The IT/IT-enabled services (ITeS) and business process management (BPM) sectors are undergoing a structural change, with several key trends emerging. Artificial intelligence (AI) is moving from pilots to real-world deployment, increasingly becoming embedded into everyday operations. Further, platform-based models are replacing fragmented systems, making it easier to scale services and integrate data, automation and intelligence. There is also a growing emphasis on governance, security and responsible AI, particularly as enterprises deal with more complex data environments and regulatory expectations.

Rather than replacing roles, AI is changing how work is done, pushing organisations towards more outcome-driven models. However, the path to scale is not without challenges, as legacy infrastructure, talent gaps and evolving security risks continue to slow down adoption. Raghavendra Chinhalli, Global CIO, HGS share his perspectives on the trends, challenges and priorities shaping the next phase of the IT/ITeS and BPM landscape…

What are the three key technology trends shaping the IT/ITeS sector?

The IT/BPM sector is at a defining moment as AI becomes central to enterprise operations. AI is beginning to move from experimentation to real-world adoption. Across enterprises, conversations are rapidly evolving from whether to deploy generative AI (GenAI) to how quickly organisations can operationalise agentic AI at scale, especially in service delivery and customer experience. The shift in focus from “AI as potential” to “AI that delivers measurable outcomes” is beginning to come to fruition. On the client side, we are seeing efforts to embed intelligence into daily workflows, combining AI capabilities with human judgment to provide intelligent experiences for customers and employees.

Further, there is a strong push toward human-AI collaboration. Automation is handling repetitive work, while people focus on decision-making, empathy and oversight. This balance helps enterprises deliver simpler, more consistent experiences while continuously improving operations. Furthermore, platform-based, cloud-native models are becoming the norm. Organisations are consolidating fragmented tools into unified platforms that integrate applications, data, AI and automation. These platforms make it easier to scale AI, build industry-specific solutions and support outcome-based pricing models.

How is AI transforming the way enterprises in the IT/ITeS sector operate? How are technologies such as AI, automation and platform-based systems reshaping operating models and workforce dynamics?

AI is no longer an add-on; it is becoming a part of everyday workflows. In the IT/BPM sector, AI is being embedded across customer interactions, training, analytics, quality assurance and decision support, making operations more proactive and resilient. Operating models are evolving in three clear ways:

  • They are becoming more connected, with the integration of experiences, platforms and operations.
  • More outcome-driven, with clear key performance indicators (KPIs) defined before solutions are scaled.
  • Evolving into a hybrid model, with AI augmenting people rather than replacing them.

For the workforce, AI is reshaping roles at scale. For example, AI-powered assistants support HR, IT and administrative functions by reducing onboarding time, improving training through real-life scenarios and freeing employees from repetitive tasks, allowing them to focus on better decision-making and higher-value work.

How are you strengthening cybersecurity frameworks while adopting new technologies?

As AI and cloud platforms scale, cybersecurity must be built into the transformation from the start. At HGS, security and governance are embedded into how we deliver intelligent experiences for both our clients and employees. Before deployment, we clearly define integration points, data flows, access controls and fallback mechanisms.

This ensures new technologies improve reliability without introducing risk, especially in global and distributed delivery environments. We follow a secure-by-design and zero-trust approach, supported by continuous monitoring, automated threat detection and strong identity management. We also focus on responsible AI governance, ensuring transparency, data privacy and ethical use.

What execution challenges or structural constraints have you encountered while adopting advanced technologies at scale?

One of the biggest challenges is moving from successful pilots to full-scale deployment. Many AI initiatives fail not because the technology does not work, but because outcomes, integration and governance are not aligned at the initial stage.

Another constraint is legacy infrastructure. Embedding intelligence often requires rethinking workflows, data flows and decision-making structures. It is not only about automating existing tasks but also about creating an intelligent experience with guaranteed outcomes.

This brings us to another crucial challenge – workforce readiness. As AI takes centre stage in enterprise transformation to meet changing market needs, it will require an AI-savvy workforce across levels to enable faster adoption, safer use and responsible decision-making.

Further, with human-AI collaboration gaining importance, an AI-ready workforce will be critical to driving productivity, resilience and service excellence. Organisations that upskill early will outpace their peers in innovation and customer experience over time.

What major technology shifts do you anticipate in the IT/ITeS domain in the coming years? How will they redefine your organisation’s systems?

The next phase in the IT/BPM space will be driven by enterprise-wide adoption of intelligent AI, with a stronger focus on responsible, secure and industry-specific solutions. GenAI and agentic AI will increasingly be built into core systems, powering training, conversational AI, decision support and end-to-end automation. Human and AI collaboration will become a default model where work will be designed around humans training intelligent AI models and directing AI agents. Productivity gains will stem from augmentation, judgment and creativity rather than automation alone.

Experience-centric technology will gain priority, with investments being increasingly measured by experience outcomes for customers, employees and ecosystems, rather than just efficiency or cost reduction. Customisation and personalisation of industry-specific solutions will become common practice. We are already seeing the emergence of an ecosystem of co-creation. As AI becomes embedded into core enterprise operations, such an ecosystem will become increasingly prominent, leading to deeper partnerships between start-ups, big tech firms and clients. Moreover, technology will evolve toward modular, interoperable architectures, enabling faster innovation, real‑time responsiveness and AI execution closer to where data is generated through edge computing.

Cybersecurity will become increasingly predictive and AI-driven as threats and attack landscapes expand. This will involve AI-powered threat anticipation, automated responses and continuous trust models. In addition, ethics, explainability and compliance will be built into systems by design, ensuring that AI and emerging technologies scale up responsibly.