Earlier this year, IBM released a report revealing that almost half of the enterprise-scale organisations worldwide actively use artificial intelligence in their businesses, a promising sign for the future of AI adoption.
However, data also showed several factors hindering this adoption, including limited AI skills and expertise (33%), too much data complexity (25%), ethical concerns (23%), AI projects that are too difficult to integrate and scale (22%), high price (21%), and lack of tools for AI model development (21%).
To equip organisations overcome these challenges and prepare for AI adoption, Remus Lim, senior vice president of Asia Pacific and Japan at Cloudera, shared his insights with FutureCIO on the state of AI adoption in the region and strategies for accelerating AI deployment to support enterprise growth.
AI adoption in APAC
Lim described AI adoption in APAC as "fragmented, with varying levels of maturity across the region." He referenced an IDC study showing that Singapore leads the way in AI, with Indonesia, Australia, Japan, and South Korea following closely as AI innovators.
"Despite differences in infrastructure, organisations throughout APAC are investing significantly in modern data architectures to support AI initiatives. The region excels in hybrid cloud adoption and machine learning, laying a strong foundation for AI growth," he said.
A Cloudera study revealed that more than half (57%) of APAC organisations are early adopters of AI, specifically GenAI and LLMs. Another study showed that most (88%) businesses use AI to enhance operational efficiency (21%), drive innovation (17%), and stay competitive (11%). However, many companies face challenges due to inadequate data infrastructure and skills.
Lim stressed that companies must invest in advanced tools and the necessary expertise to leverage AI's potential fully.
Leading the way
According to Lim, the professional services and banking industries have led to AI adoption in APAC. IDC data indicates that professional services firms heavily invest in AI infrastructure for complex projects. Lim said that banks enhance customer experiences, make personalised recommendations, and detect fraud through AI.
"Beyond these sectors, businesses across APAC leverage AI to optimise operations, analyse data, identify trends, and boost efficiency. For instance, AI is used to accelerate revenue growth, interpret complex datasets, and support informed decision-making," said Lim.
Lim noted that AI deployment in APAC includes generative AI (67%), predictive AI (50%), deep learning (45%), and classification (36%), according to Cloudera's The State of Enterprise AI and Modern Data Architectures.
He added that in the future, AI deployment will focus on integrating the technology into core business functions to drive business growth.
Key growth drivers
"AI adoption among APAC enterprises is driven by the need for deeper insights and operational efficiency," explained Lim, adding that IT (92%), Customer Service (52%), and Marketing (45%) will be the critical departments adopting AI.
He also listed the top benefits of AI integration, such as improved customer experiences (60%), increased operational efficiency (57%), and expedited analytics (51%).
“AI applications include enhancing security and fraud detection (59%), automating customer support (58%), leveraging predictive customer service (57%), and powering chatbots (55%),” he noted.
Lim said that organisations in APAC focus on practical AI applications across various departments. However, the region faces unique challenges around data management, protection, governance, and significant concerns regarding generative AI.
"Our view is that without good data, there is no AI. We are seeing the industry already using Gen AI for mission-critical use cases today – such as chat bots, document summarisation, and code generation,” said Lim.
He said enterprises should adopt industry standards to integrate large language models (LLMs) and ensure secure, governed data environments.
“This approach facilitates the rapid deployment of AI while maintaining robust data security and compliance, enabling effective and responsible AI utilisation across APAC," he added.
Challenges of AI adoption
For Lim, there are three significant challenges of AI adoption in APAC: data management, justifying AI investment, and responsible AI use.
"There need to be an organised framework, architecture, and skills in place, and businesses are proactively working to address some of the foundation layers of their data to ensure that they have trusted data for GenAI," Lim said.
Moreover, he observed that enterprises struggle to justify AI investment and prove AI's value. Organisations should consider focusing on "identifying and showcasing high-impact, quick-win use cases that deliver measurable returns across the entire organisation."
"Developing a clear business case with projected ROI and leveraging pilot projects can help gain stakeholder confidence and secure funding," he said.
Regarding responsible AI use, Lim said that organisations should prioritise data security and use the technology responsibly.
" Addressing AI biases and hallucinations is crucial for ethical implementation. AI systems often lack explainability and emotional intelligence, leading to unintended biases and ethical dilemmas,” he said.
Lim believes in the importance of transparency and ethical guidelines in AI use to foster accountability and mitigate risks.
“Human oversight is vital for managing exceptional circumstances and balancing ethical considerations with performance objectives," he said.
Accelerating adoption
"To accelerate AI adoption, CIOs and tech leaders must prioritise data management and governance. Trusted AI relies on high-quality, secure data, so establishing a solid data infrastructure and stringent governance policies is crucial. This ensures that data is accurate, accessible, and compliant, reducing risks and building trust in AI initiatives," Lim said.
He added that it is vital to implement hybrid data platforms that can manage and analyse data across different environments. This approach "ensures that all of an organisation's valuable data is effectively transformed into actionable insights, regardless of its source, and supports seamless AI deployment," he explained.
Lim underscored the need to adopt modern data architectures for comprehensive data processing and analysis, which support scalable and trusted AI development.
"Deploying AI at scale requires business decision makers to seek out use cases that impact multiple functions across the organisation to maximise investment returns. By focusing on data excellence with the right infrastructure, CIOs and tech leaders can streamline AI adoption and position their organisations for success," he added.
Avoiding the trap
The big trap about AI adoption and spending is investing in technology unsuited to the business or failing to meet business goals. For Lim, CIOs and technology leader should always define their "whys" and articulate the business problem they want AI to solve.
Lim explained that understanding the why behind an organisation's AI investments aligns them with strategic goals and can deliver measurable value. It also helps build a strong data foundation to support AI initiatives, addressing data quality, accessibility, and security.
"Prioritise data unification and governance: Break down data silos to create a single source of truth for AI analysis. Invest in data governance and consistent security measures to ensure data accuracy, end-to-end unified security, and compliance. A strong data foundation is essential for deriving actionable insights. This also ensures that breach risks are minimised through consolidated security measures and single-pane-of-glass management across cloud and on-premises data,” Lim added.
Finally, he advises technology leaders to focus on something other than technology trends but on business outcomes.
"Avoid chasing the latest AI fad and keep an eye on solving real business challenges. Identify specific business challenges and tailor AI solutions accordingly. Measure AI's impact on key performance indicators to demonstrate ROI and justify continued investment," he said.
