AI, Valuation Reset, and the New Economics Of IT Services

Why the Inflection Point Is Now and What CXOs and Finance Leaders Must Do Next

Context: Markets Are Repricing IT, Not Rejecting It.

Over the last several months, global technology markets have undergone a sharp re-rating. Indian IT stocks have not been spared. What we are witnessing is not a collapse in demand, but a reassessment of how technology revenues will be generated and how value will be delivered in an AI-first world.

Globally, investors are questioning whether traditional IT services models - heavily dependent on linear headcount growth - can sustain margins when AI demonstrably automates substantial portions of software development, testing, support, and operations. This has triggered valuation compression across technology services, even as enterprise technology spending remains resilient.

In India, the impact is equally amplified. Our IT sector, deeply integrated into global delivery chains, and capital markets are forward-looking. The message from markets is clear: future value will accrue to firms that decouple revenue growth from people growth.

Panic vs Reality: What AI Summits are Saying and What Leaders must Acknowledge

There is a visible tension today between market anxiety and policy optimism.

On one hand, AI summits across India and globally have consistently emphasized:

• Responsible deployment
• Governance and safety
• Gradual, staged adoption
• Human-in-the-loop models
• File / document management

These narratives are correct - at a systemic level.

However, for IT service providers, the reality is more immediate. Clients are not waiting for regulatory maturity. The commercial inflection point has already arrived.

Enterprises are actively asking service providers to:

• Use AI and automation
• Reduce cost per transaction, per ticket, per release
• Shift away from effort-based pricing
• Commit to outcomes, not headcount

This is not theoretical. It is happening now, in contract renewals, RFPs, and pricing negotiations.

The right conclusion is not panic but urgent execution.

Enterprise Software Evolves Under AI, it doesn't Disappear

While AI is often framed as a force that will "eat" enterprise software, evidence suggests a more nuanced reality. AI compresses the cost of building interfaces and automating routine workflows, but it does not replace systems of record that anchor core business processes, data integrity, compliance, and auditability. Instead, AI is becoming an intelligence layer that augments these platforms, making them more adaptive and efficient rather than obsolete. As a result, value is shifting away from undifferentiated, UI-centric tools toward deeply integrated systems with strong domain context. This reinforces a critical point for IT services firms: AI rewards integration depth and governance capability, not superficial automation.

The Client mandate has changed: "Automate First, Then Price"

Across industries, enterprise buyers are issuing a consistent directive:

"Use AI and automation to deliver the same or better outcomes at a lower cost."

This mandate is reshaping delivery expectations across core IT service lines:

• Testing & QA: AI-driven test creation, regression automation, defect triage
• Support & Maintenance: Code remediation, enhancement, documentation, KT automation
• Infrastructure Services: Autonomous L1/L2 monitoring, incident handling, self-healing systems
• Operations & BPO: Exception handling, reconciliations, validations via agentic workflows

For clients, this is cost transformation.

For service providers, it is a business model reset.

Outcome-Based Delivery Is Becoming the Default

The shift underway is not incremental. It is structural.

Traditional time-and-material models are giving way to:

• Outcome-based contracts
• SLA-linked pricing
• Gain-share and productivity-linked commercial terms

In practical terms:

• Clients will pay for uptime, cycle time, accuracy, throughput, and business impact
• Providers will be responsible for how outcomes are delivered: human, AI, or hybrid

For finance leaders, this requires a fundamental change in how revenue predictability, margin, and risk are modelled.

People Cost Compression Is Structural - Not Cyclical

One unavoidable consequence of AI-led delivery is structural compression of people costs.

This does not mean mass elimination of talent but it does mean:

• Slower hiring across traditional roles
• Redeployment and reskilling as the primary workforce strategy
• Elimination of purely repetitive, non-value-adding work

Mid-size Indian IT firms must recognize this early. Those that delay will face margin pressure driven externally by clients, not internal efficiency programs.

While most IT firms are slowing hiring, a leading IT major is taking a differentiated approach by committing to significant fresher hiring and aggressively training them in newer technologies to rebuild a future-ready, lower-cost talent pyramid. For CFOs, this represents a deliberate trade-off: higher near-term training investment in exchange for long-term cost control, reduced wage inflation, and greater delivery flexibility.

Both positions prevail in current situation, so make the apt decision for your business context.

Asset-Light Becomes the New Valuation Benchmark

Capital markets are increasingly rewarding firms that demonstrate:

• Reusable automation assets
• AI accelerators and platforms
• Proprietary IP embedded in delivery

And penalizing those that remain:

• Labor-intensive
• Undifferentiated
• Dependent on scale hiring for growth

The implication is clear: valuation multiples will increasingly correlate with automation depth and IP leverage, not workforce size.

Why Anthropic and Claude Matter to IT Services

Anthropic has emerged as a critical force shaping enterprise expectations.

Its Claude models, particularly recent enterprise-grade versions, significantly raise the bar in:

• Coding and refactoring
• Test generation and review
• Long-context reasoning across complex legacy systems
• Agentic task orchestration

For IT services, this accelerates:

• Automation of L2/L3 application support
• Reduction in junior developer and tester effort
• Faster onboarding and knowledge transfer

How partnerships are emerging :

• ❯ Infosys: Build industry-specific AI agents with governance focus; integrates Claude with its AI platform to transform core enterprise operations.
• ❯ Cognizant: Scale enterprise adoption of Claude internally and externally, embedding AI deeply into delivery processes.
• ❯ Accenture: Train and deploy Claude across a large consulting/support base to drive enterprise AI transformation with measurable ROI.
• ❯ IBM: Embed Claude into enterprise software with enhanced security/governance.
• ❯ Salesforce: Bring Claude into customer workflows and regulated industry automation.
• ❯ Cloud & Platform Partners (Microsoft/NVIDIA/Databricks): Scale Claude's availability and performance on major clouds and hybrid environments, enabling wider enterprise usage and performance optimisation.

From a CFO lens, Claude-class models enable a shift from fixed people cost to variable platform cost - but only for firms that redesign delivery around them.

Moltbook and the Rise of Agent-to-Agent Ecosystems

Moltbook represents an early but important signal. It is a social platform designed primarily for AI agents to interact with other AI agents, sharing tools, workflows, and strategies. While experimental today, it highlights a future where:

• Automation patterns spread rapidly
• Competitive differentiation erodes faster
• Governance, IP protection, and auditability become critical

For CXOs, this introduces new dimensions of risk:

• Data leakage
• Loss of proprietary delivery methods
• Compliance challenges in autonomous systems

Ignoring agent ecosystems would be a strategic blind spot.

What Changes for CFOs and CXOs - Immediately

This transition places finance leadership at the center of AI strategy.

Key shifts required now:

1. Redesign the P&L lens
   Move from cost-per-FTE to cost-per-outcome. Track AI enablement separately from people cost.
2. Re-architect pricing models
   Build standardized outcome-based pricing frameworks before clients force them.
3. Treat reskilling as capital allocation
   Fund reskilling programs with clear deployment metrics and ROI milestones.
4. Put AI governance under finance oversight
   Agentic systems require auditability, risk controls, and commercial accountability.
5. Reset the board and investor narrative
   Replace broad “AI transformation” claims with phased execution plans, KPIs, and margin pathways.

Conclusion: The Inflection Point Is Here

AI is not eliminating IT services. It is rewriting the economics of how they are delivered and valued. Markets have moved first. Clients are following fast.

The question for Indian mid-size IT companies is not whether this transition will happen but whether it will be led proactively or imposed externally.

Those who act early will emerge:

• Leaner
• Asset-light
• Margin-resilient
• Valuation-relevant

Those who delay will discover that pricing power, not technology, is what disappears first.

The inflection point is no longer ahead of us. It is already here.

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