AI Adoption and Data Governance Challenges in Hong Kong: What Does Our 2025 Survey Reveal?

This survey means to assess if and how companies are utilizing artificial intelligence, and which challenges they face in their pursuit of AI excellence. With over 350 participants this year, from some of the biggest companies in APAC, we gathered some fascinating insights: 

The Cornerstone of Successful Digital Transformation: Data Governance and Architecture

To achieve digital transformation to its full potential, organizations across all industries must first implement a robust data governance framework and architecture, particularly when it comes to collection and reconciliation processes. Data warehouses, Customer Data Platforms (CDPs), and proper measurement frameworks represent some of the initiatives that businesses can rely on to make sense of their data, understand their customers better, and evaluate marketing performance. 

Our 2025 survey revealed a notable shift in organizational readiness compared to last year. While in 2024 many organizations were still in planning stages, this year shows more concrete implementation progress. Approximately one-third of respondents have now implemented robust frameworks for data collection and reconciliation, marking a modest but encouraging increase from last year. This increase is a positive sign that companies in Hong Kong have been investing in building solid foundations for the long term despite the current economic slowdown. 

Question: Has your team or company implemented a robust governance and architecture for data collection and reconciliation?  (e.g. measurement framework, data warehouse, CDP)

How are companies in Hong Kong Deploying AI in 2025?

The deployment of AI use cases reveals an interesting pattern in our community.  Our 2025 survey reveals that content creation has emerged as one of the leading applications (26%), showing notable growth from 2024. This increase likely reflects the maturation of generative AI technologies and their increasing accessibility. Internal use cases, such as automated performance insights are now the most frequent type (around 27%), surpassing chatbots and customer service applications (approximately 24%).

Customer segmentation or scoring use cases have seen a slight decrease in adoption rate compared to 2024, dropping from about 18% to 15%, while Marketing Mix Modelling remains the one with the lowest adoption rate at around 8%. 

But our survey also shows that most companies in Hong Kong are still in the early stage of their AI journey, with one third of respondents still indicating that their company hasn't deployed any AI-powered use case yet (a slight decrease compared to 2024). Those that have implemented AI solutions are choosing to focus primarily on productivity, and cost-saving use cases.

Question: Which AI use cases has your team or company successfully deployed?

Current Challenges in AI Adoption

Data quality and availability challenges are still top of the list for our respondents (58%, up from 54%) – unsurprisingly so, as no AI solution can function properly without a solid data foundation. This growing concern emphasizes the critical role of reliable, accessible data as the foundation for AI initiatives.  Cost and ROI considerations have gained prominence, with 35% of respondents highlighting this challenge compared to 30% in 2024. Meanwhile, data privacy and security concerns, including legal constraints, have risen from 25% to 29%. Interestingly, challenges related to resources and skills have decreased from 30% to 25%, suggesting organizations are making progress in building internal capabilities and/or collaborating with specialized partners

‍Question: What are the main challenges your team/company is facing that are slowing down AI adoption at scale?

What comes next for AI in HK?

Lastly, our respondents were asked about their AI priorities for the next 12 months. Most of them are planning to deploy additional use cases while strengthening their data infrastructure to support them, and build internal AI capabilities. Specifically, expanding AI use cases emerged as the leading priority with 43% of respondents planning to broaden their AI applications, while 35% are focused on strengthening their data infrastructure - acknowledging the critical foundation needed for successful AI implementations. Building internal AI capabilities ranks as the third most important priority at 27%, reflecting organizations' commitment to developing in-house expertise. The survey also revealed more targeted priorities, with 15% focusing on improving model performance, 12% on establishing AI governance frameworks, and 10% working on scaling successful pilots. This distribution of priorities suggests a mature approach to AI adoption, balancing ambitious innovation roadmaps with the necessary groundwork in infrastructure, capabilities, and governance.

Question: What are your AI priorities for the next 12 months?

‍

At fifty-five, we’ve been helping our clients harness the full power of their data to power transformative use cases, in Hong Kong and across the globe. Our teams have put together a checklist addressing top enterprise concerns when implementing generative AI in creative work, and how to mitigate them. From governance to data security and scalability, this short guide will help you assess your GenAI readiness and ensure true adoption in the long run. 

‍

To learn more about the three key steps for successful GenAI implementation (identifying use cases, launching pilot projects, and scaling up), discover our dedicated e-book; “GenAI: Turning promises into reality.”