The Global Systems Integrators Driving IT Transformation

Large enterprises today operate in an increasingly complex technological environment.

The rapid adoption of AI, the shift to multi-cloud and hybrid cloud infrastructures and the constant need to manage vast amounts of data while defending against cybersecurity threats have created significant operational challenges. 

Internal IT departments often struggle to manage the sheer scale and diversity of these systems, which come from numerous different vendors and require specialised expertise.  

This is the environment where Global Systems Integrators (GSIs) have become essential.

GSIs are large technology consulting firms that specialise in designing, implementing and managing complex IT systems for major enterprises and governments.

Their core function is to bring together disparate hardware, software and services into a cohesive, functional whole.

Firms like Accenture, IBM, Tata Consultancy Services (TCS), Capgemini and Deloitte dominate this space, leveraging their global reach and deep technical knowledge to execute large-scale transformation projects.

The role of the GSI has evolved significantly.

These firms are no longer just technical implementers but have become strategic partners, guiding clients through the entire process of digital transformation.

The market for these services is substantial, with some estimates placing its value at over US$400bn in 2024 and projecting growth to more than US$2.2tn by 2034.

AI’s impact on the GSI business model

AI is causing a significant change in the GSI business model, with the technology driving a move from technical implementation to strategic enablement.

This has begun to disrupt the industry’s established commercial practices.

For decades, GSI revenue was based on time and materials, where billing was a function of hours worked.

With Gen AI tools capable of delivering considerable productivity improvements – reducing a coding task from eight hours to two, for example – clients are questioning the logic of paying for effort rather than results. 

This is accelerating a shift towards outcome-based pricing.

TCS chief executive K. Krithivasan acknowledges that clients are exploring different models.

“There are some where we do based on the outcome, some customers that expect that this is better to do it on T&M (time and materials),” he said in a recent interview with The Economic Times.

“Because as it is evolving, they also want to see how they are able to benefit from the results… So, we are seeing both options here.”  

How leading firms are adapting

The industry’s largest firms, including Accenture, IBM and Capgemini, are responding by integrating AI into their own operations, using their internal transformations as a proof point for clients.

Accenture has stated its goal is to be its clients’ “reinvention partner of choice”.

CEO Julie Sweet is leading this strategy with a US$3bn investment in AI and a programme to train 600,000 employees in AI skills.

Her objective is to build “the most AI-enabled, client-focused professional services company in the world”.  

IBM, led by CEO Arvind Krishna, has focused the company’s strategy on hybrid cloud and AI.

Krishna advocates for using smaller, domain-specific AI models, arguing they can provide a faster return on investment than larger, generalist models.

A key part of this strategy is the ‘client zero’ philosophy, where IBM uses its own technology, such as the watsonx AI platform, to improve its internal processes.

“We are transforming our enterprise operations using technology and embedding AI across more than 70 workflows, leveraging our own IBM software solutions,” he said.

European-based Capgemini is concentrating on what it terms “Intelligent Industry,” which involves applying data and AI to core industrial and business processes.

“There’s also a common misconception: increased productivity doesn't automatically translate to cost savings,” notes CEO Aiman Ezzat.

“For instance, a 30% productivity gain among lower-cost roles like testers doesn't have the same financial impact as a similar gain in higher-cost roles such as project managers or functional analysts. That nuance often gets overlooked.”  

Core services: Cloud and data

While AI is a primary focus, its effectiveness depends on the foundational areas of cloud computing and data management.

Most large organisations now operate in a multi-cloud environment, with research showing 89% of enterprises have adopted a multi-cloud strategy to avoid dependency on a single provider and improve resilience.

This approach, however, increases management and security complexity, reinforcing the need for GSIs to orchestrate these varied environments.

Data quality is even more fundamental.

The performance of any AI system is contingent on the data it is trained on, yet enterprise data is frequently disorganised and inconsistent.

This has created a significant service area for GSIs in preparing data for AI applications.

This work involves cleaning, standardising and structuring data so that it can be used effectively. 

According to research, approximately 85% of major technology firms now use specialist providers, including GSIs, for these data preparation services.

Scott Holcomb, a Principal at Deloitte, states that the objective is to “empower executives with a comprehensive view of their business” so they can “trust their most important asset: data”.

The integration of these technologies – AI, multi-cloud and data – is solidifying the GSI’s position in the enterprise IT landscape.

The requirement for a partner that can manage complexity and guide transformation continues to grow.

Speaking at the World Economic Forum, Capgemini CEO Aiman Ezzat described the need to view technology as an interconnected system.

“If you combine things like AI and space and advanced sensors and bioengineering, you can apply them to many areas in terms of environmental monitoring, to visualise the wind and the direction of the wind in real time and even in marine technology and air traffic control”.