence (CoEs) have established robust structures, scalability, and return on investment (ROI) and have reusable frameworks. By integrating agentic automation into existing CoEs, organizations can leverage proven structures that support scaling from pilot to full-scale production.

Initiatives like internal hackathons, pilot challenges, or sandbox environments can allow teams to build, test, and iterate with AI agents firsthand. An example is IHH Healthcare Singapore’s Citizen Developer programme, which is rooted in real-world application. A use case that originated from IHH Singapore’s Botathons is the Clinic Listing and Report Automation, which saves the organization approximately S$700,000 a year.

Embedding cross-functional collaboration—bringing together IT, operations, and business users—helps surface diverse use cases and fosters early ownership. This experiential learning builds trust and encourages adoption.

Lastly, AI education and training efforts for employees are often conducted in isolation in many organizations — detached from larger automation programmes, existing tech infrastructure, and longstanding transformation initiatives. Implementing shared KPIs allows organizations to monitor both the technical performance of agentic systems and their business impact.