India Inc's AI reality check: Why 92% still struggle to scale AI
Only BFSI (71%) and ITES (80%) show signs of AI maturity — but even they face integration and governance hurdles.Healthcare and retail sectors have >60% AI experimentation, but governance, ethics, and backend integration stall progress.Despite AI being a boardroom buzzword, only 8% of enterprises have realized business-scale AI value.
AI might be the star of corporate town halls and keynote speeches, but behind the curtains of glossy brochures and tech summits lies an inconvenient truth: the vast majority of Indian enterprises are nowhere close to AI maturity. According to the
ETCIO Intelligence
Report AI Playbook – GPUs, Strategies & Readiness Index 2025, a staggering 92% of organizations remain stuck in pilot or exploratory phases. Only a slim 8% have fully implemented AI initiatives.
This discrepancy reveals a telling reality—while boardrooms are bullish about AI's potential, operationalizing it at scale remains an uphill task. For a quarter of surveyed firms, AI remains an abstract concept—a buzzword to explore rather than a tool to deploy.
Pilot paralysis: From proof of concept to proof of value
What's keeping
India
Inc from achieving AI lift-off? At the heart of the issue lies an ROI dilemma. AI pilots, often built around automation or chatbots, fail to deliver tangible business impact.
'Boards demand measurable business value,' the report notes, 'but most AI efforts focus on narrow use cases with limited bottom-line value.' In response, many CIOs are shifting their KPIs from 'proof of concept' to 'proof of value,' with a sharper focus on metrics like Return on Employee (RoE).
'AI has moved beyond proof of concept - it's now about proof of value. With data at its core, the true success metric is ROE: Return on Employee, where enhanced productivity and smarter efficiency reveals AI's real impact,' says Rakesh Bhardwaj, Group Chief Information Officer, Lupin.
The infrastructure conundrum: Legacy systems as a bottleneck
India's digital backbone—comprising legacy ERP, SCADA, MES, and siloed data systems—is not AI-ready. In manufacturing, for instance, only 57% of firms report any form of AI adoption. Even among these, most remain confined to pilot projects, thanks to fragmented operational technology and poor data standardization.
The BFSI sector leads India's AI journey in terms of adoption maturity. Banks and insurers are embedding AI into fraud detection, underwriting, and customer service. But deeper integration is still constrained by legacy systems and high implementation costs.
'AI adoption in BFSI is not just about improving efficiency. It is redefining resilience, security, compliance and customer experience at scale,' says
Sampath Manickam
, Chief Technology Officer, National Stock Exchange of India.
'As we integrate AI-driven solutions, the emphasis must remain on ethical innovation, regulatory compliance and long-term value creation.'
In retail and consumer goods, the maturity is mixed. While digital-native firms and FMCG giants leverage AI for personalization and supply chain visibility, traditional retailers are still stuck on basic digital transformation journeys. Data privacy and ERP integration issues loom large.
In healthcare and pharma, AI use cases are growing—from diagnostics and imaging to drug discovery. However, full-scale adoption is rare, and ethical concerns around bias and explainability are front and center.
ITES players show relative maturity. Roughly 60% have implemented AI for customer service automation, IT ops, or HR analytics. But only 8% have embedded AI into core functions. The rest remain tactical, often boxed into non-core deployments due to legacy constraints and unclear ROI.
Talent deficit vs tool overload
Another major hurdle? – People. Despite the explosion of AI platforms and APIs, there is a severe shortage of skilled professionals—particularly AI engineers, data scientists, and MLOps experts.
'There is a huge shortage of skilled talent because modern education is unable to keep up with the speed of change,' says Priya Dar, CIO,
Valvoline Cummins
. 'We are not experimenting enough and limitations of industry-specific tools lead to customizations that need skills, time, and money. What we are doing is simple—upskilling, leaning on open source, and outsourcing some innovation to smaller partners working on specific use cases.'
Organizations are responding with hybrid strategies: reskilling programs, partnerships with academic institutions, and tapping global talent pools via remote work.
'Our leadership emphasizes innovation, operational excellence, and customer-centricity as core pillars of our growth strategy,' adds Kavita Bijlani, Head of IT & RAD, Bausch + Lomb. 'We are up-skilling and re-skilling our employees by rolling out training programs on AI/ML through virtual platforms. To overcome local shortages, we are tapping into global and regional talent pools.'
Integration complexity: The silent killer
Even when talent and tools are available, most AI projects flounder during integration. ETCIO Intelligence survey revealed that poor post-deployment support and a lack of plug-and-play capabilities remain key friction points—particularly in sectors like BFSI and healthcare, where compliance demands are non-negotiable.
As Anand Sinha, CIO, Birlasoft, explains: 'Organizations address the shortage by upskilling existing staff, recruiting from diverse backgrounds, and using global remote talent… Automation and low-code AI tools are adopted to reduce reliance on specialists.'
Who's Winning and Who's Lagging? A Sectoral Snapshot
ITES (80%) and BFSI (71%) lead due to digital maturity and strong risk/compliance needs.
Healthcare (70%) is gaining traction in diagnostics and drug discovery, but lags in AI governance. Retail (61%) shines in front-end CX but falters on backend integrations.
Manufacturing (57%) struggles with data quality and fragmented tech environments.
From projects to platforms: Global lessons for India Inc
The report emphasizes that successful AI transformation isn't about isolated pilots—it's about 'platformization'. Giants like JPMorgan (COIN platform) and Siemens (AI-augmented digital twins) show the way. Indian firms must follow suit by institutionalizing AI Centers of Excellence, building explainable AI systems, and investing in scalable data infrastructure.
To paraphrase Rucha Nanavati of Mahindra & Mahindra: 'AI has moved from curiosity to boardroom mandate. The challenge now is not in adopting AI—but in delivering on its promise.'
The next 24 months represent a defining window. For India Inc., this is the moment to evolve from pilot purgatory to platform-powered performance. The age of AI has begun—now it's time to make it real.
The AI Playbook | ET CIO
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