AI Adoption Across Enterprises: Challenges and Strategies for Growth

Earlier this year, IBM released a report showing that nearly half of enterprise-scale organizations globally are already leveraging artificial intelligence (AI). This marks a significant milestone for AI adoption, signaling a bright future for its continued integration into business operations.

However, the report also highlighted several barriers to widespread AI adoption. Key obstacles include limited AI skills and expertise (33%), data complexity (25%), ethical concerns (23%), difficulty scaling AI projects (22%), high costs (21%), and a lack of tools for AI model development (21%).

To address these challenges and promote AI adoption, Remus Lim, Senior Vice President of Asia Pacific and Japan at Cloudera, shared his insights with FutureCIO. He provided strategies for overcoming these hurdles and accelerating AI deployment across enterprises in the region.

AI Adoption in APAC: A Fragmented Landscape

Lim described AI adoption in the Asia-Pacific (APAC) region as fragmented, with varying levels of maturity. According to an IDC study, Singapore leads in AI innovation, followed by Indonesia, Australia, Japan, and South Korea.

"Despite differences in infrastructure, organizations across APAC are making significant investments in modern data architectures to support AI initiatives," said Lim. "The region excels in hybrid cloud adoption and machine learning, creating a solid foundation for future AI growth."

Cloudera’s research shows that over half (57%) of organizations in APAC are early adopters of AI technologies like Generative AI (GenAI) and Large Language Models (LLMs). Additionally, 88% of these companies use AI to enhance operational efficiency (21%), drive innovation (17%), and maintain competitiveness (11%). However, many still face challenges due to inadequate data infrastructure and limited AI expertise.

Lim emphasized the importance of investing in advanced tools and skilled professionals to fully harness AI's potential.

Leading Industries in AI Adoption

The professional services and banking sectors have emerged as leaders in AI adoption across APAC. According to Lim, professional services firms are heavily investing in AI infrastructure for complex projects, while banks use AI to enhance customer experiences, offer personalized recommendations, and detect fraud.

"Beyond these industries, businesses throughout APAC are using AI to optimize operations, analyze data, identify trends, and boost efficiency," Lim noted. "AI is being deployed to accelerate revenue growth, interpret complex datasets, and support data-driven decision-making."

In APAC, AI deployment includes generative AI (67%), predictive AI (50%), deep learning (45%), and classification (36%), according to Cloudera’s findings. Lim predicts that future AI strategies will focus on deeper integration into core business functions to drive growth.

Key Drivers of AI Growth

Lim identified operational efficiency and the need for deeper insights as key drivers of AI adoption among APAC enterprises. IT (92%), customer service (52%), and marketing (45%) are expected to lead AI deployment in the coming years.

He also outlined the primary benefits of AI integration, including improved customer experiences (60%), increased operational efficiency (57%), and faster analytics (51%). AI applications are enhancing security and fraud detection (59%), automating customer support (58%), and powering chatbots (55%), among others.

However, the region faces significant challenges related to data management, governance, and protection. "Without good data, there is no AI," Lim stated, emphasizing the need for secure and well-governed data environments. He encouraged organizations to adopt industry standards for integrating LLMs while maintaining robust data security.

Overcoming the Challenges of AI Adoption

Lim identified three major hurdles for AI adoption in APAC: data management, justifying AI investments, and responsible AI usage.

"Businesses must establish a strong data architecture and ensure they have trusted data for GenAI," Lim said. He pointed out that many organizations struggle to demonstrate AI's value, suggesting that they should focus on "high-impact, quick-win use cases that deliver measurable returns."

When it comes to responsible AI usage, Lim stressed the importance of addressing biases and ensuring ethical implementation. "AI systems can lack explainability and emotional intelligence, which can lead to unintended biases and ethical dilemmas," he said. Transparency and ethical guidelines are crucial to fostering accountability.

Accelerating AI Adoption: Focus on Data Management

According to Lim, CIOs and tech leaders must prioritize data management and governance to accelerate AI adoption. "Trusted AI relies on high-quality, secure data," he explained. A strong data infrastructure, combined with stringent governance policies, ensures data is accurate, accessible, and compliant, reducing risks and building trust in AI initiatives.

Lim recommended hybrid data platforms to manage and analyze data across various environments. "This approach allows organizations to transform data into actionable insights, supporting seamless AI deployment," he said.

He also stressed the importance of adopting modern data architectures to support scalable and trusted AI development. "Deploying AI at scale requires businesses to seek use cases that impact multiple functions to maximize ROI."

Avoiding the Pitfalls of AI Investments

One of the most significant pitfalls in AI adoption, according to Lim, is investing in technology that doesn't align with business goals. He advised CIOs and tech leaders to clearly define the "why" behind their AI initiatives.

"Understanding the purpose of AI investments ensures alignment with strategic objectives and delivers measurable value," he said. Lim also highlighted the importance of building a solid data foundation to support AI, focusing on data quality, accessibility, and security.

"Break down data silos to create a single source of truth for AI analysis. Invest in data governance and unified security measures to ensure accuracy and compliance. A strong data foundation minimizes breach risks and supports actionable insights," Lim concluded.

Finally, Lim cautioned against chasing AI trends. "Focus on solving real business challenges. Tailor AI solutions to specific needs and measure their impact on key performance indicators to demonstrate ROI and justify continued investment."