GenAI has the potential to revolutionize current ways of working, necessitating workforce upskilling and reskilling. Its integration can transform content creation and reshape job roles, such as in production, editing, distribution and marketing. By automating repetitive and technical tasks, it can create opportunities for professionals to focus on higher-order creativity and strategy. However, this shift requires a robust investment in workforce development to build both the technical skills and ethical awareness needed for working with genAI. A holistic training approach and a safe environment for experimentation are vital for adoption. Key workforce challenges include: –Job transition: AI-driven automation may reduce the need for certain roles, requiring proactive workforce management and strategies to transition workers to new roles. The impact will vary across industry sectors and areas, so organizations will need to tailor their strategies to rebalance labour demands. –Cautious adoption by the workforce: Many workers are cautious and concerned about the large-scale adoption of AI. To support effective onboarding, organizations must offer comprehensive training, transparent adoption roadmaps and guidelines for genAI use. Regulation and self-governance are also needed to ensure the responsible use of digitally replicated likenesses and performances. –Skill augmentation: Employees will need acquire new skills, particularly in AI and data management, to remain competitive in the labour market. According to Accenture research,54 the media and entertainment industry ranks among the top five industries with high automation potential (refer to the AI in Action: Beyond Experimentation to Transform Industry Value paper for an overview). –On average, 50% of working hours in the industry can be transformed by LLMs, as they have a high potential to be automated or augmented.55 –A total of 24% of tasks are susceptible to automation, particularly in manual and routine roles.56 –A total of 26% of tasks could be augmented, enhancing roles that involve creativity and strategic decision-making.573.2 Workforce Potential large language model (LLM) impact distribution on work time in media and entertainmentFIGURE 5 Weighted by employment levels Higher potential for automation Higher potential for augmentation Lower potential for augmentation or automation Non-language tasksPublishing activities28% 35% 22% 15% Advertising and market research 15% 26% 32% 28% Gambling and betting activities30% 18% 24% 28%Information service activities 15% 23% 31% 30% Programming and broadcasting activities21% 22% 35% 22% Motion picture, video and television programme production, sound recording and music publishing activities26% 21% 32% 22% Printing and reproduction of recorded media50% 13% 15% 22%Creative, arts and entertainment activities29% 22% 28% 21% Sports activities and amusement and recreation activities34% 24% 21% 20% Source: Accenture research analysis based on O*NET and national statistical databases from 22 countries and 19,000 work tasks. Artificial Intelligence in Media, Entertainment and Sport 20

Artificial Intelligence in Media Entertainment and Sport 2025