Generative AI in Asia Pacific: Young employees lead as employers play catch-up

1. Students and employees are leading the gen AI revolution across Asia Pacific, but only half believe their manager knows they are using it.

Across the region, students and employees are using gen AI at astonishingly high rates. Our survey revealed that 81% of university students and 62% of employees are using the technology. In fact, 43% of employees we surveyed are using gen AI for work purposes.

Young people who have grown up with the technology—generation AI—are more likely to be already experimenting with and using the technology. Employees who are 18 to 24 years old are nearly twice as likely to be using gen AI compared with older workers. This suggests that age and early exposure to digital technologies are key drivers of high generative AI use.

Since the release of our research report, Generation AI: Ready or not, here we come!, in 2023, use of gen AI at Australian workplaces has increased from 32% to 38% of all employees.¹ This 20% increase in less than a year represents a significant jump in usage on top of already fast adoption.

Gen AI’s importance is set to grow rapidly. A key driving factor behind the adoption has been the accessibility of the technology. Generative AI is not effectively controlled by IT departments or coordinated strategies. Much of the adoption to date has been driven by an open-source community that has rapidly progressed the accuracy and applications of large language models. The open-source community of gen AI users has been supporting rapid growth by inspiring newcomers to experiment with these tools. Open-source platforms such as LangChain and Hugging Face provide opportunities for generation AI to share knowledge and troubleshoot quickly. In addition, growing populations for some locations across Asia Pacific will mean that the region can expect rapid growth. In fact, the percentage of gen AI users who expect to use the technology daily is set to triple within the next five years, from 11% today to 32% (figure 2).

Despite the growing number of gen AI users, businesses across the region are struggling to keep up. In fact, half of employees using gen AI for work purposes do not believe management knows they’re using it. While businesses are looking to introduce safe and secure applications for their employees developed by technology vendors or bespoke platforms, they’re having to play catch-up with their own employees.

2. Developing economies are leading the charge with gen AI adoption, with take-up rates 30% higher than developed economies

For previous technology revolutions, developed economies have been the early adopters. For example, nearly half of the Korean, New Zealand, and Australian population had access to the internet in 2000 compared to less than 2% in China, India, and the Philippines.² Another example is cloud adoption, which started gaining traction in developed economies in the mid-2000s with many developing economies catching up a decade later. This pattern has been reversed in gen AI and we have seen the employees and students in developing economies adopt the technology faster than developed economies to this point.

Developing economies (China, India, and Southeast Asia) have a 30% higher share of gen AI users compared to developed economies (Japan, Taiwan [China], Singapore, South Korea, Australia, and New Zealand).³ In fact, Indian students and employees are 30% more likely to have used gen AI compared to their peers across Asia Pacific (figure 3). Daily usage is also higher in markets like India (32% of those surveyed) and Southeast Asia (19%), compared to Australia (8%) and Japan (4%). 

This adoption gap partially reflects that these locations have more “digitally native” people as a percentage of their total populations. In fact, nearly half (46%) of those surveyed in India were between 18 and 35 years old, compared to 30% of those people surveyed in Japan.

Employees from developing economies are also more enthusiastically embracing gen AI. Over half (53%) of employees and students in developing economies felt primarily excited about gen AI technology, compared to less than a quarter (23%) of students and employees in developed economies. In comparison, over a third (36%) of employees in developed economies felt primarily uncertain about gen AI, compared to only 12% in developing economies.

Most importantly, generation AI in developing economies are more likely to be proactively taking actions in response to the rapid emergence of gen AI. Actions taken include researching the basics of gen AI, advancing programming skills, collaborating with others about the technology, and undertaking formal study. In China, 71% of students and employees have taken at least one action, compared to an average of 49% across Asia Pacific and 31% in Australia (figure 4).

The concentration of generation AI in developing economies suggests there could be significant disruption in the traditional technology hierarchy across Asia Pacific as developing economies more eagerly embrace and upskill in this new technology. The finding that developing economies are ahead on leveraging gen AI highlights that this new technology is not about labor costs savings but productivity improvement and making the most of human potential.

Developed economies need to urgently address the gen AI usage gap. Lower rates of usage compared with developing economies combined with a higher proportion of their workforce in professional and managerial roles means developed economies can expect to face significant short-term disruption in these industries.⁴

Of course, there are other factors beyond usage and upskilling that will shape the impact of gen AI on an economy. These include digital infrastructure, a regulatory environment, and a skilled workforce. Based primarily on these measures, Salesforce’s Asia Pacific AI Readiness Index ranked Singapore (70.1 out of 100) and Japan (59.8) as leaders in business and government AI readiness across Asia Pacific,⁵ followed by China (59.7), South Korea (59.2), Australia (58.2), New Zealand (54.6), India (49.8) and Southeast Asia (with an average of 40.5). AI readiness is an area that organizations in developing economies may need to focus on as their use of technology continues to grow.

3. Over 11 billion work hours per week across Asia Pacific are expected to be impacted by gen AI

While developing economies are leading the charge in gen AI adoption, every market and industry across Asia Pacific will be impacted by this technology. In an Asia Pacific first, we estimate that gen AI could impact 16% of working hours—more than 11 billion hours—across Asia Pacific per week. This impact could be in the form of gen AI automating some tasks, so they no longer require worker involvement or augmented tasks in a way that requires workers to use AI to complete the task. For some workers, this may mean significant change and retraining in their everyday work while for others gen AI will require more modest upskilling or simply operate in the background of the work they do.

Even this analysis may underestimate the true impact of AI on the tasks that workers currently complete. While this analysis uses academic research on which jobs are expected to be impacted by the introduction of AI based on tasks currently required for the role, new uses of AI are certain to emerge which will create new tasks, shift the need for the type of workers economies need, and create roles that have not yet been imagined.

When employees were asked about how gen AI would impact their tasks, employees expected 61% of their current tasks would be impacted by the technology in the next five years.

Gen AI, like previous waves of disruptive technology, will free up workers for new tasks and create new types of jobs that were previously not imagined. While overall jobs are likely to continue to grow, workers in some occupations and industries are going to face significant change during the transition and may require support through it. For example, the advent of the spreadsheet in the 1980s saw the demand for bookkeepers significantly fall to be replaced by more highly skilled accountants undertaking their work. Understanding which industries and roles will be most impacted is essential for navigating the imminent change.

To further unpack the impact of gen AI on the Asia-Pacific region, Deloitte Access Economics mapped 18 industries in terms of the extent of gen AI’s impact—what we call the “bang”—and how soon those industries will be affected—the length of the “fuse” (figure 5). Timing was determined by examining which industries in our survey showed signs of early adoption, while impact was estimated by the number of occupational hours within those industries that are exposed to 10 possible applications of AI. The appendix contains further details of this methodology.

4. Using gen AI saves each user almost a day per week and frees up time to learn new skills

Those who use gen AI are already experiencing improvements in their work and study performance. According to our gen AI survey, 80% of gen AI users said that it’s improved the speed at which they can complete tasks. This has led to large time savings for completing tasks.

On average, daily users of gen AI save approximately 6.3 hours per week.

These productivity improvements have been supported by other studies. One study found that 85% of respondents in four surveyed locations in Asia Pacific (Australia, India, Japan, and Singapore) believe AI will allow them to do their job faster or more efficiently.⁷ Another study found around 80% of surveyed AI users in the manufacturing and financial sectors said that AI had improved their performance at work.⁸

So, what is generation AI doing with these time savings? The time saved by using gen AI allows employees to complete other tasks or build new skills. Fifty-four percent of employees said they used the time savings to complete other tasks while 45% invested time in additional learning or skill development.

Gen AI has also improved our ability to learn new information. According to our gen AI survey:

• 71% of gen AI users said that it’s improved their ability to generate new ideas.
• 67% of users said that it’s improved their ability to learn new skills.
• 73% of users said gen AI improved the quality of their outputs.
• 65% of users said gen AI improved the accuracy of their outputs.

From employees who have improved skill-building with gen AI, nearly 40% believe using it halves the time it takes to become proficient in a skill.

5. Gen AI increases the level of satisfaction at work and study

The current work environment is unlike anything we’ve previously experienced. Following the rapid reshaping of the way we undertook work during the COVID-19 pandemic, employees are now facing increasing cost pressures along with a weakened economic outlook for many locations across Asia Pacific.

These pressures can lead to feelings of burnout among employees. In fact, an Asia-Pacific study showed that almost 50% of workers end the workday feeling mentally or physically exhausted.⁹ The estimated cost of mental-health–related absenteeism for businesses in Australia alone is around US$13.6 billion per annum.¹⁰

The impressive time savings associated with gen AI can help employees more efficiently complete routine or repetitive tasks to focus on more value-adding tasks that require more critical and creative thinking. This can create more sustainable workloads and reduce employee burnout. According to our survey, 41% of time savers believe this time has improved their work/life balance. This finding is supported by other studies, including one that found that across all indicators of working conditions considered (job satisfaction, physical health, mental health, and fairness in management), gen AI users in the manufacturing and financial sectors were more than four times as likely to say that generative AI had improved working conditions.¹¹

More time spent on value-adding tasks such as creative thinking can also make work more enjoyable. In fact, most gen AI users believe it’s improved the nature of their work or study (81%) and the satisfaction they feel (67%) as represented in figure 6. One study has described this impact as a dual advantage, as the productivity benefits of technology can lead to greater employee engagement.¹²

Another use case is for coaching services. Gen AI can assist with personalized coaching and customized communication. According to our survey, 74% of gen AI users agree that because of the technology, they’re better equipped to support team members and peers, for example, with coaching and mentoring.

Generation AI has rapidly adopted new gen AI–enabled tools, while business leaders play catch-up. Across Asia Pacific, businesses are experimenting and deploying gen AI in its multiple forms. Data from the International Data Corporation shows that investments in AI are expected to increase from US$25 billion in 2022, when ChatGPT first came onto the scene, to US$117 billion by 2030.

That’s nearly a five-fold increase in eight years and puts AI on track to have one of the fastest deployment rates of any new enterprise technology. Some of the fastest growth in gen AI investment is expected to occur in India, Indonesia, and the Philippines, where gen AI usage is higher (figure 3).

This investment is leading to greater rates of adoption. One study found that nearly half of medium-sized businesses in the region are either exploring potential use cases or using gen AI technologies.¹³ Some businesses are providing access to certain AI applications that can be used in a secure and confidential manner provided by technology vendors or developed in-house.

Despite the growing investment and adoption, many businesses are falling behind. Employees were asked to categorize their place of employment in terms of maturity of gen AI use (by selecting one of the following: laggard, late majority, early majority, early adopter, or innovator). Only 29% of employees consider their business to be early adopters or innovators, suggesting substantial room for improvement for three quarters of businesses across Asia Pacific (figure 7). Compare this to another survey that found 44% of senior business leaders believe their organization has high or very high levels of expertise with gen AI.¹⁴

Employees are also cautious about the potential for improvement within their own business. The share of businesses expected to be considered innovators or early adopters is only expected to increase to 38% in the next five years (figure 7).

In the age of gen AI, employees will continue to play a vital role in driving business success. Given that gen AI use increases employee satisfaction, businesses risk losing employees to innovative competitors when they fail to engage their employees on gen AI.

Where to from here?

Despite high use of gen AI, employees have identified some key risks associated with using the technology for work purposes. Specifically, 85% are concerned that using gen AI will lead to a misuse of personal, confidential, or sensitive information (figure 8). A similar share of employees is concerned about the potential for the creation of malicious content (83%), as well as legal risk and copyright infringement (81%).

Business leaders should address these risks and empower employees to fully utilize gen AI applications to realize the significant benefits from this powerful tool. On the other hand, there are key risks to not using gen AI. Over the longer term, businesses that do not explore its use in their business or industry risk being left behind by competitors.

Businesses need to do more to embrace gen AI

While 43% of employees are using gen AI for work purposes, 29% of all employees are not aware of any measures from the business that respond to its rapid emergence in the workplace. This may reflect either a lack of action or a breakdown in communication between senior leaders and employees about the response to this important development.

Some businesses have taken a knee-jerk reaction to the emergence of gen AI, with 22% of employees across Asia Pacific working in a business that bans or restricts the use of gen AI. However, banning or restricting the use of gen AI has been shown to be ineffective. In fact, employees are more likely to use gen AI if they work for a business that has a gen AI ban. Seventy-six percent of employees at a workplace that bans or restricts gen AI have used it compared to 62% of employees overall.

Many other businesses are looking to encourage gen AI use amongst employees. The most popular actions taken by businesses to address the emergence of gen AI are speaking to employees (42% of employees are aware of their business taking this action), encouraging on-the-job learning (39%), and discussing limitations of the technology (35%). Only 33% of employees have received formal training on gen AI. From those that received formal training, 35% reported that they are not satisfied with the training. Training related to gen AI use has been found to increase worker productivity and improve working conditions for employees.¹⁵

Three ‘no-regret’ moves

The rise of gen AI means that business leaders and employees both need to think strategically and act proactively to respond to the rapidly changing environment. Waiting to be told how to use gen AI or how it could be used within the business could mean employees and businesses miss important opportunities. So what are the critical steps business leaders can take now? Based on the analysis of our findings, three moves with high-impact potential stood out:

1. Develop and implement a gen AI strategy that focuses on your businesses’ core value areas.
2. Empower your employees to own their AI journey.
3. Develop your data infrastructure and data governance iteratively as needed to embrace generative AI.

 1. Develop and implement a gen AI strategy that focuses on your businesses’ core value areas

To realize the potential of gen AI, business leaders must avoid confusing use with value.

There is also a growing multitude of possible use cases with some applicable across all businesses and others that are industry or business specific.

Amongst this noisy environment, it’s important for business leaders to remember the vision within their own business strategy and develop an AI strategy that supports that vision. Start with the areas that differentiate the business for customers and from competitors and explore how AI can help boost the competitive advantage your business may hold. On the other hand, identify the pain points that prevent the ambitions of the business from being realized, and point AI at these areas to help find new solutions.

Businesses that look to tailor their gen AI use to their strategy are more likely to require bespoke AI products rather than off-the-shelf solutions. The quarter one edition of the State of Generative AI in the Enterprise report found businesses were much more likely to invest in standard, off-the-shelf solutions (68% using standard applications). Far fewer businesses were using gen AI with industry-specific software applications.¹⁶

Central to the AI strategy is your employees. Yet nearly three in 10 (29%) employees are not aware of a single action taken by their business to address the emergence of generative AI, including training on its use, speaking with employees, or providing guidelines.

This lack of action is more prevalent in more developed economies. Only 29% of Australian employees and 24% of Japanese employees have heard from their employers about generative AI, compared to 64% of employees in India and 57% in Southeast Asia.

A well-developed AI strategy should identify the skills needed to effectively use gen AI and outline how the business is looking to upskill or hire additional talent to meet these needs. In fact, the most effective action a business can undertake according to employees is to provide training on how to use gen AI at work (41%), followed by developing a strategy on how to integrate gen AI into work processes (41%).

Much of the efficiency gains from technological advances require more than just adoption to achieve, but for work to be restructured to enable the use of new technologies.¹⁷ CEOs and senior leaders should consider how the new technologies can enable them to completely rethink processes and business models rather than just employ them to make current processes and tasks more efficient.

There’s a substantial dividend from implementing initiatives around gen AI. Employees who are more likely to be aware of business initiatives related to it are six times more likely to classify their business as a gen AI innovator. This is supported by another study which found that workers using AI where the business had provided or funded training were significantly more likely to report positive outcomes of AI on their working conditions.¹⁸

2. Empower your employees to own their AI journey

Employees are taking center stage when it comes to AI adoption. Generation AI has outpaced businesses in making use of this technology at work. Yet business leaders need to empower workers to build the knowledge and capabilities to use this tool effectively in a work context.

Just because an individual employee is more likely to use or is more frequently using gen AI applications does not mean they’re making full use of the technology. Only 50% of employees believe they are fully leveraging the potential of gen AI. Taking proactive action to increase understanding can help. Employees who took more action—such as researching AI, experimenting with applications, and collaborating with peers—are 51% more likely to feel they’re fully utilizing gen AI at work.

The quickly changing nature of gen AI applications means that businesses that try to develop comprehensive training material or courses for their employees will find their material is quickly outdated. Shorter training sessions with practical examples, and even real-world case studies, can be more effective to intrigue employees to learn more.

Leveraging expertise from trusted partners and vendors will help ensure employees receive the latest information. These partners will have the best understanding of what can be done with their products and have a view on lessons around best practices from their client base.

One way to empower employees is to encourage greater collaboration between users within the business. In some cases, competitions or showcases can be used to highlight relevant examples amongst employees and for the business to garner new ideas that otherwise may not have been implemented.

3. Develop your data infrastructure and data governance iteratively as needed to embrace generative AI

Once a business has developed the AI strategy along with the associated use cases to realize value, they’ll need to ensure that their data infrastructure and governance setting enable their desired use cases.

The rapid adoption of gen AI has relied on open-sourced platforms and communities of support. However, more advanced use cases that offer the greatest return for businesses will require AI models to be trained on internal business data.

Traditional forms of AI highlighted the need to improve the quality of structured data including spreadsheets and customer relationship management databases. Gen AI has proven extremely valuable in semistructured or unstructured data such as text documents, emails, video footage, and audio recordings. One example of semistructured data could involve pulling together a proposal to win new work for an engineering company based on previous proposal themes, experience, and methodology.

Data quality is the key enabling factor to leveraging gen AI models trained on business information. However, one Deloitte Access Economics study looking at data maturity levels of businesses across Asia Pacific found that half of businesses have basic or beginner levels of data maturity. This suggests substantial room for improving data capabilities across the region.¹⁹

But where should businesses begin when it comes to improving data quality? The gen AI strategy should guide where improvements in data quality offer the largest return or value. Data sets that can be used for multiple purposes, such as employee data accessed by employees themselves as well as human resource reporting and resourcing purposes, should be prioritized when it comes to data cleaning.

Successful gen AI implementation depends on a robust data governance, and working with large language models for business use cases requires the implementation of quality and privacy considerations to drive responsible AI. According to a survey of business and technology leaders conducted for Deloitte’s State of Generative AI in the Enterprise report, the biggest concerns related to governance included: intellectual property issues (35%) and misuse of client or customer data (34%).²⁰ Having an established governance framework for managing risks, ethical issues, and other outcomes to address these governance concerns is a necessary prerequisite for any gen AI use case.

Embracing the gen AI revolution

As the influence of generation AI continues to reshape the Asia-Pacific region, it’s crucial for employers to adapt and embrace the rapid advancements in AI technology. By doing so, organizations can not only keep pace with the evolving workforce, but also capitalize on the immense potential that gen AI presents for economic and societal transformation. The future of work in the region depends on harnessing the power of gen AI in a way that fosters collaboration and innovation among employees and employers alike.

Appendix: Methodology

Deloitte gen AI survey

In February and March 2024, we surveyed 2,903 university students and 9,042 employees across thirteen locations: Australia, China, India, Japan, Singapore, Taiwan (China), South Korea, New Zealand, and Southeast Asia (Indonesia, Malaysia, Philippines, Thailand, and Vietnam).

The survey expanded on the previous inaugural survey conducted within Australia, with survey questions designed to understand current use, benefits, barriers, and attitudes toward gen AI.

Employees across all industries and students across all fields of study were surveyed to get a holistic view of the impact of gen AI.

Deloitte Access Economics developed this report in collaboration with the Deloitte AI Institute and Deloitte Insights to understand the use of gen AI by employees and students, current actions taken by individuals, businesses and educational providers, the barriers to gen AI adoption and expectations for the future across thirteen locations in the Asia-Pacific region.

This report is informed by a survey fielded by Dynata. Survey respondents were either students or employees at various levels across a range of industries.

‘Short fuse/big bang’ methodology

For this research, we have mapped each of the 18 industries in terms of the extent of impact—what we call the “bang”—and how soon those industries will be affected – the length of the “fuse”.

The impact score is based on occupational exposure scores produced by Felton, Raj, and Seamans in Occupational Heterogeneity in Exposure to Generative AI.²¹ The exposure scores assess the relative exposure of occupations to 10 applications of gen AI including language modelling and image generation. Scores have been mapped and aggregated from granular O*NET standard occupational classification to two-digit, International Standard Classification of Occupations codes using correspondences sourced from the International Labour Organisation. The way in which gen AI could impact these exposed occupations will differ in each case. For example, some occupations may have tasks that become automatically completed by gen AI tools (for example, chatbots providing support may reduce the need for some call centre operators) while other tasks will be altered by gen AI but still completed with human efforts (construction designs generated based on specified parameters that are reviewed and adjusted by engineers).

The impact score is then calculated based on the occupational composition of each pair-wise country-industry weighted by average hours worked from the International Labour Organisation ISCO database. Data availability across locations varied with several only having one-digit, International Standard Classification of Occupations employment available. Where this occurred, the same process was undertaken at the best level of data granularity available.

The timing of impact is based on the share of surveyed employees who have used generative AI and would consider the company they work for an innovator or an early adopter of gen AI within their industry. Note that these scores are survey estimates based on responses from all 14 locations and sample sizes do vary between locations and industries, and thus the results may be subject to bias toward locations with a smaller share of the Asia Pacific’s population but with a higher share of survey responses.

The four industries in the “short fuse, big bang” scenario are finance; information and communications technology (ICT) and media; professional services, and education. To illustrate the magnitude of impact from gen AI, the economic activity generated by these industries across the 13 locations was estimated at over US$5 trillion. The estimated economic activity generated was produced by taking the share of Gross Value Added (GVA) within these industries by country and applying this share to the Gross Domestic Product (GDP) of each respective country. GVA estimates the value of production in those industries. Where GVA by industry data was limited in certain locations, the share of GVA was conservatively imputed as the minimum share across the other Asia Pacific countries. GVA data was sourced from the Asian Development Bank Key Indicator Database while GDP data was sourced from the World Bank. 

Another computation to demonstrate the scale of impact from gen AI was estimating the number of working hours impacted by gen AI per week, which was estimated at over 11 billion hours across the 13 locations. This estimate was derived by the following steps:

1. Identifying the tasks that could potentially be affected by AI from the Jobs and Skills list included in the Australian Skills Classification database
2. To produce occupation specific share of time affected by AI, we aggregated the time spent on affected tasks per week within each of the Australian and New Zealand Standard Classification of Occupations included in the dataset
3. Mapping the shares to International Standard Classification of Occupations codes using correspondences sourced from the International Labour Organisation
4. Applying the shares to hours worked by occupation data for each of the 13 locations sourced from the International Labour Organisation to produce the total number of hours affected per week 

Using the inputs described above, the computation for number of working hours impacted by gen AI per week was as follows:

It is the sum of each occupation’s work time by gen AI multiplied by the people employed in that occupation multiplied by the average hours worked.

As mentioned above, the exact nature of these impacts will differ across occupations. As such, this estimate of working time impacted is not equivalent to time saved from gen AI.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities (collectively, the “Deloitte organization”). DTTL (also referred to as “Deloitte Global”) and each of its member firms and related entities are legally separate and independent entities, which cannot obligate or bind each other in respect of third parties. DTTL and each DTTL member firm and related entity is liable only for its own acts and omissions, and not those of each other. DTTL does not provide services to clients. Please see www.deloitte.com/about to learn more.

Deloitte Asia Pacific Limited is a company limited by guarantee and a member firm of DTTL. Members of Deloitte Asia Pacific Limited and their related entities, each of which is a separate and independent legal entity, provide services from more than 100 cities across the region, including Auckland, Bangkok, Beijing, Bengaluru, Hanoi, Hong Kong, Jakarta, Kuala Lumpur, Manila, Melbourne, Mumbai, New Delhi, Osaka, Seoul, Shanghai, Singapore, Sydney, Taipei and Tokyo.

None of DTTL, its member firms, related entities, employees or agents shall be responsible for any loss or damage whatsoever arising directly or indirectly in connection with any person relying on this communication.

© 2024 Deloitte Asia Pacific Services Limited