Introduction


By Narinder Kumar, the Co-Founder and CEO of TO THE NEW

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.


The Promise: Not Just Hype, But Tangible Potential

India has more than 5 million tech workers, a vibrant startup 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 organized sector and 2.82% in the unorganized 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 personalized 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.


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 organizations back from achieving real, scalable impact. Why?

1. 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.

2. 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.

3. 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 organizations further hamper progress.

4. 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.

1. 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, organizations 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.

2. 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.

3. Centers of Excellence

Building specialized spaces like GenAI Labs or a Centre of Excellence where teams synthesize learnings, iterate models, and showcase proofs of concept. It de-risks innovation and speeds bottom-up GenAI adoption.

4. Psychological Safety

The fear that "AI will replace me" is legitimate. Rather than ignoring it, organizations 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 organizations are showcasing what is possible:

  • Health industry platforms are employing GenAI to respond to patient queries in local languages, reducing call center volumes.
  • Online education businesses are offering personalized content based on student behavior and performance data, driven by GenAI summarization and generation capabilities.
  • 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.


The 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:

  • Public-private cooperation on AI literacy and infrastructure.
  • Localized GenAI creation, rather than mere API consumption.
  • Ethical guardrails that reflect Indian values concerning data and fairness.

GenAI won't change India overnight, but over the next decade, it can potentially revolutionize 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.

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