India's IT Spending Surge: Why 70% of Leaders Still Don't See IT as Strategic

Indian enterprises are spending more on IT than ever before. Bain & Company’s 2026 India Enterprise Technology Report reveals that Indian organizations spend 150-200 basis points more on IT as a percentage of revenue than their global counterparts. IT spending is projected to grow 6-8% in 2026 — that’s 200-250 basis points higher than global projections.

By any measure, the money is flowing. Capital expenditure accounts for 50-60% of IT budgets in India, compared to just 20-30% for global peers. The spending is real, and it’s accelerating.

Here’s the problem: only 15% of business leaders see IT as truly strategic.

Another 70% view IT as “good, but not great.” And the gaps they describe are damning: 90% say their data foundations are weak and not fit to scale. Three out of four cite a lack of alignment between business unit and IT goals. PoC fatigue — particularly around AI — is widespread, with low to no-value realization from experiments that never reach production.

The money is there. The value isn’t landing. And if this pattern continues, India risks building the very thing every previous spending surge created: legacy technology that looked modern at the time but lost its edge within three to five years.

Where the Money Is Going

The spending surge isn’t random. Bain’s report identifies four major categories of capital expenditure:

1. Data modernization and AI infusion — 30% of capex, 40%+ of spending change
2. Core application modernization — 25% of capex
3. Cloud adoption and IT infrastructure — 25% of capex
4. Cybersecurity — 20% of capex

These are the right priorities. The problem isn’t what Indian enterprises are buying — it’s how they’re implementing what they buy.

The Three Gaps Killing ROI

Gap 1: Weak Data Foundations

Nine out of ten Indian business leaders say their data foundations are weak. This is the most critical finding in the entire report, because every downstream investment — AI, analytics, automation — depends on data quality.

KPMG’s 2026 Global Tech Report reinforces this from a global perspective: “The foundations for the next wave” must address data before layering AI on top. Indian enterprises are trying to build AI capabilities on top of fragmented, inconsistent, and incomplete data architectures. The result is predictable: AI PoCs that demonstrate capability in controlled environments but fail in production because the data isn’t reliable enough for real-world decisions.

Gap 2: Business-IT Misalignment

Three out of four leaders cite a lack of alignment between business unit and IT goals and KRAs. This is the oldest problem in enterprise technology, and it hasn’t gotten better despite the spending surge.

The symptom: IT delivers projects that meet technical specifications but don’t address business priorities. Business units request tools without understanding implementation constraints. Neither side speaks the other’s language.

The consequence: technology investments that check boxes but don’t move business metrics. When Bain asked leaders what’s working, they cited scalability and operational stability. When asked what needs to improve, they cited alignment, data, and talent — the exact gaps that determine whether IT investments create business value.

Gap 3: PoC Fatigue

The ET Edge Insights 2026 enterprise playbook notes that 47% of Indian enterprises are already running multiple GenAI use cases. That sounds like progress until you pair it with Kearney’s finding that 46% of AI proof-of-concepts globally were scrapped before deployment in 2025.

Indian enterprises are running AI experiments, but most never reach production. The pattern is familiar: a team builds a promising PoC, demonstrates it internally, and then the initiative stalls because of integration complexity, data quality issues, or — most commonly — the absence of a clear path from PoC to production deployment.

This creates PoC fatigue. Leaders who have seen multiple promising experiments go nowhere become skeptical of the next one, even when it’s better justified. The spending continues, but the organizational will to see things through erodes.

The ‘Legacy of Tomorrow’ Risk

Bain’s report draws a direct parallel to previous spending surges — the early 2000s banking-sector modernization in India, and the post-COVID digitalization wave. Both created technology platforms that seemed modern and futuristic but became legacy within 3-5 years.

The risk in 2026 is the same. Indian enterprises are modernizing the entire IT stack simultaneously — data, applications, cloud, security — which is the right thing to do. But without a corresponding change in operating model and approach, the technology they’re buying today will be the technical debt they’re trying to pay down in 2030.

Seventy percent of leaders say the technical debt of legacy systems will take significant time to overcome. They’re right — and the way to avoid adding to that debt is to change how transformation is approached, not just how much is spent on it.

What Needs to Change

Redesign, Don’t Just Automate

BCG’s 2026 analysis of agentic enterprise operations makes the core point: “Most companies have captured their first productivity gains from AI, deploying copilots, bots, and an automation layer, yet few companies have realized impact at scale.” The reason is that organizations are bolting AI onto existing human-centric workflows instead of redesigning the workflows themselves.

For Indian enterprises, this means: before investing in AI tools, map the end-to-end process you want to transform. Identify where AI can replace entire steps (not just assist humans within them). Redesign the process for AI autonomy, with human oversight at decision points — not human execution at every step.

Build Data Foundations First

If 90% of leaders say their data foundations are weak, that’s where the investment should start — not with AI tools layered on top of weak data. This means:

• Data architecture — structured, accessible, lineage-tracked data products
• Data quality — consistent, complete, and reliable enough for production decisions
• Data governance — clear ownership, quality standards, and access controls

This is unglamorous work. It doesn’t generate headlines or impress at conferences. But without it, every AI investment is a gamble.

Measure Business Outcomes, Not Technology Outputs

KPMG’s report identifies a critical issue: “Investment decision making, particularly for new AI tools, has often been based on indirect and hypothetical benefits.” The fix is to tie every IT investment to a specific business outcome before approval — revenue, cost, cycle time, customer satisfaction — and measure that outcome after implementation.

This sounds obvious, but it’s not how most organizations operate. Most measure technology outputs: systems deployed, models trained, PoCs completed. The shift required is to business outcomes: revenue increased, costs reduced, decisions improved.

The India Opportunity

Despite the gaps, the underlying dynamics are positive. Deloitte’s TMT Predictions 2026 projects that India’s data centre capacity will scale from 1.5 GW in 2025 to 10 GW by 2030. The semiconductor market is expected to reach $120 billion by 2030. 93% of Indian enterprises plan to increase AI and analytics spend.

The spending surge is real, and the infrastructure build-out is happening. The opportunity is to ensure that this spending creates lasting value rather than tomorrow’s legacy — and that requires changing the operating model, not just the technology stack.

If your organization is in the middle of this spending surge and struggling with the value gap, the Digital Transformation Roadmap consultation is designed to address exactly this — auditing where you are, identifying the highest-value investments, and building a phased plan that delivers measurable business outcomes, not just technology deployments. For organizations grappling with legacy systems and technical debt, the CTO Technology Advisory provides independent strategic guidance on architecture and investment priorities.