3 min readNew DelhiFeb 27, 2026 11:00 PM IST
At a time when artificial intelligence tools can draft emails, generate code, analyse data, and create presentations in seconds, the traditional step-by-step workflow is giving way to something more fluid. This has led to the rise of ‘vibe working,’ a new workplace phrase reshaping daily tasks.
Instead of meticulously planning every detail before execution, workers increasingly describe their goals in simple language and then collaborate with AI tools to refine, iterate, and build toward a finished product. The idea evolved from ‘vibe coding’ a term popularised in tech circles as developers began using generative AI to co-create software through ongoing prompts and adjustments rather than writing every line manually.
At its core, vibe working is about turning fuzzy thoughts into structured outputs through iteration. It emphasises experimentation over perfection, allowing ideas to evolve through continuous feedback between human judgement and machine suggestions.
But how does vibe working fundamentally change traditional workplace structures?
Dr Sakshi Mandhyan, psychologist and founder at Mandhyan Care, tells indianexpress.com, “I see vibe working, shifting workplaces from fixed hierarchies to environments of adaptive collaboration. Our earlier workplaces and systems valued predictability and defined roles. The present AI-supported environments reward curiosity and rapid iteration. Psychologically, this demands cognitive flexibility and tolerance for ambiguity. Employees will no longer just execute tasks. They will continuously refine ideas with technology.”
She continues, “I believe that in such a scenario, emotional regulation also becomes important because constant feedback cycles can create mental fatigue.” She mentions that professionals need metacognition, which means thinking about how they think. They must question AI outputs rather than accept them passively. Communication skills and ethical judgement also gain importance.
What risks come with relying heavily on AI for idea generation and execution?
Dr Mandhyan sees the biggest risk as “cognitive outsourcing.” As people start relying excessively on AI, she explains, the brain reduces effort in analysis and memory formation. Research in cognitive psychology shows that expertise develops through struggle and repetition. Without this process completing its course properly, the depth of knowledge weakens.
Accountability can also get blurred. If an outcome fails, the responsibility may feel diffused between human and machine. “Our long-term expertise principally depends on reflective thinking. We as professionals must remain active decision makers. AI should accelerate our thinking process and not replace judgement,” states the expert.
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Balancing experimentation with clear metrics and training
According to Dr Mandhyan, experimentation works best only when people know what they are trying to learn from the process. The brain needs clarity to stay motivated. Without it, curiosity quickly turns into anxiety or comparison.
“I advise business leaders to combine freedom with structure. Psychological safety allows people to test ideas without fear, but accountability gives direction to that freedom. With clear metrics, employees understand effort and impact. I believe that training, as always, should not only teach tools but also strengthen judgement, ethical reasoning, and decision ownership,” says Dr Mandhyan.
“I also notice that sustainable productivity comes from pacing our innovation,” explains Dr Mandhyan, adding that when teams reflect on outcomes and question results through multiple perspectives, learning naturally becomes deeper. It must also be understood that AI performs best when human thinking stays active and responsible.
