New research from Accenture finds GenAI could create an extra of 4.5 trillion (USD) in economic value in APAC, equivalent to a 0.7 percentage points increase in annual GDP growth, over the next 15 years by adopting the technology responsibly, at scale, and focusing on people.
The research combines economic modelling conducted for four of Asia Pacific’s largest economies, including Australia, China, India and Japan, and a CXO survey in these countries and Singapore.
Key survey findings include:
⦁ 33% of working hours across APAC will either be automated or augmented by generative AI, leading to a productivity boost. Working hours in Australia and Japan will be most impacted at 45% and 44% respectively, followed by China (33%).
⦁ In India, 31% of working hours will either be automated or augmented by generative AI. It could lead to a 0.6 percentage point increase in GDP growth per year and an additional 675 billion (USD) in economic value by 2038.
⦁ 96% of APAC business leaders acknowledge the significant impact of generative AI, and 91% of APAC workers indicate that they are keen to acquire new skills to work with generative AI, but only 4% of business leaders have rolled out generative AI training at scale. Similarly, 89% of APAC businesses are planning to increase their spending on generative AI technology this year, but only 35% are prioritising investments in their workforce’s development.
⦁ The most impacted industries include Capital markets where generative AI will transform nearly three-quarters of working hours (71%) and Software and Platforms where two-thirds (66%) of working hours will be automated or augmented. This is followed by Banking (64%), Insurance (62%) and Retail (49%).
Saurabh Kumar Sahu, Lead for India Business at Accenture said, “The scaled and responsible deployment of generative AI can not only drive revenue growth, but also act as a force of change that can reinvent almost all functions across industries. To unlock its real value, businesses need to have a bold vision for reinvention anchored in a strong data and technology foundation. This coupled with an intentional approach to skilling is crucial to succeed in the age of generative AI.”
To fully leverage the potential of generative AI, Accenture recommends that businesses take the following steps:
⦁ Lead and learn in new ways: To be effective and build trust in the generative AI-enabled future, leaders need to engage, lead differently and challenge old mindsets to learn new things. It is important that leaders immerse themselves in the technology, effectively changing how they learn by embedding learning into the flow of work.
⦁ Reinvent work: By rethinking entire workflows, leaders can gain a clear view of where generative AI can be most impactful, aligning it with business goals for better efficiency and innovation across the enterprise and collapsing silos in a lasting, meaningful way. From there, it’s possible to re-focus on how the work needs to change to better serve customers, support people and achieve business outcomes.
⦁ Reshape the workforce: The shift in how work is done demands a dynamic and adaptable workforce. Organisations need to prioritise continuous talent reinvention. As use of this technology grows, organisations should further leverage tools and technologies, such as skills mapping, that can help facilitate smoother transitions from declining to emerging roles. And as work and roles shift, increased capacity can free up time and talent for higher value activities.
⦁ Prepare workers: As organisations invest in helping workers acquire market-relevant technical skills and the capability to collaborate with machines, they will also need to focus on soft skills. A teach-to-learn model is emerging to equip workers to teach the machines. Along this journey, leaders also need to listen and involve their people at every step of the way to strengthen trust.
Further information on the economic modelling
To estimate the working hours likely to be impacted by gen AI and the associated impact on GDP, Accenture looked at data from four of the five largest economies in APAC – Australia, China, Japan and India:
1. Breakdown jobs into tasks which we then tagged according to potential for automation and augmentation (using machine learning with human verification
2. Used economic literature to estimate the total hours saved based on current state of generative AI
3. Explored likely job transitions (based on historical trends and similarities in skill composition)
4. Built out different scenarios of how organisations could adopt generative AI across three parameters: innovation focus, pace of adoption, and degree of talent displacement.
5. Modelled GDP growth (2023-2038) for each geography under each scenario (which we compared against baseline GDP growth projections)
