# AI Adoption in Asia Faces Challenges Amid Rising Funding Costs

> As the AI investment landscape evolves, the transition from adoption to deployment is essential for monetization. Asia stands out as a key player in this shift, ready to capitalize on the commercialization of AI.

**Source**: eastspring.com | **Published**: 2026-10-28 | **Type**: article

## Key Facts

- AI adoption is rising, but only 36% in Asia report measurable business outcomes, indicating deployment gaps.
- Asia's tech ecosystem benefits from semiconductor strength, crucial for AI infrastructure investment.
- Alibaba's Qwen model shows tangible AI monetisation, enhancing competitive positioning in enterprise services.
- Rising funding costs may hinder AI investment, emphasizing the need for capital discipline in deployments.
- Open-source models risk commoditising AI, challenging differentiation and sustainable earnings for firms.

## Summary

Recent sell-offs in the technology sector have raised concerns about the sustainability of the artificial intelligence (AI) investment cycle. Factors such as rising funding costs, high valuations, and increasing competition have dampened investor sentiment and heightened scrutiny on the returns from AI infrastructure spending. Despite these challenges, demand for AI infrastructure remains strong, signaling that the commercialization of AI is still on track as enterprises increasingly adopt Agentic AI to enhance revenue and operational efficiency.

The shift from mere adoption of AI to its deployment is crucial for monetization. While many companies are integrating AI tools into their operations, the challenge lies in embedding these technologies into products and workflows at scale. This distinction is vital; monetization is not guaranteed by access to AI but rather by its ability to deliver measurable business outcomes. Investors are now more focused on companies that can demonstrate productivity gains and cost savings from AI deployment.

Asia is strategically positioned as the AI cycle transitions from model development to broader deployment. The region benefits from being at the core of the global technology manufacturing ecosystem, with strong semiconductor and infrastructure capabilities. Additionally, Asia's diverse application environments support rapid deployment across various sectors, including commerce and financial services. Notably, the Digital Realty 2026 Global Data Insights Survey indicates that 36% of IT decision-makers in Asia Pacific report measurable business outcomes from AI, highlighting the region's potential.

Evidence of AI generating tangible returns is emerging within Asia's technology sector, particularly among infrastructure and hardware suppliers. Companies are leveraging AI to enhance productivity and streamline processes. For instance, Alibaba's Qwen large language model is being commercialized through its Bailian platform, enabling enterprises to utilize AI for customer service and workflow automation. This reflects a significant step toward monetizing AI through enterprise services and software platforms.

Looking ahead, the evolution of Agentic AI presents a critical test for the market. Currently, its deployment is primarily enterprise-focused, aimed at automating workflows and improving operational efficiency. As AI agents advance from answering queries to executing complex tasks, the architecture of AI compute may shift, necessitating a balanced approach across various hardware components. This could broaden the beneficiary landscape beyond GPUs to include CPUs and other essential technologies in the server supply chain.

However, the path to monetization is fraught with challenges. Despite the rising usage of AI, many companies struggle to prove that customers will pay a premium for AI-enhanced products. The competitive landscape is evolving, with open-source models lowering barriers to entry and commoditizing certain AI capabilities. As AI becomes more accessible, maintaining differentiation will be increasingly difficult. Additionally, concerns around data security, regulatory compliance, and model accuracy will need to be addressed to facilitate further deployment.

As the AI investment cycle enters a more demanding phase, companies must adapt to higher funding costs and stricter capital discipline. Investors are likely to become more selective, prioritizing firms that can effectively translate AI adoption into sustainable earnings growth. The ongoing developments in Asia's technology ecosystem suggest that while opportunities abound, the focus will shift toward companies that can deliver tangible returns on AI investments. This evolving landscape indicates that the successful monetization of AI will hinge on not just technological advancement but also on the ability to navigate competitive pressures and operational challenges effectively.

## Entities

- **Companies**: Microsoft, Alibaba
- **Products**: Qwen large language model
- **Technologies**: Agentic AI, AI infrastructure, AI tools
- **Organizations**: Eastspring Investments, Digital Realty

## Key Concepts

AI adoption, monetisation, Agentic AI, deployment, operational efficiency, Asia's technology ecosystem, AI investment cycle, competition in AI

## Definitions

- **Agentic AI**: A type of AI that automates multi-step workflows and executes tasks to improve operational efficiency.
- **monetisation**: The process of converting AI usage into measurable financial outcomes such as productivity gains and new revenue streams.
- **AI infrastructure**: The underlying technology and systems that support the development and deployment of AI applications.
- **deployment**: The integration of AI into products, workflows, and business processes at scale.
- **digital AI**: AI applications that operate in digital environments, as opposed to physical environments.

## Use Cases

- Customer service automation
- Marketing optimization
- Content generation
- Workflow automation
- Operational efficiency in manufacturing
- AI services for enterprise customers

## Frequently Asked Questions

**What is the current state of AI adoption in Asia?**

AI adoption in Asia is on the rise, with many companies integrating AI tools into their operations. Approximately 36% of IT decision-makers in the region report measurable business outcomes from AI.

**How does Agentic AI differ from traditional AI?**

Agentic AI focuses on automating complex workflows and executing tasks, whereas traditional AI may primarily involve data analysis or answering queries. This shift represents a move towards more practical applications of AI in business.

**What challenges do companies face in monetising AI?**

Companies often struggle to demonstrate that customers are willing to pay more for AI-enabled products. Additionally, competition and the rise of open-source models can complicate efforts to maintain differentiation.

**What advantages does Asia have in the AI market?**

Asia benefits from being at the center of the global technology manufacturing ecosystem, offering diverse application environments and a strong position in robotics and industrial automation, which are crucial for AI deployment.

**What factors could impact the future of AI investment?**

The AI investment cycle is entering a more demanding phase, with rising funding costs and the need for capital discipline becoming critical. Investors are likely to focus on companies that can translate AI adoption into sustainable earnings.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/ai-adoption-in-asia-faces-challenges-amid-rising-funding-costs)
- [Original source](https://www.eastspring.com/insights/deep-dives/from-ai-adoption-to-monetisation)
- [Alibaba Group](https://welcome.ai/company/alibaba-group): Featured company

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Source: Welcome.AI | https://welcome.ai/content/ai-adoption-in-asia-faces-challenges-amid-rising-funding-costs