Key Takeaways
- Only 8% of Indian enterprises have deployed AI at a business scale.
- BFSI and ITES sectors show higher maturity, but integration and legacy systems hinder progress.
- Talent shortages and governance concerns slow adoption in healthcare, retail, and manufacturing.
While AI may dominate corporate conversations, the ETCIO Intelligence Report 2025 reveals a contrasting reality: most Indian enterprises are yet to make it work at scale. A staggering 92% of companies, for instance, are still stuck in early-stage pilots or testing phases.
While enthusiasm is high at the top, actual implementation faces several hurdles – from unclear return on investment to fragmented infrastructure.
From Pilot Projects to Business Value
Many firms begin with AI pilots – typically chatbots or automation tools – but struggle to move beyond proof of concept. The challenge lies in showing measurable value.
Instead of chasing flashy tools, CIOs are now focusing on proof of value, with metrics like Return on Employee (RoE) gaining attention. This shift reflects a need to align AI with actual productivity gains rather than abstract innovation.
A significant hurdle in achieving this business value, however, stems from the underlying infrastructure.
Infrastructure Is Still Playing Catch-Up
Legacy systems continue to act as roadblocks. Manufacturing firms, for instance, rely heavily on outdated ERP and operational tech, making AI integration complex. Even where pilots exist, scaling them is tough due to siloed data and poor system compatibility.
The BFSI sector (banking and insurance) is ahead in AI maturity. Use cases like fraud detection and customer service automation are common. However, deeper integration is limited by older systems and rising costs.
In healthcare, AI is emerging in areas like diagnostics and imaging, but ethical concerns and governance gaps prevent full-scale rollout.
Similarly, in retail, a split exists – large FMCG firms are investing, but traditional players remain stuck in earlier stages of digital transformation.
Skills Shortage Slows Progress
Despite a wide range of AI tools and platforms, skilled professionals remain in short supply. The demand for data scientists, AI engineers, and machine learning experts far outpaces availability.
Organisations are addressing this gap with a mix of strategies:
- Upskilling internal teams
- Collaborating with academic institutions
- Hiring remote and international talent
Some firms are also turning to low-code AI tools to reduce reliance on deep technical expertise.
Integration: The Silent Barrier
Even when talent and tools are available, many AI initiatives stall during integration. Sectors with strict compliance requirements, like finance and healthcare, find post-deployment support lacking.
Without reliable “plug-and-play” solutions, companies face increased technical friction, which undermines adoption and delays business impact.
Sector Snapshot: Who’s Ahead?
A look at the AI maturity index across sectors further highlights these disparities:
- ITES (80%) and BFSI (71%) lead the AI maturity index.
- Healthcare (70%) shows progress in diagnostics but lags in governance.
- Retail (61%) is active in customer-facing use cases but struggles with backend systems.
- Manufacturing (57%) remains limited by outdated infrastructure and data silos.
Moving Beyond One-Off Projects
AI adoption in many Indian enterprises remains trapped in early-stage pilots. These isolated experiments might test feasibility, but they rarely lead to sustained business value. Therefore the next step is clear: moving from short-term projects to long-term platforms.
Global firms are already doing this. JPMorgan’s COIN platform automates legal document reviews at scale. Siemens uses AI-powered digital twins to optimise factory operations. These examples highlight how organisations are embedding AI into their core systems—not treating it as an add-on.
For Indian enterprises to follow suit, three changes are essential:
- Institutionalise AI: Set up Centres of Excellence to drive strategy, training, and governance.
- Build infrastructure: Invest in unified data systems that support AI at scale.
- Align teams: Ensure business and tech leaders share goals and decision rights.
Scaling AI isn’t just about buying tools. It’s about committing to long-term capability, trust, and measurable outcomes.
