As AI continues to evolve, a new stage of automation is emerging—Agentic Automation, where systems can act and decide independently. With its strong semiconductor industry, growing AI ecosystem, and high enterprise adoption, Taiwan is well positioned to lead this shift. This is not just a technological upgrade, but a transformation in how businesses operate and make decisions.

Introduction: Why Taiwan Matters in the AI Automation Era

In the global AI wave, Taiwan is emerging not only as the “factory of the world” but also as a core engine of the AI economy. The boom in artificial intelligence has significantly accelerated economic growth: Taiwan’s GDP reached approximately 8.6% in 2025 – the highest level in 15 years, largely driven by global demand for AI and semiconductors (according to AP News).

The government is also investing heavily in AI development, allocating around NT$3.2 billion to AI R&D, accounting for roughly 15% of total national R&D spending (according to industry statistics reports). At the enterprise level, 76.2% of companies have already implemented at least one AI use case (according to EE Times Asia).

Meanwhile, Taiwan’s AI ecosystem continues to expand, with approximately 1,250 AI startups and 68,000 AI professionals (according to Zipdo statistics).

Most importantly, Taiwan produces over 90% of the world’s most advanced semiconductors, forming the backbone of modern AI models and high-performance computing systems (according to EE Times Asia). This makes Taiwan an irreplaceable hub in the global AI value chain.

Alongside its hardware dominance, Taiwan is entering a new phase of automation:

• Traditional Automation (rule-based)
• Intelligent Automation (AI-assisted)
• Agentic Automation (autonomous AI systems)

This shift is not merely technological-it is fundamentally transforming how businesses operate and make decisions.

What is Agentic Automation?

Definition

Agentic Automation represents the most advanced stage of automation, where AI agents can set goals, make decisions, and execute multi-step tasks autonomously without continuous human intervention.

AI agents are projected to generate up to $450 billion in global economic value by 2028 (according to Taiwan News).

Core differences:

• RPA → rule-based execution
• Intelligent Automation → AI-assisted workflows
• Agentic Automation → autonomous execution

Key Capabilities

Agentic systems typically include:

• Goal-driven execution
• Multi-step reasoning
• Self-learning and adaptation
• System orchestration across platforms

These capabilities enable AI to evolve from a simple tool into a true “digital worker.”

More importantly, Agentic Automation is not just a technology shift-it represents a new human–AI collaboration model, where AI handles execution while humans focus on strategy and oversight.

Use Cases of Agentic Automation in Taiwan

AI adoption in Taiwan is rapidly shifting from experimentation to real-world deployment. Notably, nearly 48% of enterprises expect short-term ROI from AI agents, indicating strong confidence in the commercial value of AI-driven automation (according to Workday research)

Manufacturing & Semiconductor

Manufacturing remains the most advanced sector in adopting Agentic Automation, largely due to Taiwan’s global leadership in semiconductors.

AI-powered smart factories are already generating up to NT$2.1 trillion in revenue, while AI-driven optimization has reduced energy consumption by approximately 22% (according to industry statistics).

Unlike traditional automation, agentic systems can:

• monitor production data continuously
• detect anomalies and adjust workflows in real time
• predict equipment failures before they occur

This enables a shift from reactive operations to self-optimizing production systems, where decisions are increasingly made by AI rather than humans.

BFSI (Banking & Finance)

In the financial sector, where speed and accuracy are critical, Agentic Automation is transforming how decisions are made.

AI-based fraud detection systems have already saved around NT$500 million, while algorithmic trading accounts for approximately 40% of total trading volume (according to industry reports).

Building on this, agentic systems can:

• automate the entire loan approval process from application to decision
• monitor transactions in real time to detect fraud
• personalize financial products based on user behavior

This allows financial institutions to move toward real-time, data-driven decision-making at scale, reducing reliance on manual processes.

Healthcare

Healthcare is emerging as a high-impact area for Agentic Automation, particularly in diagnostics and hospital operations.

AI diagnostic systems can achieve accuracy rates of up to 92%, compared to 85% for human doctors (according to healthcare AI studies). However, the real value lies beyond diagnostics.

Agentic systems can:

• coordinate hospital workflows across departments
• assist doctors with real-time insights and recommendations
• manage patient journeys from admission to discharge

By automating operational complexity, healthcare providers can improve both efficiency and quality of care.

Commercial & Service Sector

Taiwan is also expanding AI adoption into retail and service industries, supported by strong government initiatives.

To date, more than 12,000 enterprises have been supported in adopting AI, with a national target of 20,000 AI-enabled retail stores (according to Taiwan News).

In practice, AI agents are being used to:

• Optimize inventory and demand forecasting
• Dynamically adjust pricing strategies
• Automate customer service interactions
• Streamline logistics and delivery

These applications help businesses deliver faster, more personalized, and more cost-efficient services, which is critical in increasingly competitive markets.

Challenges & Risks

Despite its potential, Agentic Automation introduces new layers of complexity and risk that organizations must address carefully.

Technical Challenges

One of the biggest barriers is integrating AI systems with existing infrastructure.

Many Taiwanese enterprises still rely on legacy systems that were not designed to support AI-driven workflows. As a result:

• Around 60–61% of companies report difficulties in system integration
• Approximately 35% face compatibility issues with legacy systems

This creates bottlenecks that limit the scalability of Agentic Automation.

Workforce Impact – Skill Gap

The shift toward Agentic Automation is also reshaping workforce requirements.

Many organizations face a shortage of talent with the necessary AI expertise:

• 63% of companies cite skill gaps as a major barrier
• 40% lack in-house AI capabilities

Unlike traditional IT systems, Agentic Automation requires new skill sets, including:

• AI engineering
• Prompt engineering
• System orchestration

This creates an urgent need for reskilling and upskilling programs to prepare the workforce for human–AI collaboration.

How Taiwan Businesses Can Get Started to Approach Agentic Automation

• Start with low-risk, high-impact use cases
  Businesses should begin with simple but valuable processes such as customer support routing or document processing. This allows them to test Agentic Automation in a controlled environment while minimizing operational risks.
• Combine RPA + AI + agents for gradual transformation
  Instead of replacing entire systems, companies can integrate RPA for task execution, AI for decision-making, and agents for orchestration. This step-by-step approach ensures stability while increasing automation capabilities over time.
• Build a robust AI governance framework
  As systems become more autonomous, clear policies on data usage, decision transparency, and risk control are essential. This helps ensure that agents operate reliably and in compliance with regulations.
• Partner with technology providers and AI experts
  Collaborating with experienced vendors and AI specialists can accelerate implementation and reduce risks. It also allows businesses to adopt best practices and scale more effectively.