India Inc. scales GenAI, keeps spend tight, leans on hybrid builds

GenAI in India: Scale climbs, budgets stay tight

Indian enterprises have moved from pilots to production. Nearly half now run multiple Generative AI applications in core workflows. Yet most are still conservative with spend, keeping AI and ML under 20% of IT budgets. The signal is clear: speed to impact matters more than big cheques.

Key data points

• 47% of enterprises run multiple GenAI apps in core operations.
• 95% allocate under one-fifth of IT budgets to AI/ML.
• 76% of leaders expect GenAI to change their businesses in a big way.
• 63% believe they're positioned to use it effectively.
• 9 in 10 prioritize time-to-value when choosing build vs buy.
• Investments target operations, customer service, and marketing.
• ROI now spans efficiency, time gains, revenue upside, edge over competitors, and resilience.
• ~60% co-develop with startups/OEMs; 78% use hybrid (internal + external) models.
• 64% report role shifts in standardized tasks; 59% face AI-ready talent shortages.

Source: EY-CII "The AIdea of India: Outlook 2026." See related context from EY here: EY India - AI insights.

What this means for strategy and operations

This is a performance-led phase. Focus on a short list of high-velocity use cases, wire them into existing processes and data, and measure hard outcomes. Scale what clears the ROI bar; pause what doesn't. Maintain spend discipline, but don't slow execution.

Where to place bets in 2026

• Operations: document automation, SOP copilots, exception handling, QA summarization.
• Customer service: assisted agents, self-service flows, intent routing, knowledge retrieval.
• Marketing: brand-safe content pipelines, dynamic product copy, campaign testing at scale.
• Risk and compliance: policy interpretation, control evidence drafting, audit prep.
• Supply chain: demand notes, PO and invoice checks, vendor response drafting.
• Field/service: work-order notes, troubleshooting guides, parts recommendations.

Build vs buy: a practical hybrid

• Time-to-value: Buy for commodity capabilities; build around proprietary data and workflows.
• Ownership: Keep prompts, orchestration, and data contracts in your control plane.
• Security: Enforce red-teaming, PII handling, and logging at the platform layer.
• Integration: Favor vendors with APIs, event hooks, and SOC2/ISO proof.
• Co-development: Use milestone-based SOWs, outcome SLAs, and shared IP where it matters.

ROI you can defend

• Efficiency: cycle time, AHT, TAT, first-contact resolution, straight-through processing.
• Revenue: conversion rate lift, upsell rate, lead velocity, time-to-launch for campaigns.
• Quality: error rate, rework, CX sentiment, QA pass rate, brand compliance.
• Risk: policy breach reduction, audit findings, hallucination rate, safety incidents.
• Adoption: weekly active users, task coverage, assist acceptance rate.
• Cost: per-task inference cost, infra utilization, vendor spend per outcome.

Operating model: update the org

Work is getting rewired. Routine tasks shift; new mid-office and innovation roles appear. Treat AI as a product: backlog, owners, roadmaps, and SLAs. Put a small enablement core in the center; execution stays with business units.

• Roles to formalize: AI product owner, automation designer, content ops lead, model risk lead, data contracts owner, prompt QA and evaluation.
• Governance: policy library, model registry, evaluation suites, incident playbooks, and change control.

Budgeting for scale (without bloat)

• 70-20-10 portfolio: 70% proven use cases, 20% adjacencies, 10% bets.
• Shared services: central retrieval, vector stores, evaluation harness, observability.
• FinOps for AI: cost caps, autoscaling, caching, model routing, usage alerts.
• Reuse first: prompt libraries, components, patterns, and policy packs.
• Security baseline: data minimization, masked datasets, key management, approval gates.

Talent: close the gap fast

Leaders report a shortage of AI-ready professionals. Up-skill your best operators and analysts; pair them with experienced external specialists to compress time. Make capability-building part of the operating plan, not an afterthought.

• Stand up a role-based academy and internal guilds; track badges tied to job outcomes.
• Run vendor labs on your data; rotate operations leads through AI product squads.
• Codify patterns from pilots into playbooks that new teams can reuse.

If you need a curated path for training by role, explore AI courses by job or fast-start options in popular AI certifications.

A simple 90-day plan

• Weeks 0-2: pick 3 use cases tied to P&L metrics; lock scope, data access, and guardrails.
• Weeks 3-4: design flows, prompts, and evaluation; define success thresholds and fail-fast rules.
• Weeks 5-8: pilot with 20-50 users; measure; fix failure modes; decide go/kill.
• Weeks 9-12: scale to 1-2 business units; add monitoring, cost controls, and training.

Early movers are expanding GenAI across departments while keeping budgets disciplined. The winners will pick use cases that pay back fast, ship relentlessly, and standardize what works. Speed, proof, and reuse - that's the play.