For much of the past two years, the conversation around artificial intelligence and work has been dominated by a single fear: automation. As AI tools grow more capable, many workers have wondered whether their roles will eventually disappear altogether. But the 2026 Agentic Coding Trends Report by AI company Anthropic points to a more complex reality. Rather than removing humans from the loop, the next phase of AI adoption may make human judgment more central to professional work.
One of the report’s key findings is that “human oversight scales through intelligent collaboration”. In practical terms, this means AI systems are becoming better at recognising when they need human input, while people are learning to intervene only when their attention has the greatest impact.
The shift is most visible in software development. AI agents can now write code, run tests, debug failures, and generate documentation. Yet Anthropic’s internal research shows that while engineers use AI in roughly 60% of their work, they report being able to fully delegate only 0-20% of tasks. Most AI-assisted work still involves active supervision, validation, and decision-making by humans.
This means that instead of reviewing every output line by line, engineers are increasingly relying on agentic systems to surface issues that genuinely require human judgment, according to Anthropic. These include architectural inconsistencies, security risks, or decisions with business consequences. Routine checks are handled automatically, while uncertain or high-stakes situations are escalated to people. This is a clear shift from reviewing “everything” to reviewing “what matters”. An important distinction.
This pattern is not limited to engineering. Anthropic documents similar dynamics emerging across legal, operations, and design teams, where AI is used to automate repetitive work while humans retain control over interpretation, risk, and final approval. AI reduces busywork, but responsibility and accountability remain firmly human.
That conclusion echoes findings from outside the technology industry. In a 2024 editorial, Ekkehard Ernst, Chief Macroeconomist at the International Labour Organization, argues that debates about AI have focused too narrowly on job losses and gains. Instead, Ernst and his collaborators highlight how AI is reshaping job quality, managerial control, autonomy, and working conditions. Their analysis of labour markets across 23 OECD countries finds no clear link between AI exposure and overall employment loss, but significant changes in how work is organised and supervised.
In particular, Ernst points to evidence that AI often increases autonomy in supervisory roles while intensifying control over execution-level work. In other words, as machines take on routine tasks, human roles increasingly shift toward oversight, coordination, and decision-making, rather than direct execution.
A similar conclusion emerges from a 2025 article, ‘Understanding Human-AI Augmentation in the Workplace’, published in the journal Information Systems Frontiers, which examined human-AI augmentation across business and management research. The authors describe AI adoption as a “double-edged sword” whose outcomes depend heavily on how collaboration between humans and machines is designed. Their review finds that there is no one-size-fits-all model for AI integration, but that successful adoption consistently relies on clear human roles in supervision, judgment, and accountability.
Anthropic’s report highlights a related paradox. Despite dramatic productivity gains, AI has not reduced the importance of human experience. In interviews cited in the report, engineers say they trust AI most when they already know what the correct answer should look like. One Anthropic engineer notes that this intuition comes from having learned software engineering “the hard way”, i.e. judgment cannot be automated without first being developed by humans.
As AI systems generate more output than ever before, the bottleneck in many organisations is no longer execution, but attention. Across industry research and academic studies alike, a common theme is emerging: the scarcest resource in AI-driven workplaces is skilled human oversight. Deciding what to prioritise, what to trust, and when to intervene is fast becoming a defining part of professional value.
As AI takes on more tactical execution, human work is shifting upward: toward supervision, judgment, and responsibility for outcomes. The future of work, it seems, is not about stepping aside for machines, but about knowing when, and how, to step in.