A look ahead at the fast-paced evolution of technology, regulation and business The scale of adoption in business Companies must reinvent work to find a path to generative AI value. Business leaders must lead the change, starting now, in job redesign, task redesign and reskilling people. Ultimately, every role in an enterprise has the potential to be reinvented, once today’s jobs are decomposed into tasks that can be automated or assisted and reimagined for a new future of human + machine work. Generative AI will disrupt work as we know it today, introducing a new dimension of human and AI collaboration in which most workers will have a “co- pilot,” radically changing how work is done and what work is done. Nearly every job will be impacted – some will be eliminated, most will be transformed, and many new jobs will be created. Organizations that take steps now to decompose jobs into tasks, and invest in training people to work differently, alongside machines, will define new performance frontiers and have a big leg up on less imaginative competitors. 54% 48% 36% 40% 43% 33% 34% 31% 28% 30% 26% 30% 26% 28% 27% 25% 26% 24% 24% 20% 12% 14% 21% 14% 9% 13% 7% 9% 11% 9% 13% 6% 8% 6% 6% 8% 6% 6% 5% 5% 24% 26% 28% 29% 14% 21% 12% 22% 33% 35% 20% 13% 16% 15% 15% 17% 14% 13% 14% 11% 10% 12% 15% 18% 34% 33% 46% 38% 27% 26% 41% 50% 50% 50% 52% 50% 54% 57% 56% 64% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Banking Insurance Software & Platforms Capital markets Energy Communications & Media Retail Industry Average Health Public Service Aerospace & Defense Automotive High Tech Travel Utilities Life Sciences Industrial Consumer Goods & Services Chemicals Natural Resources Figure 3: Generative AI will transform work across industries Work time distribution by industry and potential AI impact Based on their employment levels in the US in 2021 40% of working hours across industries can be impacted by Large Language Models (LLMs) Why is this the case? Language tasks account for 62% of total worked time in the US. Of the overall share of language tasks, 65% have high potential to be automated or augmented by LLMs. Source: Accenture Research based on analysis of Occupational Information Network (O*NET), US Dept. of Labor; US Bureau of Labor Statistics. Notes: We manually identified 200 tasks related to language (out of 332 included in BLS), which were linked to industries using their share in each occupation and the occupations’ employment level in each industry. Tasks with higher potential for automation can be transformed by LLMs with reduced involvement from a human worker. Tasks with higher potential for augmentation are those in which LLMs would need more involvement from human workers. Higher potential for automation Higher potential for augmentation Lower potential for augmentation or automation Non-language tasks 11 A new era of generative AI for everyone |