Indian banks move AI into production, but scaling remains a challenge: Zeta

Indian banks have moved beyond experimenting with artificial intelligence (AI), with most institutions now deploying AI in production. However, scaling these deployments across the organisation remains a challenge due to concerns around security, data usability, governance and skills, according to a Zeta survey.

Zeta’s 2026 CXO survey, based on responses from 40 CXOs across 18 banks and NBFCs, found that 70% of chief data officer respondents place their institutions at either selective or scaled AI deployment, including 30% at scaled deployment.

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AI adoption is strongest in areas where outcomes can be reviewed and existing controls can contain risks, including customer service, fraud and risk analytics, document processing and software testing. Its integration into end-to-end workflows and consequential decisions remains at an earlier stage.

The survey highlights a gap between proving that AI works in production and deploying it repeatedly across an institution without rebuilding data, integrations and controls for every new use case.

Retail lending sees biggest AI impact

Retail lending emerged as the area seeing the biggest operational impact from AI, with 88% of COOs surveyed identifying it as a meaningful area of impact, followed by customer service at 75%. CASA and back-office operations were cited by 63% each.

Zeta said roughly four in 10 CDO respondents were unable to identify a high-ROI AI use case within their institution. However, lack of ROI clarity was rated the lowest barrier to adoption, while security and data privacy emerged as the biggest concern.

Data availability isn't the problem

About 80% of CIOs and CTOs surveyed described their data environment as mostly ready for AI at scale, although none considered it fully ready.

The bigger constraints are around making data usable for AI. About 61% cited insufficient labelled or training data, 53% pointed to privacy and consent and 46% cited siloed data.

At the same time, 67% of respondents said their banks were using or piloting AI to enhance or enrich their data.

AI adoption slows when it moves from producing to executing

AI has also gained ground in software engineering. Around 80% of CIOs and CTOs said their organisations use AI for testing and quality assurance, while 60% use it for code generation.

Adoption drops to 40% for code review and 30% each for specifications and documentation, deployment and CI/CD, and incident detection.

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Security and data privacy scored 3.89 out of five as a barrier, compared with 2 out of five for lack of ROI clarity.

Banks are now preparing to use AI in more consequential areas. At least 60% of chief risk officers surveyed identified AI-led credit-risk models, predictive early-warning systems and real-time fraud decisioning as priorities over the next 18-24 months.

Around 60% said responsible AI frameworks were under development, although none reported organisation-wide implementation. Only 20% described model-risk management as very mature.

AI could change work before workforce size

Half of operations leaders surveyed expect AI-driven productivity gains to free up capacity for redeployment into higher-value work, while none expect workforce reductions above 20%.

Banks are currently building AI capabilities faster through specialist hiring and external partners than internal development, which received the lowest capability score in the survey.

“Indian banks have shown that AI creates value in production. The next challenge is making that success repeatable, and the survey is clear about what stands in the way: not conviction, but control,” said Sivaram Kowta, President, Zeta India.

Zeta said the next phase will depend on banks building shared data, infrastructure, governance and control capabilities that allow successful AI deployments to be replicated across the organisation.