AI gold rush? New research reveals major gap between AI leaders and followers in Australia

The IDC Data and AI Pulse: Asia Pacific 2024 study, commissioned by SAS, has revealed Asia Pacific organisations are rushing to jump onto the AI bandwagon, with nearly half (43%) planning a large investment increase in AI, of over 20%, in the next 12 months.

While organisations are investing heavily in AI, only 18% of APAC businesses identify as ‘AI leaders’, highlighting a significant gap between those driving long-term transformational change and the majority who are merely experimenting with various projects without a clear AI strategy. In Australia, just 9% of participants were in the AI leader category: half the rate of the APAC overall average. The majority of Australian businesses identify as being in the mid stages of their AI maturity journey.

“Around one-third of Australian businesses surveyed are just beginning to evaluate AI and consider how best to invest in this space,” said Craig Jennings, Vice President, SAS, Australia and New Zealand. “Australian organisations can see the potential for growth that AI brings, and return expectations are high. However, it is critical these organisations get the foundations right to ensure AI success, and we must seek to bridge the AI skills-gap that is holding back many organisations from reaping true business value.”

Of those surveyed across APAC, AI leaders indicated their top business outcomes from AI initiatives are focused on driving new revenue growth (32%), increasing operational efficiency (31%) and increasing profits (26%). By comparison, ‘AI followers’ indicated improving customer service (27%), expanding market share (25%) and faster time to market (25%) as their top business outcomes.

“The disparity in target outcomes between AI leaders and AI followers demonstrates a lack of clear strategy and roadmap. Where AI followers are focused on short-term, productivity-based results, AI leaders have moved beyond these to more complex functional and industry use cases,” said Shukri Dabaghi, Senior Vice President, Asia Pacific and EMEA Emerging at SAS.

“As businesses look to capitalise on the transformative potential of AI, it’s important for business leaders to learn from the differences between an AI leader and an AI follower. Avoiding a ‘gold rush’ way of thinking ensures long-term transformation is built on trustworthy AI and capabilities in data, processes and skills,” said Dabaghi.

“The IDC Data and AI Pulse: Asia Pacific 2024 study is an important snapshot of how hundreds of large APAC organisations are approaching adoption and implementation of AI, highlighting the leaders and followers across industries,” said Chris Marshall, Vice President, Data, Analytics, AI, Sustainability, and Industry Research at IDC Asia/Pacific. “These insights give us the opportunity to unpack the barriers to successful AI implementation, allowing businesses to make wiser investments into these new and emerging technologies, without being caught-up in the gold rush.”

Generative AI is only one part of the AI journey

While a great deal of AI hype has focused on generative AI, the study reveals that organisations have also been investing in predictive and interpretive AI. In 2023, generative AI accounted for just 19% of total AI investment but by 2025, it is expected to increase to 34% reflecting a more balanced spending distribution across these three AI categories.

The research suggests AI spending in Asia Pacific will reach US$45 billion in 2024, rising to US$110 billion by 2028 at 24% CAGR (2023–2028).¹

The study finds that Australia continues to show significant interest in AI, with 35% of organisations planning to increase their AI investment in 2025.

The research reveals that organisations are reallocating budgets for the 2024 increase in generative AI investment, with a third saying it will come from redistributing funds away from infrastructure modernisation and 37% from application modernisation.

Expectations are high when it comes to ROI

The study reveals this prospective gold rush is fuelled by inflated expectations of AI’s potential return on investment. The research found that 40% of organisations surveyed expect at least a three-fold return on investment, with the ‘fear of missing out’ continuing to spur AI spending. Australian businesses reflect this sentiment on ROI, expecting up to three times their investment. As a result, the research shows AI has at times been adopted without a clear alignment between investments and their outcomes and business value.

Where organisations are struggling to implement AI technologies, top challenges facing Australian businesses are lack of skilled personnel (35%) and data governance processes (30%), while needing to navigate operations in a highly regulated industry was also a major factor (30%). The data specific challenges that lead to AI failure for Australian respondents are led by data engineering complexity (41%) and inability to access data due to infrastructure restrictions (41%).

With 43% of organisations planning to increase their AI investment by 20% or more in the next 12 months, organisations risk being disillusioned with AI because of these tactical investments’ likely returns. Instead, business leaders should realise that building an AI capability takes time and requires solid AI foundations to ensure long-term value add.

“While consumer access to generative AI tools made AI feel magical, integrating it into an enterprise environment takes a lot of work, the right infrastructure, and often the high expectations placed on these tools are unrealistic,” said Dabaghi. “Understanding these pitfalls provides us the opportunity to learn how we tackle these issues, enabling a higher success rate, and meeting business objectives when it comes to adopting and successfully implementing AI.”

The research showed that in Australia, AI performance is increasingly driven by efficient data and model management, alongside growing considerations for regulatory compliance. This balanced approach strongly focuses on using robust data platforms to streamline data management and model oversight, enhance collaboration across teams, improve data management, and provide real-time insights.

Pulse of AI across industries

The study provides a detailed analysis of how AI is impacting different industry sectors in the APAC region, with key focus areas including the banking, insurance, healthcare, and government sectors.

The skills-gap remains a consistent challenge across industries when it comes to successful AI adoption and implementation. This skills-gap is felt the most within the healthcare industry (41%), followed by the government sector (38%), insurance industry (32%), and less so in banking (29%). Despite this challenge, these industries continue to invest in improving their data and AI capabilities to deliver more streamlined decision-making, greater automation, faster time-to-market for new products and services, cost savings, and a host of other benefits.

Nonetheless, some use cases are being consistently and successfully deployed — in banking for instance, with its top three use cases: liquidity risk management, asset and liability management and financial crime analytics. In insurance, the research suggests we are seeing AI use cases for insurance claims fraud, omni-channel delivery of products and intelligent pricing. In health care, notable use cases include healthcare fraud and cost containment, while in government, the popular AI use cases relate to ensuring social benefits programme integrity, supporting emergency response, and tax and revenue compliance.

AI adoption trends vary across countries

The AI landscape in APAC varies by country, with each market showing unique adoption trends. China is leading in AI investments, showing a large increase in AI projects over the next 12 months (59%), with India and Japan following suit (51%; 46% respectively).

While not leading in investments, the study does show Australian spending is increasing, with 28% of businesses citing a large increase (greater than 20%) over the next 12 months, and 34% citing a small increase (4–20% increase). However, the study reveals that Australian businesses need to focus on the strategy behind their investments, with many businesses citing a short-term focus (28%) on AI investment.

The study reveals the top three business outcomes from AI initiatives in Australia are to drive revenue growth, save costs and unlock faster time-to-market.

“In order to realise these top business outcomes from AI investments, Australian businesses need to assess how AI leaders across APAC are building strategic frameworks around their investments,” said Jennings. “These are lessons that are invaluable, particularly in Australia where AI maturity is still developing.”

Furthermore, China and South Korea are advancing more rapidly in AI adoption and integration than other APAC countries. This disparity is driven by factors such as investment levels, regulatory frameworks, and the availability of AI talent and infrastructure. The lack of skilled personnel is a national as well as an industry concern in Australia, Japan and South Korea and many parts of Southeast Asia.

The research highlights the opportunities and the challenges associated with increasing AI investments across APAC in the coming years. It suggests that to unlock AI’s full potential, companies must develop in-house skills, build a strong portfolio of strategic use cases and plan for AI costs and risks from the start. By doing so, they can achieve some of the promised higher returns and foster greater trust in future AI investments.

The full report is available through the eBook: Data and AI Pulse: Asia Pacific 2024.

Methodology

The study, conducted in June 2024, includes 509 executives across eight Asia-Pacific markets (Australia, China, India, Japan, Korea, Malaysia, Singapore, and Thailand) and sampled organisations across banking and finance, manufacturing, government, and health care and life sciences. Leaders were surveyed to examine their AI investment decisions, how they want AI to serve their organisations, challenges to deployment, and their approaches to managing these processes to achieve trusted AI outcomes.

^{1. IDC 2024, Worldwide AI and Generative AI Spending Guide, August 2024}

Image credit: iStock.com/Khanchit Khirisutchalual