Developers now save about 30% in manual effort by auto-generating boilerplate code using GenAI. An Indian e-commerce company recently reduced costs by 75% by utilising a GenAI-powered voice bot for customer support.
It’s more than a tech trick; it’s a micro-revolution. India now claims 13.5% of global ChatGPT usage, beating the US, according to the recent Mary Meeker’s AI Trends Report. It is quite evident that Generative AI has arrived, and with it, a wave of change that has the potential to redefine the way we work, learn, heal and innovate.
The Indian government recently announced its plan to invest INR 10,000 Crore on the AI mission to build computing infrastructure (with recent compute capacity exceeding 34,000 GPUs), promote start-ups, train an AI-ready workforce and develop ethical AI. However, as India advances with vernacular GenAI innovation, the gap between promise and proficiency has never been more pronounced.
This is not merely a technology problem; it’s a human one.
Promise: Not Just Hype, But Tangible Potential
India has more than 5 million tech workers, a vibrant start-up ecosystem and digital-first policies accelerating GenAI adoption across industries.
According to a report by EY, GenAI is estimated to impact 38 million jobs in India by 2030, boosting productivity by 2.61% across the organised sector and 2.82% in the unorganised sector. The report also points out that by the coming year, 24% of tasks across industries can be fully automated, freeing up to 8–10 work hours weekly per employee.
In healthcare, AI-powered assistants are enabling faster diagnoses and personalised treatment plans. In financial services, GenAI is improving fraud detection rates and automating compliance processes.
The Indian IT-BPM sector is poised for a 45% productivity gain, with software development expected to see a 60% increase and BPO and consulting experiencing 52% and 47% growth respectively. Even tier-2 start-ups are leveraging GenAI to design pitch decks, write product descriptions and localise marketing without hiring large teams.
These GenAI applications aren’t hypothetical; they’re today’s reality. The advantages are real, including better accuracy, quicker service, improved personalisation and ultimately greater accessibility. The promise has moved decisively beyond hype into real-world value.
The Proficiency Gap
Despite all the hype and exciting GenAI adoption across India, very few Indian businesses have implemented GenAI at scale. A substantial proficiency gap is holding many organisations back from achieving real, scalable impact. Why?
Talent Shortages
The talent required to deploy GenAI is niche and limited. Every year, millions of engineers graduate in India, but only a few are educated in prompt engineering, model tuning or AI ethics. The consequence? Most companies desire to take up GenAI but do not have the human resources to build or maintain it.
Integration Overload
Most companies weren’t designed or built with AI in mind. Siloed data, legacy systems and old workflows make it difficult to install GenAI without deep transformations. And unlike previous IT upgrades, GenAI requires both technical adaptability and cultural preparedness.
Data Privacy & Risk
GenAI lives and thrives on data. Yet in a nation where data sovereignty, consent and responsible AI practices are still taking shape, companies naturally are hesitant, particularly in industries working with health, finance or identity data. Outdated IT systems in organisations further hamper progress.
Human Hesitation
A less talked-about but very present problem is the uncertainty of AI. The lack of measurable ROI, clear objectives and assessable impacts feeds into the anxiety of the managers, creating resistance based on uncertainty and hinders adoption more than any technical challenge.
What will Bridge the Gap?
To shift from hype to habit, GenAI in India requires more than pilot projects and flashy demos. It requires conscious investment in capability, confidence and culture.
Skill-Based Training
Upskilling cannot be a one-time workshop. Developers require hands-on, problem-specific training using real tools, not just in theory. In one study, organisations that trained workers in GenAI over 5+ modules achieved triple adoption. Training isn’t a nice-to-have; it’s the path between fear and fluency.
Recently, Snowflake partnered with the state-backed FutureSkills Prime initiative to train over 100,000 students on AI & Data, clearly showing how the government and private sector are together pushing the pedals on upskilling.
Measuring What Matters
It’s easy to get caught up in what the model can do. But the real question is – does it save time? Does it reduce errors? Does it help people do better work? To overcome this, we need to track what matters.
The focus must shift from “What can GenAI do?” to “What did GenAI do?” Leaders should start tracking GenAI impact via dashboards on time saved, error reduction, cost saving and quality uplift. This visibility builds confidence and helps secure long-term ROI.
Centres of Excellence
Building specialised spaces like GenAI Labs or a Centre of Excellence where teams synthesise learnings, iterate models and showcase proofs of concept. It de-risks innovation and speeds bottom-up GenAI adoption.
Psychological Safety
The fear that “AI will replace me” is legitimate. Rather than ignoring it, organisations must address it. Conversations around data use, ethics and responsible AI use foster trust and transparency in communication, open roadmaps. Participative planning goes a long way.
There have been some emerging success stories budding in the market, as some Indian organisations are showcasing what is possible:
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Health industry platforms are employing GenAI to respond to patient queries in local languages, reducing call centre volumes.
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Online education businesses are offering personalised content based on student behaviour and performance data, driven by GenAI summarisation and generation capabilities.
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SMEs and individual entrepreneurs are applying GenAI tools to develop pitch decks, contracts, ad copy and even Instagram copy without needing to engage with agencies.
Way Forward: An Indian Model for GenAI
India does not need to follow the way GenAI is being taken up in Silicon Valley or Shanghai. We have our own set of challenges — 22 official languages, bandwidth limitations, informal economies — but our own set of strengths too: a problem-solving culture, fast digital penetration and an ever-curious, inquiring workforce.
The challenge is not merely to deploy GenAI. It’s to define it, create Indian-context models, open datasets in vernacular languages and develop innovative and inclusive governance models.
We can achieve this, but it will take:
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Public-private cooperation on AI literacy and infrastructure
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Localised GenAI creation rather than mere API consumption
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Deliberate inclusion of small businesses and non-tech industries
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Ethical guardrails that reflect Indian values concerning data and fairness
GenAI won’t change India overnight, but over the next decade, it can potentially revolutionise the way we learn, serve, build and grow. To unlock this potential, we need more than rollout plans; it takes a human-focused strategy based on training, transparency, trial and error and trust.
At its best, GenAI does not merely create content or code; it creates confidence — the certainty that by adapting the right GenAI strategies, individuals can do more, reach farther and solve problems previously thought to be unsolvable. That is its greatest promise.
The author is Co-Founder & CEO of TO THE NEW. Views are personal.
