Concerns and challenges
As with any new technology, generative AI poses many challenges, some more daunting than others
Despite its widespread adoption and more mature business deployment, APAC is not a leader in every category. There is a significant level of guardedness, with 9% of APAC leaders indicating that generative AI will have a negative impact on business — despite their successes so far (Figure 8).
More generally, business leaders in APAC are slightly less positive and slightly more negative about generative AI’s impact on business than those in North America and Europe. APAC companies are also a little more likely to be neutral about generative AI compared to businesses in North America and about even with those in Europe.
Figure 8. General sentiment of generative AI’s expected business impact
Source: Infosys Knowledge Institute
The reasons for this negative sentiment vary widely by region, with ANZ and Asian companies more concerned about almost every potential impact compared to businesses in Europe and North America (Figure 9).
Generally, business leaders in ANZ are significantly more worried than their counterparts in other regions about generative AI’s negative impact on their business models and reputations. We found that 10% of leaders in Australia and New Zealand expect generative AI initiatives to negatively affect their reputations, compared to 3% in North America and 5% in Europe. Just over one in 10 (11%) also expect a negative impact on business models, compared to 6% each for both North America and Europe.
Generative AI Radar: APAC
Figure 9. Negative view of generative AI’s potential impact on business areas by region
Asian leaders also have significant concerns about the more practical aspects of generative AI, such as its negative impact on talent (14%) and cost efficiency (12%). However, as we discuss in our Tech Navigator report, one key function of enterprise AI is to augment human capabilities rather than replace them.
We expect that generative AI will lead to the development of new jobs as well as an evolution of previous employment categories. For example, prompt engineers will be in demand from companies wanting to extract the maximum potential from this technology. Companies are already reskilling and upskilling existing staff as an alternative to competing in the jobs market for prompt engineers, as we discuss below.
Similarly, we expect that companies will use generative AI’s capabilities as an assistant. Many firms are already experimenting with Microsoft’s Copilot, which is being rolled out alongside its enterprise suite of productivity applications.
Copilot and similar tools can take over tasks such as summarizing meetings and documents, as well as code review, code completion, and other manual tasks. This in turn frees up humans to do other work, in theory making them more productive. In short, generative AI could help mitigate talent shortages.
As with any new technology, generative AI poses many challenges, some more daunting than others. About one-third of APAC companies consider data privacy and security to be the top challenge of generative AI (Figure 10). This is consistent with governmental guidance, particularly in New Zealand, which insists on transparent and accessible discussions of ethical and privacy concerns.
Data usability also rates as the most significant challenge for about one-quarter of APAC respondents, with ethics, bias, fairness, and safety just behind.
Figure 10. Percentage of respondents by region rating each challenge as the most difficult
A further complicating factor for companies, both based in and operating in this region, is the fragmented regulatory regime. While firms in the EU benefit from that bloc’s unified approach to regulation with the EU AI Act and the General Data Protection Regulation, individual countries in the APAC zone have their own legal frameworks.
China is “very ahead of the game” on regulation, according to Professor Lilian Edwards of Newcastle University in the UK. She pointed out that the country has three pieces of legislation complete, at least two of which are already in force.
Meanwhile, Singapore is taking a wait-and-see approach to regulation, and Australia has proposed amendments to existing legislation that would, among other things, require transparency around targeting, algorithms, and profiling.
The Australian government is reviewing existing law and sought public submissions on the future governance of AI in Australia in the middle of 2023. Professor Edwards flags the potential for challenges in this fragmented landscape. “There’s going to be lots of new rules coming out about AI governance and how they are going to compete with each other.”
Despite these perceived challenges, Asian organizations are broadly positive about their readiness for generative AI deployment in the workplace. About two-thirds (67%) say their workforce is ready, the highest among the regions we have surveyed (Figure 11).
This compares to 59% of businesses in Europe and 56% in ANZ and North America that say they are positive about their workforce readiness.
Figure 11. Readiness for generative AI
This generative AI future, however, will require a workforce with new skills, many of which are in high demand and short supply. To fill the skills gaps, APAC leaders will look mainly to upskill. Respondents in every APAC country we surveyed expect to use upskilling and reskilling as their strategy for managing demand for new generative AI skills more than half of the time (55% in ANZ and 54% in Asian countries). In comparison, the frequency was 41% of the time in North America and 47% in Europe.
The countries and regions we surveyed also vary in how they think generative AI will benefit them the most (Figure 12). Asian countries expect the biggest benefits from product development and design use cases than in any other region. This is driven by China, where three in 10 companies list this as where generative AI will have the largest positive impact in their companies. Singapore is an outlier here, with nearly 40% of companies holding the same sentiment for enhanced user experience and personalization.
Like North America and Europe, ANZ companies were most likely to expect generative AI to have the most positive effect on user experience. Similarly, ANZ respondents list increased operational efficiency and automation as the use cases most likely to have the biggest impact.
Figure 12. Where generative AI is expected to have the biggest impact
Operational efficiency and automation is not a top focus for any region, despite research indicating that workers are interested in the benefits that AI-assisted automation could bring.
A 2023 study found that nearly 60% of respondents think that automation will “address burnout and improve job fulfillment.” An almost equal number more favorably view employers that modernize and use business automation to help support workers than those that do not.
The retention of skilled workers poses a pressing problem across tech-related fields globally. APAC businesses can do more to explore the benefits that automation can bring to the workforce.
There are some nuances, though. We found in our North America and Europe Generative AI Radars that content creation was not a top of mind use case for the benefits of generative AI.
However, the picture is different in Japan, where 30% of companies identified this as the most likely use case — ahead of enhanced user experience and personalization, streamlined product design, and increased efficiency and automation.
Companies pursue generative AI for different reasons, often steered by industry needs. Our survey of ANZ organizations found a split between the public and private sectors.
Public-sector organizations expect that generative AI will help them achieve operational efficiency (FIgure 13).
About four in 10 (44%) of public-sector leaders list this where they expect generative AI to do them the most good. Just two in 10 (22%) in the private sector say the same about efficiency. User experience ranks second for the region’s public sector, but it is where private-sector organizations are most likely to expect generative AI to perform well for them.
Figure 13. Areas of most positive expected impact in ANZ public and private sector organizations
Almost a third in the ANZ public sector (31%) have yet to determine who in the organization should sponsor generative AI projects. It is striking compared to only 12% responding in the “to be determined” category in the ANZ private sector. Organizational structures, processes and relationships are key areas for improvement here.
In the APAC region, the chief information security officer (CIO) is the primary sponsor for AI initiatives across the region. Executive boards also assume a significant role alongside CIOs across the region in setting policy, which highlights the need for a collaborative board-CIO relationship.
Chief information officer
Chief information security officer
Chief executive officer
Business unit leader
Don’t know
