India's young workers have a new colleague: AI

Calling AI a new colleague means treating it as a work assistant rather than an independent decision-maker. It can handle repetitive tasks, organize information and prepare early drafts. This matters because workers can spend more time on analysis, reporting, problem-solving and useful business insights. However, human responsibility does not disappear.

Farah Kazmi uses AI to find court rulings and dog-bite cases, then reads and cross-checks the material herself. Rakshanda Deka uses it to sort legal documents and identify important dates. Yajatt Misra uses it for summaries, data analysis and presentations. These examples show AI supporting professional work while leaving final judgment with the worker.

The arrangement has clear limits. AI can misread context, translate a name incorrectly or present false information confidently. Employers therefore want workers who can think critically and check AI-generated work. AI may make teams leaner, but it also raises expectations for what employees can do from their first day.

Young Indian workers are using AI across several kinds of professional work. Journalists use it for research and translation. Business employees use it to summarize information, analyze data and prepare presentations. Lawyers use it to organize documents and draft standard legal materials. The technology matters because it reduces time spent on repetitive tasks across different industries.

Farah Kazmi asked AI to find Supreme Court guidelines, earlier rulings and dog-bite cases. She also uses it between English and Hindi, with careful checking. Yajatt Misra turns financial information into management updates. Rakshanda Deka sorts client documents by date or theme and prepares lists of important dates. AI can also help prepare applications and petitions in a lawyer's style.

The use extends beyond individual workers. TCS reported scaling its Human+AI operating model and embedding AI across services and software development. The article presents AI as support for research, drafting, translation, organization and analysis, not as a replacement for reporting, legal thinking or final verification.

AI is already common among young Indian workers and learners. A Deloitte survey cited in the article found that 93% of Gen Z respondents and 95% of millennial respondents used AI in their day-to-day work. These figures show that AI is not limited to specialist technology roles. It is becoming part of ordinary workplace activity.

Learning use is also widespread. A separate Emeritus survey found that 82% of respondents aged 15 to 28 used AI tools at least weekly to learn or develop new skills. Together, the surveys point to regular use at work and outside immediate job tasks. They also show that younger people are adopting AI for both productivity and personal development.

The figures do not mean every user relies on AI in the same way. The article gives examples ranging from translation and document sorting to business analysis and drafting. As use expands, employers increasingly value people who can check AI output, solve problems and think critically rather than simply produce basic drafts.

Human checking is necessary because AI output can sound reliable while containing serious mistakes. The article describes errors in research, translation, business analysis and legal work. If workers accept those answers automatically, incorrect information could enter news reports, management decisions, legal documents or presentations. Verification protects the quality and meaning of professional work.

Farah Kazmi found that AI interpreted the name Suraj as “sun,” changing a sentence's meaning. She checks court material and cases herself. Yajatt Misra checks AI's numbers, logic and conclusions because it can miss business context. Rakshanda Deka warns that AI can dress false information up confidently as fact. These examples show why speed cannot replace professional review.

Checking also keeps human expertise in control. Kazmi says reporting still requires talking to people and fieldwork. Deka warns against outsourcing substantive legal thinking. As AI use grows, employers want workers who can evaluate, correct and refine its output rather than simply pass it on.

Widespread workplace AI can make teams leaner and raise the expected starting level for junior employees. Tasks such as basic research, drafting, summarizing and document sorting may require fewer people or less time. The change does not necessarily eliminate whole job categories, but it can alter how entry-level work is organized and measured.

Dimple Khubchandani gives a marketing example. An agency that once might have hired four junior copywriters could now hire one person to use AI for initial drafts. That worker would spend more time editing, refining tone and shaping strategy. In law, AI can sort large document bundles, while in business it can prepare first drafts and presentations.

This shift may reduce repetitive work while increasing responsibility from day one. Junior employees will need to check AI output, improve its quality and contribute analysis or problem-solving. The article also warns that some young lawyers may use AI as a substitute for their own thinking, creating a risk alongside the productivity gains.

Employers are increasingly seeking skills that complement AI rather than duplicate its routine abilities. The article highlights critical thinking, problem-solving and the ability to check AI-generated work. These skills matter because AI can produce quick drafts and analyses, but workers must decide whether the results are accurate, relevant and useful.

Dimple Khubchandani says employers want young professionals who can think critically, solve problems and verify AI output. In business, Yajatt Misra checks numbers, logic and conclusions before using them. In journalism, Farah Kazmi cross-checks sources and continues reporting from the field. In law, Rakshanda Deka says AI should not replace substantive legal thinking.

The workplace baseline is therefore rising. Employees may be expected to use AI efficiently while adding judgment, context, editing and strategy. Khubchandani says AI is raising the capability expected on day one. People who only handle basic research or drafting may have less advantage as AI takes on more of those tasks.

Generative AI is designed to produce new text, translations and analyses from patterns learned from large amounts of existing material. It responds to a request by generating an answer that fits the wording and context it detects. This makes it useful for summaries, first drafts, translations, data explanations and organized research. The process is about generating plausible output, not guaranteeing truth.

That helps explain the article's examples. AI found starting points for Farah Kazmi's court research, translated between English and Hindi, and helped Yajatt Misra turn business information into updates. Rakshanda Deka used it to arrange documents and identify dates. These tasks benefit from speed and pattern recognition, but they still require a professional to inspect the source and meaning.

The same process can produce confident errors when context is missing or the system generates a likely answer that is not supported. AI misread “Suraj” as “sun,” while lawyers and business workers reported false information and incorrect logic. Human verification is therefore essential, especially in journalism, law and business.