Embrace the generative AI era: Six adoption essentials 2 Take a people-first approach Success with generative AI requires an equal attention on people and training as it does on technology. Companies should therefore dramatically ramp up investment in talent to address two distinct challenges: creating AI and using AI. This means both building talent in technical competencies like AI engineering and enterprise architecture and training people across the organization to work effectively with AI-infused processes. In our analysis across 22 job categories, for example, we found that LLMs will impact every category, ranging from 9% of a workday at the low end to 63% at the high end. More than half of working hours in 5 of the 22 occupations can be transformed by LLMs. Figure 4: Generative AI will transform work across every job category 57% 49% 28% 45% 25% 27% 21% 33% 31% 30% 29% 22% 29% 27% 29% 23% 25% 23% 15% 16% 8% 14% 9% 6% 13% 32% 14% 26% 20% 24% 9% 9% 9% 8% 15% 7% 8% 6% 8% 5% 4% 4% 1% 8% 2% 0% 14% 14% 23% 35% 26% 25% 25% 58% 22% 44% 31% 40% 59% 31% 23% 50% 9% 7% 7% 9% 17% 8% 7% 23% 24% 17% 6% 22% 28% 30% 0% 38% 17% 32% 22% 6% 34% 43% 19% 61% 66% 75% 75% 66% 76% 84% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Office and Administrative Support Sales and Related Computer and Mathematical Business and Financial Operations Arts, Design, Entertainment, Sports, and Media Life, Physical, and Social Science Architecture and Engineering Legal Occcupation Average Management Personal Care and Service Healthcare Practitioners and Technical Community and Social Service Healthcare Support Protective Service Educational Instruction and Library Food Preparation and Serving Related Transportation and Material Moving Construction and Extraction Installation, Maintenance, and Repair Farming, Fishing, and Forestry Production Building and Grounds Cleaning and Maintenance Work time distribution by major occupation and potential AI impact Based on their employment levels in the US in 2021 In 5 out of 22 occupation groups, Generative AI can affect more than half of all hours worked 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 job category. 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 15 A new era of generative AI for everyone |