The next business advantage will come from leaders who manage machine labour as deliberately as they manage people, according to Dr Gleb Tsipursky. Professor Ethan Mollick at Wharton framed this as AI management after executive MBA students used ChatGPT, Claude, Gemini, Claude Code, and Google Antigravity to build working startup prototypes in four days. McKinsey’s 2025 global survey found that 88 per cent of respondents reported regular AI use in at least one business function, while 62 per cent said their organisations were at least experimenting with AI agents, yet nearly two-thirds had not begun scaling AI across the enterprise. OpenAI’s GDPval evaluation tested model performance on 1,320 specialised tasks from 44 knowledge-work occupations, reporting models could complete some tasks roughly 100 times faster and cheaper than experts, excluding human oversight, iteration, and integration. In a field experiment with 758 Boston Consulting Group consultants, those using GPT-4 completed 12.2 per cent more tasks, finished 25.1 per cent faster, and produced more than 40 per cent higher-quality work on tasks inside AI’s capability range, but on one task outside that range, they were 19 percentage points less likely to reach the correct answer. An NBER study of 5,179 customer support agents found access to a generative AI assistant increased productivity by 14 per cent on average and by 34 per cent for novice and lower-skilled workers. A 2026 benchmark across