Back in 2018, when GenAI had yet to become the biggest buzzword in the tech industry, one of India’s major national banks, Bank of Baroda, launched an Analytics Centre of Excellence with a core focus on AI.

The CoE included a petabyte-scale enterprise data platform capable of handling structured, semi-structured, and unstructured data. It’s underpinned by production-grade data pipelines, integrated machine learning operations, and a governed data-science workbench.

The bank’s emphasis was clear — AI would be key to enhancing customer experience, improving operational efficiency, and data-driven decision making.

Bank of Baroda was not the only one. In fact, it was preceded by the likes of State Bank of India, India’s largest bank, which launched an AI-powered chatbot, SIA, in 2017.

In the same year, HDFC Bank launched an AI chatbot named Electronic Virtual Assistant (EVA), while ICICI Bank followed suit with its chatbot, iPal, in 2018.

The past seven years have been about perfecting these experiments. But now, AI is moving the needle in a bigger way than ever before. Models have improved and have been trained on more relevant datasets; the proliferation of GenAI tools has reduced response time and increased accuracy. It’s also become indispensable in many internal cases and for customer-facing operations.

There’s also a realisation among the top rank and file at big banks that artificial intelligence is no longer discretionary; it is now a core requirement for success in banking and financial services.

As highlighted above, national banks such as SBI and Bank of Baroda have known how essential AI is for some time, but it’s the maturity of AI models and algorithms that have now created challenges. The early movers have already invested in their data foundation, services, and enabling architecture. But updating this for the modern age is another technology and talent-intensive task.

Can Indian banks stand up to this challenge?

The Shift From AI To GenAI

Digital banking has been around since the internet, but these days the push is towards invisible banking — driven by machine learning, deep learning, and embedded APIs that quietly step in when needed.

Now, with large language models and generative AI, banking is getting even smarter and intelligent, bringing a higher degree of personalisation and unlocking new features such as advisories and money management.

It is expected that by 2030, Indian banking operations could see productivity improvements of up to 46% thanks to Generative AI, according to a report by EY India. This isn’t speculative. 74% of financial firms in India have already launched GenAI proof-of-concept projects, and 11% are in production deployments. Whether it’s voice bots, email automation, or risk analytics, banks are starting to scale and capture measurable value.

Analysts Inc42 spoke to noted that, although these are early days, the overall use case repository is already extensive. Some of the key areas where banks are experimenting with GenAI are sales and relationship........

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