The impact of artificial intelligence (AI) on the IT services industry is now visible in hiring trends, deal structures and technology investments. The industry is entering a phase of disruption, with layoffs, hiring slowdowns and pricing pressures. AI is increasingly automating routine and repetitive tasks, particularly at entry and mid-skill levels, reducing the need for large volumes of traditional manpower. Meanwhile, enterprises are demanding greater output with fewer resources, pushing IT firms to improve productivity through automation rather than workforce expansion.
However, IT sector is not witnessing a reduction in workload, but rather a shift in how that work is structured and delivered. Enterprises continue to depend on core systems such as core banking platforms, enterprise resource planning systems, customer relationship management tools and other enterprise applications, which remain critical to operations. Instead of being replaced, these systems are being enhanced and interconnected to support evolving business needs. AI is central to this transition. It is being embedded across enterprise systems to enable automation, improve data processing and support faster decision-making. As a result, the role of IT services is shifting from building standalone applications to upgrading existing systems and integrating intelligence into complex, interconnected environments.
AI is also influencing how other technologies are being adopted. Robotic process automation (RPA) is becoming more intelligent, moving beyond rule-based automation to handle more complex tasks. Edge computing is enabling AI to process data in real time, closer to where it is generated. Furthermore, data platforms are being redesigned to support AI workloads. AI is not just one technology among many, it is the foundational layer that is shaping how the broader technology stack evolves.
A look at how AI convergence is defining the next phase of IT services, where multiple technologies are integrated to deliver more intelligent and efficient outcomes…
RPA
RPA has been a part of enterprise IT systems for several years, but its role is now changing as it becomes more closely linked with AI. Earlier, RPA worked on predefined rules and structured data, which made it useful for automating repetitive back-end tasks. Traditional RPA could only follow fixed instructions, but when combined with AI, it can now handle unstructured data, deal with exceptions and adjust to evolving scenarios. This allows RPA to move beyond basic task automation and work across different enterprise systems. In areas such as IT operations, application management and enterprise workflows, RPA is being used to trigger actions, manage processes and keep systems running smoothly.
The global RPA market is expected to reach $12.22 billion by 2029, with the technology sector itself accounting for around 31 per cent of adoption. Automation is being widely used within IT systems not only to improve efficiency but also to handle increasing system complexity. As enterprises expand their digital operations, the number of processes, applications and data flows continues to grow, creating demand for systems that can work across platforms without constant manual effort.
Edge computing
Edge computing is becoming an increasingly important part of enterprise IT as companies deal with growing data volumes and the need for faster decisions. Instead of sending all data to central cloud systems, edge allows data to be processed closer to where it is generated, such as on devices, at branch locations, or within local networks. This helps reduce delays and improve response time, which is important for many business applications.
As more devices get connected, especially with the rise of internet of things, the amount of data generated at the edge is increasing rapidly. Managing this data is not simple. This is where integration platforms play a role. They help collect and process data from different sources, and when combined with analytics and AI, they allow enterprises to extract useful insights in real time. They are pushing organisations towards more data-driven and responsive systems. For example, the collaboration between Infosys and NVIDIA shows how edge is being combined with AI to enable real-time analytics, video processing and faster decision-making in areas like IT operations and customer-facing systems. The global edge computing market is expected to reach $155.9 billion by 2030.
Cloud
Cloud has emerged as a basic requirement for the IT sector as systems become more complex and interconnected. IT companies manage large enterprise environments that involve multiple applications, continuous data flow and various locations. This makes it difficult to rely only on traditional infrastructure. Cloud allows these systems to be more flexible, easier to scale and better connected across different environments.
Further, AI workloads require large computing capacity, access to data and the ability to scale quickly. Cloud platforms support this by providing the infrastructure needed to train, deploy and manage AI models.
Software and technology companies are not relying on a single approach; instead, they are adopting hybrid models. Around 20 per cent of usage is through private cloud, 21 per cent through public cloud and a majority 59 per cent through hybrid cloud setups. This shows that hybrid models are becoming the preferred approach, as they allow companies to balance control, cost and performance while managing complex IT systems.
Advanced data analytics
Advanced data analytics and engineering are gaining importance within enterprise IT environments as systems become more connected and data-heavy. IT environments are becoming more distributed, with data generated not only from central systems, but also from cloud platforms and edge locations. This makes it more difficult to manage data consistently across the entire system, especially when real-time processing and AI-based decision-making are involved. As a result, there is a growing need to integrate data from core systems, cloud environments and edge infrastructure into a unified setup.
To address this, organisations are building stronger data engineering capabilities within their IT systems. This includes creating data pipelines that connect applications, integrating data across platforms and setting up environments such as lakehouses that can handle both structured and unstructured data. These systems ensure that data can move smoothly across enterprise IT infrastructure and can be used reliably for analytics and AI-driven applications. This shift makes data engineering a continuous requirement within IT systems. As new applications are added and systems evolve, data pipelines and platforms need to be updated and maintained. This ensures that enterprise IT environments remain connected and capable of supporting increasingly data-driven and AI-enabled operations.
Security concerns
As organisations adopt cloud, edge and automation technologies, the attack surface has expanded significantly. This is reflected in rising investments, with information security spending in India projected to reach $3.4 billion in 2026, growing by 11.7 per cent over the previous year. At the global level, the cybersecurity market is expected to reach $351.92 billion by 2030, at a CAGR of 9.1 per cent from 2025 to 2030, indicating sustained enterprise demand for security solutions.
At the same time, the nature of threats is changing. AI is being used to strengthen security systems and enable more sophisticated cyberattacks. Enterprises need to continuously upgrade their security frameworks while also integrating AI into their defence mechanisms. Security is no longer a one-time implementation but an ongoing process that requires constant monitoring, updating and response.
However, this shift also brings its own challenges. Rising cybersecurity investments are putting pressure on enterprise budgets, particularly as organisations are already spending heavily on AI, cloud and data infrastructure. At the same time, there is an increasing shortage of skilled cybersecurity professionals, making it difficult for companies to build and manage in-house capabilities. This has led to increased reliance on IT services providers for managed security services and continuous monitoring.
For IT services firms, this creates both opportunity and responsibility. Security should be embedded across all systems, from application development to data platforms and edge infrastructure. Managing hybrid environments, where data flows across on-premise, cloud and edge systems, further increases the complexity of securing enterprise IT.
In sum
The idea that AI will replace the IT services industry does not seem to hold up when looked at closely. Demand for technology is not diminishing. Enterprises will continue to rely on core systems, long-term contracts and ongoing digital transformation initiatives. However, it is equally clear that the industry cannot continue in its current form without adapting.
This shift changes the nature of work within the IT sector. Routine and repetitive tasks are likely to reduce, but new areas of demand are emerging in integration, data management, AI deployment, governance and system optimisation.
The overall picture is not one of decline, but of transition. The volume of work remains, but the skills required and the way services are delivered are changing. Companies that align with this shift, particularly by building capabilities around AI and its supporting technologies, are likely to remain competitive. Those that fail to adapt may find it difficult to keep pace.
Harshita Kalra
