The Great AI Gold Rush IDC's Data & AI Pulse: Asia Pacific commissioned by SAS has 500+ senior exec perspectives from 8 countries to help firms drive competitive advantage

The Great AI Gold Rush

New research uncovers the hype and hard truths of AI investments in Asia Pacific

AI is at the forefront of nearly every business’s growth strategy, with grand expectations driving ambitious investments. Is this optimism rooted in reality? Not entirely. Our study reveals that most Asia Pacific organisations remain in the early to mid-stages of AI maturity due to the lack of a clear, data mismanagement, and skills shortage.

We uncover how businesses chart their unique AI transformation journeys—whether it is barriers to AI implementation or lessons from AI leaders—in the Data and AI Pulse: Asia Pacific 2024 study, commissioned by SAS.

These findings offer a guide to the critical milestones you should aim for on your journey toward AI maturity.

Methodology

When

Where

Who

How many

Which industries

June 2024

Six markets: Australia, China, India, Japan, South Korea, and Southeast Asia (Malaysia, Singapore, and Thailand)

Directors, VPs, C-levels, professionals, and specialists

Over 500 enterprises

Key industries include financial services, insurance, government, healthcare, and life sciences

Learn more about this study

AI maturity in Asia Pacific:

AI spending is on the rise

We know AI has captured the imagination of businesses across every industry, but how much of this excitement has translated into tangible, strategic action? What forces propelled AI from being a buzzword to a transformative force that has reshaped business models, driven efficiencies, and fuelled innovation?

$45 billion in 2024 and $110 billion by 2028 with a 24% CAGR.

Expectations are high with about 40% expecting up to 3x ROI from their AI investments.

Note: AI spending figures in Asia Pacific is sourced from IDC Worldwide AI and GenAI Spending Guide Forecast, AP, (August 2024 v2) Source: IDC’s Data and AI Pulse: Asia Pacific study, 2024, (n=509)

What’s fuelling the frenzy?

Fear of Missing Out (FOMO)

Technological Advancements

Economic and Competitive Pressures

Regulatory Support

The reality: The AI maturity gap

EXPERIMENTATION

While many organisations are eager to realise the promise of AI, from improved operational efficiency to heightened business resilience, the truth is that most are still in the early to mid-stages of their AI journey.

Rapid, chaotic adoption of multiple proofs of concept with inconsistent strategies and technologies.

The GenAI Scramble2023-2024

The AI Pivot2025-2026

The AI-fuelled Business 2027 and beyond

Established a strong data foundation and governance through a comprehensive platform and infrastructure strategy, and built a tailored AI tech stack.

AI embedded into core processes for smarter operations, accelerated innovation, and enhanced customer engagement.

ADOPTION

ACCELERATION

AI follower

Focuses on cloud computing, consulting and services, relying on external expertise for the right strategy, use cases, and skills.

of organisations are recognised as AI leaders.

What are the differences between AI leaders and AI followers? These span across:

AI leader

Seeks cost savings, better customer experience, and market expansion, but needs a clear roadmap to justify ongoing investment.

Considers individual use cases like automating processes and workflows or code generation.

Sees the need for building strong data science skills, policies, and responsible AI foundations.

Prefers off-the-shelf solutions for simple quick, cost-effective integration into existing systems.

Invests in enhancing in-house developer productivity and performance, and leverages advanced AI/ML platforms.

Seeks more top-line and bottom-line impact such as faster revenue growth, greater efficiency, and profits.

Moves beyond standard use cases and develops strategic portfolios aligned with value creation.

Continues to develop skills as they shift focus to monitoring and maximising the performance of deployed models, and building the AI architecture for scale.

Prefers a mix of building custom AI models (35% plan to do so entirely in-house) and adopting off-the-shelf models.

Tech Investment Priorities

Top Business Outcomes

Types of Use Case

Capability Focus

Deployment Type

AI’s uneven rise across Asia Pacific

South Korea: Cautious optimism in AI investment

While overall growth is accelerating, varying levels of AI adoption and maturity across different countries are influenced by factors such as talent accessibility, regulatory environments, and market environments, shaping how they are navigating their AI ambitions.

Despite being a tech giant, South Korea is hesitant about AI investments due to concerns over ROI and talent shortages. With 30% delaying AI adoption, companies struggle to identify valuable use cases and gauge returns, partly due to an aging workforce impacting talent availability.

Australia: Navigating the skills gap

Australian companies see AI as a growth driver but are hampered by data governance, compliance, and a pronounced skills shortage. While many are at the mid-stage of AI maturity, about one-third are still in the exploratory phase.

China: Unwavering focus and rapid AI expansion

Download the eBook for an in-depth market view across other countries

Harnessing data:

Beyond skills: The data dilemma

Where organisations rush to uncover the promise of AI, one truth stands out: AI success depends not just on sophisticated algorithms or modelling, but on quality data. The difference between AI leaders and followers lies in how effective their data strategy is in managing and extracting the best value from it.

A lack of skilled personnel may top the list of AI stumbling blocks, but data-related issues have risen to become major impediments.

Insufficient Data Governance

Data/IP Loss:

Data as the foundation for AI to thrive

Without a well-structured data strategy, even the most advanced AI systems will fail to perform at their best.

Despite significant investments, half of Asia Pacific companies reported that up to 29% of their AI/ML projects failed.

THE NEXT STEP

With organisations lacking effective management across the data layer, they must dedicate more time and resources to refining their data strategy.

This means investing in robust data governance platforms, eliminating outdated datasets, and adopting high-performance data storage solutions. Choosing the right partners and vendors is also key to ensuring seamless AI implementation.

Scaling the AI maturity ladder

Organisations with grand AI ambitions need more than just that to scale the AI maturity ladder. What are the next steps you should take to go higher?

Business activity plane

Human oversight

Key Aspects:

Bias mitigation

Explainable AI and transparency (XAI Techniques)

(AI privacy and security (30.2%) and governance and policy control (30%) are the most critical aspects to an AI leader)

In AI we trust

AI’s potential to streamline claims management, enhance customer experiences, and drive operational efficiency is immense, but only if it's grounded in transparency and fairness.

In highly regulated sectors like insurance and government, trustworthy AI is non-negotiable given the high stakes of compliance and public accountability.

Ultimately, AI success lies in balancing innovation with responsibility, ensuring that as AI advances, it does so with integrity and accountability. Join the conversation today and explore how your peers are navigating AI challenges and opportunities

Want to know how other sectors fare? Read the eBook

Hint:

From buzz to business impact

43%

AI spending in Asia Pacific is set to reach

Only 18%

Leading the AI charge, China is investing heavily, with a focus on financial growth and market expansion in Southeast Asia. Over a quarter of Chinese companies are AI leaders, leveraging technology to gain a competitive edge.

The cornerstone of AI success

Many organisations lack the development and experience with AI to build a robust data foundation, hindering AI deployment.

Improper use of AI presents risks of data or intellectual property (IP) loss, posing serious reputational risk and compliance concerns.

25%

28%

Lack of specialised skilled personnel

Data foundation lacks sufficient governance processes

Lack of clear evaluation criteria for the AI solutions

Costs associated with AI development and deployment

Concerns about data or IP loss due to improper use of AI

Lack of AI governance and risk management

Organisations’ top 3 challenges in implementing AI technology

35%

28%

27%

26%

25%

29%

Download the eBook for an in-depth market view across other countries

Inability to access data due to infrastructure restrictions

Inability to access data due to business restrictions

Insufficient skills in-house and cost of hiring AI talent

Data sets are constantly changing or rapidly expiring

Large data volumes making meaningful analysis difficult

Insufficient or lack of initial investments in AI platforms and tools

Data governance requirements

Privacy or compliance limitations or concerns

25%

37%

25%

14%

27%

26%

21%

27%

29%

23%

29%

26%

30%

23%

34%

30%

Key factors contributing to AI initiatives failing to meet initial expectations

Leaders

Followers

Data-specific challenges lead to failure of AI initiatives

30%

33%

34%

36%

37%

30%

Data integration platforms

Data warehouses

Data catalogs

Direct database access

Automated data pipeline

Cloud storage solutions

Governance and policy control

Insurance:

Government:

of organisations plan to increase AI spending by over 20% in the next 12 months.

Data synthesis plane

Data control plane

Data plane

• Decisioning • Optimisation • Publication • Cataloguing • Action • Communication

• Analytics • AI training • AI tuning • AI grounding • AI inferencing

• Intelligence • Engineering • Governance

• Distributed • Dynamic • Diverse (structured, semi-structured, unstructured)

Research Insights by

AI followers face challenges like restricted access to quality data, in-house talent shortages, and rapidly changing datasets. In contrast, AI leaders have overcome these roadblocks and now focus on privacy, compliance, and investing in advanced platforms to scale their AI efforts.

How do challenges vary across different AI maturity levels?

Banking:

Healthcare & Life Sciences:

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