India enters 2026 with AI moving from experimentation to selective scale. Much of the current artificial intelligence development in India is broad, but depth varies by industry and function. CIOs face a familiar mandate: show measurable value, stabilise governance, and integrate AI into systems that were never designed for it. The opportunity is real; so are the constraints.
The EY–CII 2025 report found that 47% of Indian enterprises now operate multiple generative-AI use cases in production, with another 23% still in pilots—evidence of a shift from experimentation to operationalisation.
Budget patterns, however, tell a different story. 95% of organisations allocate less than 20% of their IT spend to AI, and only 4% exceed that threshold. Enterprises want proof before expanding commitments—an expectation CIOs will continue to navigate in 2026.
IBM’s AI Outlook for 2025 echoes this tension: Indian organisations expect AI to improve productivity, efficiency, innovation, and revenue, but fragmented systems and uneven workflow maturity constrain progress.
It’s clear that adoption is rising and ambition is high, but durable scale is slowed by four structural constraints—fragmented data, legacy integration gaps, governance shortfalls, and tight ROI timelines. For CIOs, 2026 won’t hinge on acquiring more AI tools; it will hinge on preparing the environment required to use them effectively.
AI Adoption Challenges in India’s Top 3 AI-Adopting Industries
The industries with the most visible AI progress—BFSI, IT/ITeS, and Retail—also face the most friction. Their AI adoption challenges are structural: inconsistent data, regulatory pressure, integration debt, and workflow fragmentation. These constraints will shape what CIOs can realistically scale in 2026.
BFSI: Data Fragmentation and Compliance Friction
BFSI leads India’s AI adoption curve but is slowed by fragmented customer data spread across core banking systems, digital channels, and Know Your Customer (KYC) repositories. RBI’s heightened focus on fraud, KYC, and model risk stretches validation cycles and forces controlled experimentation. BFSI’s challenge for 2026 isn’t capability; it’s data consistency and regulatory alignment.
IT / ITeS: Workflow Incoherence and Client Governance
Coding copilots and delivery assistants became mainstream in 2025, but productivity gains remain uneven. The root cause is workflow incoherence: outputs don’t flow smoothly across QA, security, or client environments. Client restrictions on GenAI add further constraints. In 2026, progress will depend on workflow redesign, not expanding toolkits.
Retail & Ecommerce: Catalogue Disorder and Multilingual CX Pressure
Retail’s AI gains—catalogue enrichment, CX automation, personalised outreach—remain limited by inconsistent product data, multilingual support demands, and variable supply-chain signals. Adobe’s 2025 India snapshot records efficiency gains, but mostly in content and CX, not end-to-end operations. In 2026, AI returns will hinge on catalogue hygiene and disciplined integration.
How Indian Enterprises Can Overcome These AI Barriers in 2026
The organisations that progress in 2026 won’t be the ones with the most AI pilots—they’ll be the ones that fix the foundations: cleaner data, tighter governance, clearer workflows, and pragmatic integration. How companies use AI will not be glamorous, but it will be decisive.
In 2026, the fastest gains for AI in India will come from fixing foundations, not expanding use cases.
Fix Fragmented Data With Thin-Slice Integration, Not Enterprise Overhauls
To scale AI, enterprises don’t need unified data estates—they need thin, reliable data slices for the workflows that matter.
- In BFSI: consistent layers for onboarding, KYC files, or dispute documentation.
- In Retail: standardising the 10–20% of product attributes that drive most catalogue errors.
- In IT/ITeS: stable metadata for code, QA, and documentation pipelines.
The principle is simple: Fix the input surface for each workflow; don’t attempt enterprise harmonisation.
This directly addresses BFSI’s fragmentation, IT services’ workflow variation, and Retail’s catalogue disorder.
Shorten Governance Cycles With a Lightweight AI Review Board
Compliance drag in BFSI and client oversight in IT/ITeS both slow AI deployment.
A small AI review board—IT, data, security, legal/compliance, and the business owner—reduces ambiguity by defining:
- acceptable data flows
- validation standards
- risk thresholds
- escalation paths
This brings governance forward in the process, accelerating approvals without compromising control.
Use Modular AI + SI/MSP Support to Reduce Integration Debt
Most enterprises don’t have the engineering capacity to rewire legacy systems. A modular path—starting with copilots, then automating specific workflows, and integrating only where it counts — keeps the work manageable.
- BFSI can overlay modular tools on rigid cores.
- IT/ITeS can standardise deployment patterns across delivery units.
- Retail can stabilise catalogue operations before layering automation.
India’s system integrator/managed services provider (SI/MSP) ecosystem makes this approach commercially rational.
Target High-Volume, High-Friction Workflows First
These are the workflows where AI reliably pays for itself:
- BFSI: KYC prep, claims summarisation, customer query documentation
- IT/ITeS: coding support, QA reviews, knowledge retrieval
- Retail: product data cleanup, returns classification, CX triage
This is where Adobe’s 2025 India study found consistent efficiency gains.
Prove ROI With Metrics That Leadership Actually Cares About
CIOs will need to anchor AI performance in operational metrics that matter to boards: faster cycle times, lower cost-to-serve, higher accuracy, better use of employee hours, and measurable impact on customer resolution and revenue. These are the levers that justify scaling AI beyond early workflows.
2026 Will Be the Year AI Either Scales or Stalls
If 2025 was the year Indian enterprises proved they could deploy AI, 2026 will be the year they prove they can sustain it. Adoption is broad, but scale will depend on disciplined foundations, not experimentation.
The sectors with the most live use cases—BFSI, IT/ITeS, and Retail—show the same pattern: AI isn’t failing on capability; it’s stalling on readiness. CIOs who resolve data, governance, and workflow constraints will convert AI from isolated wins into enterprise gains. Those who layer automation onto unstable foundations will see returns plateau.
The mandate is clear: focus on high-volume workflows, fix the data that matters, govern before scaling, and measure outcomes, not activity.
AI is becoming a standard enterprise capability—powerful, but only as strong as the environment around it.
