NASSCOM

Introduction Organizations are investing heavily in AI, analytics, automation, and digital employee experiences. Yet there is a basic question that often receives less attention: Can the organization trust the employee data behind these systems? An employee may have one job title in one system and another somewhere else. A manager may have changed teams, but an outdated reporting relationship remains active. Skills may not have been updated after years of learning and experience.

Introduction Employee self-service is often treated as a convenience feature. An employee checks leave balance, downloads a payslip, updates personal information, submits a request, or completes a routine HR activity without contacting the HR team. That saves time. But there is a much bigger opportunity. When self-service is designed well, it can change how the entire organization interacts with HR.

Agentic AI has moved beyond being a slide in an enterprise strategy deck. Across Indian enterprises, AI agents are increasingly being explored for software development, customer support, claims processing, finance operations, procurement, IT service management and other business workflows. Yet a significant gap remains between experimenting with AI agents and deploying them reliably in production. That gap is not simply a model-performance problem.

Data engineering teams at most large enterprises spend the majority of their working hours keeping existing pipelines alive rather than building new capability. Dataops automation addresses this by shifting monitoring, testing, schema management, and error recovery from manual intervention to policy-driven systems. The Budget Line That Never Gets Questioned Every enterprise technology budget has a line item for "data infrastructure" that nobody scrutinizes closely enough.

Training gets most of the attention in AI infrastructure conversations. It is easy to understand why. Training a large model requires massive amounts of compute, specialised accelerators, high-speed networking, and substantial infrastructure investment. The numbers are impressive, the hardware is visible, and the scale is easy to associate with AI's rapid progress. But once a model is ready, another infrastructure problem begins. Someone has to run it. That is inference.

For over two decades, the story of Global Capability Centers (GCCs) in India followed a familiar script: set up an offshore unit, cut operational costs, and let it quietly handle back-office work while the "real" innovation happened at headquarters. That script has changed. In 2026, GCCs are no longer support functions sitting on the sidelines — they are becoming the innovation engines of the enterprises they serve, and AI is the biggest reason why.

Introduction A strong manager can transform an employee's experience. They provide context when priorities change, notice when someone needs support, recognize good work, help employees develop, and make everyday decisions easier. The problem begins when the quality of an employee's experience depends almost entirely on which manager they happen to report to. In some teams, goals are clear and feedback is frequent. In others, employees may wait months for meaningful conversations.

A blockchain can record every block, transaction, contract interaction, and state change with cryptographic guarantees. But that does not mean the data is immediately usable by an application. A blockchain explorer needs to retrieve transaction histories in milliseconds. A DeFi analytics platform needs to aggregate swaps, liquidity, and lending activity across millions of records. A wallet needs to identify token transfers associated with an address.

A business can have a great product, strong marketing, and a steady stream of leads but if important calls are missed, opportunities can disappear before anyone gets a chance to respond. This is where an AI Calling Agent can change the way businesses manage everyday communication.

Walk into any modern factory today and you'll notice something that would have seemed like science fiction a decade ago. Sensors talking to dashboards. Robots syncing with inventory systems. AI models predicting maintenance schedules before a machine even shows signs of trouble. This is Industry 4.0, and it's changing how manufacturing works in India and around the world.