Indian IT’s AI Turning Point: Embedded Today, Modular Tomorr…

Indian IT services firms have embedded AI capabilities—AI infused into delivery, engineering, operations, testing, and managed services. Infosys’ Topaz platform, TCS’ MFDM (Machine First Delivery Model), Ignio AI operations, Wipro Lab45 are a few examples, among others. 

Very few firms have dedicated P&L or org structures around AI/Gen AI enterprise application business, and hence, few have translated these capabilities into visible revenue outcomes, said Gaurav Vasu, CEO & founder of UnearthInsight, a cognitive intelligence platform that provides business & financial intelligence on Indian Startups.

TCS and HCLTech are the only firms recognising AI-led revenue. This is a result of past product and platform business exposure, where independent AI solutions are sold, outside the current services deals construct.

Embedded AI is largely invisible, running in the background to reduce effort, improve cycle time, and boost quality. Clients now expect AI/GenAI clauses in deals that promise faster outcomes, lower costs, and predictable delivery.

Tech service providers are embedding AI in their delivery models to create efficiency, cost, and time-to-market impact for their clients, said Yugal Joshi, partner at global research firm Everest Group. “The productivity promises of 20–30% have become common,” he added. 

But here’s the catch: despite the AI push, most real money still comes from traditional levers, large contract wins, multi-year deal renewals, and price-based expansions. Embedded AI keeps Indian IT competitive, but its monetisation potential is yet to unfurl. 

Globally, Modular AI — reusable, auditable AI building blocks — is gaining momentum. But, Indian IT continues to prioritise embedded AI. Deepak Dastrala, CTO at Intellect Design Arena, explained why. 

“The hesitation is less about technology and more about the prevailing business model. Indian IT is optimised for people, projects, and billable hours, not products and IP. Embedded AI fits the traditional services model perfectly.”

Modular AI demands upfront investment, a product mindset, and sometimes saying no to custom client work which is a tough shift for firms built on services scale. Limited access to high-end compute, curated datasets, and research-heavy talent adds friction. 

Jayaprakash Nair, head of AI and analytics at Altimetrik, a digital business services company, pointed this out. “Creating foundational models demands sustained GPU capacity and significant capital expenditure. Fine-tuning existing models remains the practical approach.”

This is also why many internal AI accelerators, copilots, and automation engines never get productised. Indian IT has built powerful internal AI tools, but few have been converted into modular products, or subscription offerings that influence pricing or deal structures.

Embedded AI fits the current business model, and that is both its strength and limitation.

Embedded AI boosts efficiency, but it also creates complexity.

Each project ends up with its own prompts, integrations, guardrails, and custom workflows. Over time, this produces technical debt and governance headaches. When regulations shift or base models change, you are forced to patch 10 to 50 different places. Observability and safety become a game of whack-a-mole.

As Dastrala noted,“A module reused across 10 clients reduces new-build revenue. Firms fear standardisation because it cannibalises services revenue.”

Without measurable metrics, embedded AI rarely becomes a deal-winning differentiator. Vasu highlighted, “Embedded AI is still under the hood. Firms need observability dashboards that show real-time impact on cycle time, effort saved, defects avoided, and SLA improvements.”

This lack of visibility means clients continue awarding mega-deals and renewing large contracts based on transformation scale and pricing, not on embedded AI value, because it isn’t packaged or quantified well.

Internal transformation is another challenge. Embedding AI requires rethinking workforce planning, updating delivery assets, and preparing teams for automation-driven role changes, a cultural shift that moves slower than expected.

Despite its limitations, embedded AI is a logical first step. It integrates directly into existing workflows and delivers early, low-risk wins. As Joshi put it,“Starting with embedded AI helps companies deliver faster outcomes and validate impact. It creates confidence before they scale with modular AI.”

A modular foundation of reusable agents, policy checkers, KYC summarisers and governance modules helps solve long-term problems such as technical debt, versioning, security, and cross-client standardisation. 

Dastrala called this hybrid approach: “Modular at the core, embedded at the edge.” This lets firms scale proven modules across clients, while customising only the last mile.

Embedded AI should evolve into a client-facing differentiator with clear ROI attribution and deal-linked monetisation, added Vasu.

Modular architectures also allow upgrades without tearing apart legacy systems. Nair noted that embedded solutions don’t always win when scaling or managing complexity. “Modular architectures reduce risk by separating functionality into components that can be upgraded independently,” he said.

The path is clear: embedded for early wins, modular for sustainable scale.

Clients won’t choose between embedded and modular AI, they will demand both. Modular AI provides governance, security, and reusability; embedded AI ensures contextual, workflow-specific integration. 

“Once the API buzz fades, clients will ask platform-oriented questions. Providers who remain purely embedded will get trapped in project debt,” warned Dastrala.

Indian IT firms that can show measurable AI impact — reduced cycle time, predictable SLAs, fewer defects — already enjoy higher win ratios in transformation and managed services deals. 

“But, clients still treat it [embedded AI] as table stakes because it isn’t standardised or productised,” noted Vasu.

This is where modularity becomes commercially essential. Firms that link AI impact to pricing, renewals, and value-based deals will move away from volume-led revenue and into outcome-led monetisation.

Nair summed up the opportunity: “Reusable modular blocks on top of proven embedded AI delivery models let clients start small, iterate fast, and embed fully once value is proven.”

The firms that productise their embedded intelligence — and combine it with modular building blocks — will shape how enterprises adopt AI at scale.

Indian IT’s AI strategy is at an inflection point. Embedded AI has delivered efficiency and client trust. But, long-term growth will come from productised, modular AI that compounds across clients, shapes deal structures, and influences pricing.

The winning formula is now clear: start embedded, scale modular, productise everything that delivers repeatable value.

Indian IT must evolve embedded AI from an internal efficiency tool into a visible, governable, monetisable product layer, because in the next wave of global technology spending, clients will expect nothing less.

The post Indian IT’s AI Turning Point: Embedded Today, Modular Tomorrow appeared first on Analytics India Magazine.

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