Artificial Intelligence Adoption and Investment Trends in APAC

APAC Tech Data Story Report

Data, AI and Analytics sector has a critical role to play in driving digital transformation through the shift toward intelligence-driven decision-making. According to 451 Research, a technology research offering of S&P Global Market Intelligence, data science, machine learning (ML), self-service analytics, DataOps, data governance, cloud databases, data warehouses, data lakes and data marketplaces each have a part to play in making pervasive intelligence a reality and enabling the development of agile real-time applications and data-driven decision-making that will drive the next wave of digital transformation.

In this report, we cover some prominent issues (themes) in the sector and analyze adoption and investment of related technologies in Asia.

1. Data-Driven Decision Making

Data, AI and analytics are foundational building blocks and drivers of dynamic automation in economies around the world. As businesses experience uncertainties in 2023 and with recessions looming or already underway, getting an accurate picture of the state of a business is more important than ever. As belts tighten in many sectors, organizations need to know precisely how they are faring and what might be around the corner. This has made data rise to the fore as the means to make more impactful decisions.

This is evident in the organizational structure of businesses according to a 451 Research survey. Well over a third (39%) of respondents employ a chief data officer, and another 18% are planning to within 12 months (see Figure 1 below). With the rise in data has come an attendant evolution in analysis tools used to explore, understand, and communicate data insights.^[1]

Figure 1: Chief Data Officer Adoption Rates and Reporting Structures

Decision intelligence is the next generation of business intelligence, incorporating analysis and data management capabilities built on a foundation of machine learning that is designed to improve data-driven decision-making. More traditional techniques involving reports and dashboards will continue to thrive due to their familiarity. Enterprises will also continue to look for ways to arm business decision-makers with self-service analytics and remove some of the “heavy lifting” for expert data scientists and analysts, which will shape data science platform functionality. As a result, multi-persona data science platforms with capabilities that can be customized based on users’ roles and goals will become commonplace.

2. Pursuit of Data Quality, Consistency and Reliability

In seeking to establish enterprise-wide data culture, organizations must consistently ensure the availability of high-integrity data. After an era of “big data” fervor, the pendulum is swinging back toward a focus on data reliability and consistency. The tools, techniques and roles organizations use to achieve this can vary, but the core business objective remains consistent: to drive accurate and actionable insight. As IT architectures diversify — we’re in a hybrid and multicloud world, and interest in edge devices is growing quickly — organizations have more enterprise data sources than ever, while consumer data collection is shifting to first-party sources, all of which affects data quality.

Research analysts covering the data, AI, and analytics at 451 Research expect to see more basic data control efforts such as data quality programs “shift left” to catch potential problems early, before they manifest. Organizations are increasingly supplementing their human roles and processes to support the data “supply chain” of reliable and high-integrity data. And finally, worker productivity with data will become a key determinant of success, as automation and guided/assisted technology help users navigate data relevance.

3. Asian Markets Differ in Data Tech Adoption Rates^[2]

451 Research’s APAC Tech Adoption Index report in H2 2022, surveyed IT decision makers from all industry sectors in 6 Asian countries to gather insights about adoption and investment trends related to data, AI and analytics type technologies. The survey covered India, Indonesia, Malaysia, Thailand, the Philippines, and Vietnam.

Southeast Asian markets appear to have substantially different rates of data technology adoption. Respondents from Philippines-headquartered organizations more frequently report that data, AI and analytics technologies are in broad deployment, with particular maturity in data governance, data warehouses and data quality. It is perhaps this strength in storage and cleansing that has laid the foundation for Philippines headquartered respondents’ higher reported rates of machine learning and data science technology adoption.

Vietnam and India appear to be less mature markets, at least in terms of some technology areas (see Figure 2). Respondents headquartered in both countries had comparatively immature data integration and data marketplace adoption. Vietnam appears to be lagging in machine learning and master data management adoption. Parallels can also be drawn between Indonesia and the less mature Malaysian market. Both have comparatively lower rates of data warehouse/data lake and business intelligence/analytics adoption relative to their standing in other areas.

Figure 2: Regional heatmap of broad-deployment rates for data, AI and analytics technology

Source: Asia Pacific Technology Adoption Index 2H 2022

When including respondents who say data technologies are in the process of being deployed or are planned for implementation in the next 12 months, regional differences decline markedly. The appetite for data technologies across all assessed regions is substantial and holistic, encompassing all technology areas.

With machine learning being the foundation of decision intelligence software to improve data-driven decision making, the survey response indicates that access to machine learning capabilities appears to be increasing across South-East Asia. Broad deployment is more common in companies headquartered in the Philippines, Thailand, and Indonesia (see Figure 3 below). However, a significant proportion of companies in apparently less mature markets such as India and Vietnam appear to be in the process of deploying or scaling up limited implementations.

Data further suggests in South-East Asia markets, machine learning is more frequently in broad deployment in the largest organizations, those with more than 5,000 employees. More than two in five respondents (42%) from these largest companies say machine learning is in broad deployment, versus 32% of respondents at companies with fewer than 5,000 employees.

Figure 3: Current Adoption State of Machine Learning by Country

Source: Asia Pacific Technology Adoption Index 2H 2022

With reference to technologies in Figure 4, we see strong levels of increased investment across every technology area assessed. This includes technologies often denoted as “emerging,” such as machine learning and blockchain, but also technologies that Figure 2 illustrates are already widely adopted in the region. Data quality and analytics appear to be areas of particularly fast growth, with 51% and 47% of respondents, respectively, expecting to increase spending on these capabilities by over a fifth in 2023. Many of the surveyed regions are key markets for process outsourcing, so the drive toward greater efficiencies, commonly driven by data, may be at the center of such ambitious digital roadmaps.

Figure 4: Spending by Technology (2023 vs. 2022) – Data, AI and Analytics

Source: Asia Pacific Technology Adoption Index 2H 2022