08/23/2024 | Press release | Distributed by Public on 08/23/2024 03:40
\r\nAugust 23, 2024
August 23, 2024
Our recent study reveals the key factors inhibiting or accelerating business adoption of generative AI in Australia and New Zealand-and how ANZ businesses can ensure gen AI success.
\r\n"}}" id="text-5bfdb0f0c5" class="cmp-text">Our recent study reveals the key factors inhibiting or accelerating business adoption of generative AI in Australia and New Zealand-and how ANZ businesses can ensure gen AI success.
Businesses in Australia and New Zealand (ANZ) believe generative AI is critical to their future success. Buoyed by their conviction, they plan to spend over $50 million on this powerful technology this year alone, a little over the global average of $47 million.
\r\nHowever, a clear majority (69%) believe they aren't moving fast enough with their generative AI strategies, and over half (52%) believe these delays will result in a competitive disadvantage.
\r\nAt the same time, the ANZ region seems to offer an advantageous landscape for generative AI adoption relative to many other countries. The fact is, regional variances-regulatory environment, country infrastructure and available talent, for instance-as well as internal factors like the business's own technology foundation, will influence success with implementing generative AI strategies and how businesses use this powerful technology. As a result, the pace of generative AI uptake and the way in which it's used will be uneven across the globe.
\r\nTo better understand what generative AI adoption will look like globally, we conducted a study of 2,200 business leaders in 23 countries and 15 industries, including 200 in ANZ. The study assessed a wide range of generative AI adoption trends, including investment levels, use cases, how critical gen AI strategies are to business success and organizational readiness to adopt the technology.
\r\nWe also analyzed 18 regional and internal business factors that will either inhibit or accelerate business adoption of gen AI (see end of article for the full list of factors). Respondents evaluated each factor's potential impact on their generative AI strategy, rating it as either positive or negative on a scale of high to low impact.
\r\nFrom the results, we calculated a "momentum score" for each country or region. The momentum score represents the level of confidence business leaders have about being able to roll out their generative AI strategy based on internal business factors and the prevailing local conditions of their country or region.
\r\nFor all the regions covered, inhibitors to adoption outranked accelerators, meaning that all momentum scores skewed negative. In effect, businesses globally feel constrained by their operating environment.
\r\nBut to understand how different regions varied relative to each other, we averaged the ratings to establish a baseline global momentum score. This approach enabled us to identify regions that are more optimistic about their ability to adopt the technology compared with a global average.
\r\nFor Australia and New Zealand, the momentum score is 15% higher than the global average. The factors contributing to this score vary, but the most impactful are respondents' more optimistic views than their global peers when it comes to their data readiness and operating model flexibility. Momentum is equally driven by a relatively less pessimistic view of their existing technology infrastructure and the cost and availability of capital, although ANZ businesses still rate both factors as significant inhibitors. Pulling the score back, ANZ leaders are more acutely concerned about generative AI's impact on sustainability and have above-average concerns about current and prospective employee perceptions.
\r\nANZ gen AI scorecard
\r\n"}}" id="spy-1" class="cmp-text">Businesses in Australia and New Zealand (ANZ) believe generative AI is critical to their future success. Buoyed by their conviction, they plan to spend over $50 million on this powerful technology this year alone, a little over the global average of $47 million.
However, a clear majority (69%) believe they aren't moving fast enough with their generative AI strategies, and over half (52%) believe these delays will result in a competitive disadvantage.
At the same time, the ANZ region seems to offer an advantageous landscape for generative AI adoption relative to many other countries. The fact is, regional variances-regulatory environment, country infrastructure and available talent, for instance-as well as internal factors like the business's own technology foundation, will influence success with implementing generative AI strategies and how businesses use this powerful technology. As a result, the pace of generative AI uptake and the way in which it's used will be uneven across the globe.
To better understand what generative AI adoption will look like globally, we conducted a study of 2,200 business leaders in 23 countries and 15 industries, including 200 in ANZ. The study assessed a wide range of generative AI adoption trends, including investment levels, use cases, how critical gen AI strategies are to business success and organizational readiness to adopt the technology.
We also analyzed 18 regional and internal business factors that will either inhibit or accelerate business adoption of gen AI (see end of article for the full list of factors). Respondents evaluated each factor's potential impact on their generative AI strategy, rating it as either positive or negative on a scale of high to low impact.
From the results, we calculated a "momentum score" for each country or region. The momentum score represents the level of confidence business leaders have about being able to roll out their generative AI strategy based on internal business factors and the prevailing local conditions of their country or region.
For all the regions covered, inhibitors to adoption outranked accelerators, meaning that all momentum scores skewed negative. In effect, businesses globally feel constrained by their operating environment.
But to understand how different regions varied relative to each other, we averaged the ratings to establish a baseline global momentum score. This approach enabled us to identify regions that are more optimistic about their ability to adopt the technology compared with a global average.
For Australia and New Zealand, the momentum score is 15% higher than the global average. The factors contributing to this score vary, but the most impactful are respondents' more optimistic views than their global peers when it comes to their data readiness and operating model flexibility. Momentum is equally driven by a relatively less pessimistic view of their existing technology infrastructure and the cost and availability of capital, although ANZ businesses still rate both factors as significant inhibitors. Pulling the score back, ANZ leaders are more acutely concerned about generative AI's impact on sustainability and have above-average concerns about current and prospective employee perceptions.
ANZ gen AI scorecard
Base: 200 senior business leaders in ANZ
\r\n Source: Cognizant and Oxford Economics
\r\n Figure 1
As for where their generative AI investments will be aimed in the near term, we looked at two distinct uses of the technology: productivity, such as helping people work more quickly and get more done, and disrupt-the-business innovations, which involves more sweeping change to business and operating models. Overall, ANZ mirrors the global trend: Over the next two years, more respondents emphasized productivity as their greatest strategic priority (see Figure 2).
\r\nGreater focus on productivity than innovation
\r\nQ: Which of the following best describes the role generative AI will play in your organization's business strategy in the next two years? (Percent of respondents naming each as a top-3 choice)
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Base: 200 senior business leaders in ANZ
Source: Cognizant and Oxford Economics
Figure 1
As for where their generative AI investments will be aimed in the near term, we looked at two distinct uses of the technology: productivity, such as helping people work more quickly and get more done, and disrupt-the-business innovations, which involves more sweeping change to business and operating models. Overall, ANZ mirrors the global trend: Over the next two years, more respondents emphasized productivity as their greatest strategic priority (see Figure 2).
Greater focus on productivity than innovation
Q: Which of the following best describes the role generative AI will play in your organization's business strategy in the next two years? (Percent of respondents naming each as a top-3 choice)
Base: 200 senior business leaders in ANZ
\r\n Source: Cognizant and Oxford Economics
\r\n Figure 2
However, our study also reveals a change in what productivity means when pursued with generative AI. The end goal is not efficiency and cost-cutting as has been the case with previous automation endeavors. This new dynamic requires fresh thinking around understanding business use cases of generative AI, which we'll address later in this report.
\r\nThis report identifies the regional and business factors that could either inhibit or accelerate generative AI momentum in the ANZ region. It also provides an industry-specific look at how generative AI will be used, a regional focus on business readiness and strategies to successfully implement generative AI in ANZ.
\r\n"}}" id="text-b92e460311" class="cmp-text">
Base: 200 senior business leaders in ANZ
Source: Cognizant and Oxford Economics
Figure 2
However, our study also reveals a change in what productivity means when pursued with generative AI. The end goal is not efficiency and cost-cutting as has been the case with previous automation endeavors. This new dynamic requires fresh thinking around understanding business use cases of generative AI, which we'll address later in this report.
This report identifies the regional and business factors that could either inhibit or accelerate generative AI momentum in the ANZ region. It also provides an industry-specific look at how generative AI will be used, a regional focus on business readiness and strategies to successfully implement generative AI in ANZ.
To dig deeper into these mechanics, rather than comparing to a global average, we'll now examine how business leaders rate inhibitors and accelerators within their region. By doing so, our study provides a detailed temperature check that leaders can use to take advantage of what's working well in their local environment, while strategizing on overcoming challenges.
\r\nA look at ANZ gen AI accelerators
\r\n"}}" id="spy-2" class="cmp-text">To dig deeper into these mechanics, rather than comparing to a global average, we'll now examine how business leaders rate inhibitors and accelerators within their region. By doing so, our study provides a detailed temperature check that leaders can use to take advantage of what's working well in their local environment, while strategizing on overcoming challenges.
A look at ANZ gen AI accelerators
Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage difference to the country's momentum score compared to the global baseline.
\r\n
Base: 200 senior business leaders in ANZ
\r\n Source: Cognizant and Oxford Economics
\r\n Figure 3
A key driver for adoption in the region is the market demand for generative AI. Businesses in the region recognize that the market has spoken, making it essential to embed the technology into their operations or product and service offerings.
\r\nLeaders in the region also have a rosy view about the flexibility of their operating model-believing they can adapt their processes to make the most out of generative AI. They are also optimistic about the readiness of their data to power the technology, naming this as the third highest accelerator for adoption. At the same time, they seem far less sure about the readiness of their overall technological infrastructure. A scant 9% cited their technological infrastructure as a gen AI accelerator.
\r\nIn effect, businesses recognize they have huge swathes of valuable data dotted around their business but are less sure about their technological capabilities to enable generative AI to use it. Digging deeper into the data, we can see the nuances of this challenge laid bare. Leaders are more likely to rate the quality and cleanliness of their data as either good or excellent (52% of respondents sit in this category). However, only 16% of respondents have a similarly positive view of the accessibility of their data. The risk is that any momentum gained through high data readiness will be stalled by challenges with legacy technology.
\r\nThe perceived output quality of existing generative AI solutions is another adoption accelerator for ANZ businesses. In many ways, ChatGPT captured the imaginations of businesses and individuals alike by the speed and quality of response to various prompts-albeit with a rocky start and multiple high-profile gaffs. But as this output has improved over the last year, business enthusiasm for generative AI has increased too.
\r\nThis confidence in the quality of generative AI output plays out in the wide range of business cases ANZ respondents highlighted in our survey. Nearly, one-third have fully deployed a generative AI solution for customer-facing communications. And 29% already use it to write or test software code.
\r\nUnderstanding ANZ's gen AI inhibitors
\r\n"}}" id="text-05384276e7" class="cmp-text">Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage difference to the country's momentum score compared to the global baseline.
Base: 200 senior business leaders in ANZ
Source: Cognizant and Oxford Economics
Figure 3
A key driver for adoption in the region is the market demand for generative AI. Businesses in the region recognize that the market has spoken, making it essential to embed the technology into their operations or product and service offerings.
Leaders in the region also have a rosy view about the flexibility of their operating model-believing they can adapt their processes to make the most out of generative AI. They are also optimistic about the readiness of their data to power the technology, naming this as the third highest accelerator for adoption. At the same time, they seem far less sure about the readiness of their overall technological infrastructure. A scant 9% cited their technological infrastructure as a gen AI accelerator.
In effect, businesses recognize they have huge swathes of valuable data dotted around their business but are less sure about their technological capabilities to enable generative AI to use it. Digging deeper into the data, we can see the nuances of this challenge laid bare. Leaders are more likely to rate the quality and cleanliness of their data as either good or excellent (52% of respondents sit in this category). However, only 16% of respondents have a similarly positive view of the accessibility of their data. The risk is that any momentum gained through high data readiness will be stalled by challenges with legacy technology.
The perceived output quality of existing generative AI solutions is another adoption accelerator for ANZ businesses. In many ways, ChatGPT captured the imaginations of businesses and individuals alike by the speed and quality of response to various prompts-albeit with a rocky start and multiple high-profile gaffs. But as this output has improved over the last year, business enthusiasm for generative AI has increased too.
This confidence in the quality of generative AI output plays out in the wide range of business cases ANZ respondents highlighted in our survey. Nearly, one-third have fully deployed a generative AI solution for customer-facing communications. And 29% already use it to write or test software code.
Understanding ANZ's gen AI inhibitors
Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage difference to the country's momentum score compared to the global baseline.
\r\n
Base: 200 senior business leaders in ANZ
\r\n Source: Cognizant and Oxford Economics
\r\n Figure 4
Chief among the factors inhibiting adoption, meanwhile, is the cost and availability of talent. Talent shortages continue to be challenging for the area; according to the Australian Government's analysis of current skills shortages, 36% of occupations assessed were in national shortage, an increase of 5% since 2022.
\r\nTo overcome the talent crunch, over half (54%) of ANZ respondents plan to implement training programs to upskill employees. One-third, however, are looking to hire specialized generative AI talent-difficult to do amid talent scarcity.
\r\nConsumer perception is another top inhibitor to gen AI adoption. A major source of consumer concern is the potential for job losses due to gen AI. However, an extremely small number of ANZ businesses believe they will need to lay off employees because of generative AI-only 3% of respondents said this was a real possibility.
\r\nInstead, many ANZ businesses plan to help affected employees shift into roles in other areas of the business (46%) or provide training to upskill and boost productivity (34%). Businesses in the region need to do a better job of communicating their intended use of generative AI and how that will impact employees and the labor market.
\r\nAnother area of consumer concern has to do with potential crises fueled by AI. In a recent survey by The Conversation, 80% of Australians believe preventing catastrophic risks from advanced AI systems should be a global priority on par with pandemics and nuclear war.
\r\nTo this end, the Australian Government is working to drive responsible adoption of AI. In early 2023, it commissioned a report to assess the opportunities and risks of generative AI models, followed by the release of a public discussion paper, "Safe and Responsible AI In Australia", which invited submissions and feedback from businesses and the community.
\r\nThe Australian Government has also committed A$41.2 million to support the responsible deployment of AI in the national economy. Further, in its 2023-2024 budget, the government allocated A$101.2 million over five years to support businesses in integrating quantum and AI technologies into their operations.
\r\nA third inhibitor to generative AI adoption is the perceived maturity of generative AI solutions. While executives seem bullish about the capabilities of available solutions, they are looking for a wider and deeper array of generative AI solutions to unlock the next level of value and tackle more complex business challenges.
\r\n"}}" id="text-9fad6e9825" class="cmp-text">Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage difference to the country's momentum score compared to the global baseline.
Base: 200 senior business leaders in ANZ
Source: Cognizant and Oxford Economics
Figure 4
Chief among the factors inhibiting adoption, meanwhile, is the cost and availability of talent. Talent shortages continue to be challenging for the area; according to the Australian Government's analysis of current skills shortages, 36% of occupations assessed were in national shortage, an increase of 5% since 2022.
To overcome the talent crunch, over half (54%) of ANZ respondents plan to implement training programs to upskill employees. One-third, however, are looking to hire specialized generative AI talent-difficult to do amid talent scarcity.
Consumer perception is another top inhibitor to gen AI adoption. A major source of consumer concern is the potential for job losses due to gen AI. However, an extremely small number of ANZ businesses believe they will need to lay off employees because of generative AI-only 3% of respondents said this was a real possibility.
Instead, many ANZ businesses plan to help affected employees shift into roles in other areas of the business (46%) or provide training to upskill and boost productivity (34%). Businesses in the region need to do a better job of communicating their intended use of generative AI and how that will impact employees and the labor market.
Another area of consumer concern has to do with potential crises fueled by AI. In a recent survey by The Conversation, 80% of Australians believe preventing catastrophic risks from advanced AI systems should be a global priority on par with pandemics and nuclear war.
To this end, the Australian Government is working to drive responsible adoption of AI. In early 2023, it commissioned a report to assess the opportunities and risks of generative AI models, followed by the release of a public discussion paper, "Safe and Responsible AI In Australia", which invited submissions and feedback from businesses and the community.
The Australian Government has also committed A$41.2 million to support the responsible deployment of AI in the national economy. Further, in its 2023-2024 budget, the government allocated A$101.2 million over five years to support businesses in integrating quantum and AI technologies into their operations.
A third inhibitor to generative AI adoption is the perceived maturity of generative AI solutions. While executives seem bullish about the capabilities of available solutions, they are looking for a wider and deeper array of generative AI solutions to unlock the next level of value and tackle more complex business challenges.
Of course, there are many use cases and strategies for using generative AI. As we've said, ANZ businesses are primarily focused on realizing productivity gains with generative AI, at least in the next two years. However, a look at what's driving their business cases sheds a new light on productivity from how it's been seen historically.
\r\nTraditionally, businesses have equated automation productivity gains with cost-cutting: driving down the cost of output by reducing the number of people needed to get work done.
\r\nWhile generative AI-driven automation will likely lower headcount to some degree, that is no longer the end goal. Instead, as seen through the metrics respondents will use to drive business cases, we see a shift toward redirecting productivity gains into funding endeavors that increase revenues or lead to entirely new revenue streams.
\r\n
The metrics ANZ respondents say will be most important for justifying generative AI expenditures include increasing revenues, discovering new revenue sources and creating new products and services all of which were named by at least 46% of respondents (see Figure 5). Conversely, metrics like cost savings, time-to-market and productivity were cited by 38% of respondents or fewer.
\r\nIn other words, the concept of productivity no longer stops at cost-cutting-businesses appear to be redirecting productivity gains into initiatives aimed at growth.
Revenue is a top metric for justifying gen AI use cases
\r\nQ: Which of the following metrics are most important in terms of justifying your organization's generative AI business cases? (Percent of respondents naming each as a top-3 choice)
\r\n"}}" id="spy-3" class="cmp-text">Of course, there are many use cases and strategies for using generative AI. As we've said, ANZ businesses are primarily focused on realizing productivity gains with generative AI, at least in the next two years. However, a look at what's driving their business cases sheds a new light on productivity from how it's been seen historically.
Traditionally, businesses have equated automation productivity gains with cost-cutting: driving down the cost of output by reducing the number of people needed to get work done.
While generative AI-driven automation will likely lower headcount to some degree, that is no longer the end goal. Instead, as seen through the metrics respondents will use to drive business cases, we see a shift toward redirecting productivity gains into funding endeavors that increase revenues or lead to entirely new revenue streams.
The metrics ANZ respondents say will be most important for justifying generative AI expenditures include increasing revenues, discovering new revenue sources and creating new products and services all of which were named by at least 46% of respondents (see Figure 5). Conversely, metrics like cost savings, time-to-market and productivity were cited by 38% of respondents or fewer.
In other words, the concept of productivity no longer stops at cost-cutting-businesses appear to be redirecting productivity gains into initiatives aimed at growth.
Revenue is a top metric for justifying gen AI use cases
Q: Which of the following metrics are most important in terms of justifying your organization's generative AI business cases? (Percent of respondents naming each as a top-3 choice)
Base: 200 senior business leaders in ANZ
\r\nSource: Cognizant and Oxford Economics
\r\nFigure 5
Using this more granular view of productivity goals and business drivers, we analyzed the differences in how industries intend to use the technology.
\r\nRather than focusing on the distinction between productivity vs. innovation, we grouped the metrics into two high-level categories of business use cases:
\r\nWe then assigned each of the metrics a score to see the relative gap between a number-one-ranking metric and a number-three-ranking metric. By calculating the average score across industries, we could clearly see how each industry's responses deviated from the baseline.
\r\nOur analysis reveals stark differences among ANZ industries in terms of the business use cases they'll likely prioritize (see Figure 6).
\r\nIndustries diverge on business cases
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Base: 200 senior business leaders in ANZ
Source: Cognizant and Oxford Economics
Figure 5
Using this more granular view of productivity goals and business drivers, we analyzed the differences in how industries intend to use the technology.
Rather than focusing on the distinction between productivity vs. innovation, we grouped the metrics into two high-level categories of business use cases:
We then assigned each of the metrics a score to see the relative gap between a number-one-ranking metric and a number-three-ranking metric. By calculating the average score across industries, we could clearly see how each industry's responses deviated from the baseline.
Our analysis reveals stark differences among ANZ industries in terms of the business use cases they'll likely prioritize (see Figure 6).
Industries diverge on business cases
Note: This figure depicts each industry's relative deviation from a baseline of "zero," using a ranked scoring of the top-three metrics respondents cite as important for justifying their generative AI use cases. It reveals a weighted view of each industry's overall priorities for gen AI deployment.
\r\n
Base: 200 senior business leaders in ANZ
\r\n Source: Cognizant and Oxford Economics
\r\n Figure 6
Note: This figure depicts each industry's relative deviation from a baseline of "zero," using a ranked scoring of the top-three metrics respondents cite as important for justifying their generative AI use cases. It reveals a weighted view of each industry's overall priorities for gen AI deployment.
Base: 200 senior business leaders in ANZ
Source: Cognizant and Oxford Economics
Figure 6
A remaining question is whether businesses are ready to drive real value from these business cases.
\r\nThe answer, according to our research, is mixed. To better understand how prepared executives believe their business is to adopt generative AI, we asked respondents to rank their organization's maturity on a scale of 1 to 4 by selecting a statement that best described their organization in the following five areas, from low maturity to high:
\r\nThe message from business leaders in the ANZ region is evident: Leadership commitment is high, and strategies are robust (perhaps a testament to the multimillion-dollar investments most respondents report). However, the fundamental operational and technological building blocks necessary to adopt the technology are lacking (see Figure 7).
\r\nLeadership support is sound, but fundamentals are lacking
\r\nRespondents were asked to rate the maturity of their organization's operations in relation to generative AI. (Percent of respondents rating each as a 3 or 4, with 4 representing the highest level of maturity)
\r\n"}}" id="spy-4" class="cmp-text">A remaining question is whether businesses are ready to drive real value from these business cases.
The answer, according to our research, is mixed. To better understand how prepared executives believe their business is to adopt generative AI, we asked respondents to rank their organization's maturity on a scale of 1 to 4 by selecting a statement that best described their organization in the following five areas, from low maturity to high:
The message from business leaders in the ANZ region is evident: Leadership commitment is high, and strategies are robust (perhaps a testament to the multimillion-dollar investments most respondents report). However, the fundamental operational and technological building blocks necessary to adopt the technology are lacking (see Figure 7).
Leadership support is sound, but fundamentals are lacking
Respondents were asked to rate the maturity of their organization's operations in relation to generative AI. (Percent of respondents rating each as a 3 or 4, with 4 representing the highest level of maturity)
Base: 200 senior business leaders in ANZ
\r\nSource: Cognizant and Oxford Economics
\r\nFigure 7
Unsurprisingly, given that talent shortages sit high on the list of the biggest inhibitors impacting the region, respondents assign low ratings to the maturity of their business's skills availability and talent strategy. When it comes to the underlying tech infrastructure, while data quality is rated high (52% rate it as good or excellent), many other foundational aspects are lacking. These include the ability to comply with company rules, policies and frameworks, data security and customer privacy. All of these technology infrastructure capabilities achieved the lowest two ranks by the majority of respondents.
\r\n"}}" id="text-5f2e4fb838" class="cmp-text">
Base: 200 senior business leaders in ANZ
Source: Cognizant and Oxford Economics
Figure 7
Unsurprisingly, given that talent shortages sit high on the list of the biggest inhibitors impacting the region, respondents assign low ratings to the maturity of their business's skills availability and talent strategy. When it comes to the underlying tech infrastructure, while data quality is rated high (52% rate it as good or excellent), many other foundational aspects are lacking. These include the ability to comply with company rules, policies and frameworks, data security and customer privacy. All of these technology infrastructure capabilities achieved the lowest two ranks by the majority of respondents.
ANZ is an "accelerating" region when it comes to generative AI momentum. The challenge ahead is to take full advantage of the factors that could encourage gen AI strategy success while overcoming the inhibitors.
\r\nTo navigate these challenges, executives should prioritize the following actions:
\r\n*The full list of regional factors we evaluated includes: the flexibility of the existing operating model, market demand for gen AI-enabled products and services, data readiness, quality of output from gen AI, availability of compute power, cost/availability of gen AI-related technologies, shareholder/investor sentiment, regulatory environment, sustainability, national infrastructure, cost/availability of capital, data privacy and security, existing technology infrastructure, current and prospective employee perceptions, flexibility of the existing business model, maturity of gen AI-related technologies, consumer perceptions and cost/availability of talent.
\r\nLearn about the impact of generative AI on jobs and the economy in our report New Work New World.
\r\n"}}" id="spy-5" class="cmp-text">ANZ is an "accelerating" region when it comes to generative AI momentum. The challenge ahead is to take full advantage of the factors that could encourage gen AI strategy success while overcoming the inhibitors.
To navigate these challenges, executives should prioritize the following actions:
*The full list of regional factors we evaluated includes: the flexibility of the existing operating model, market demand for gen AI-enabled products and services, data readiness, quality of output from gen AI, availability of compute power, cost/availability of gen AI-related technologies, shareholder/investor sentiment, regulatory environment, sustainability, national infrastructure, cost/availability of capital, data privacy and security, existing technology infrastructure, current and prospective employee perceptions, flexibility of the existing business model, maturity of gen AI-related technologies, consumer perceptions and cost/availability of talent.
Learn about the impact of generative AI on jobs and the economy in our report New Work New World.