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12. August 2025
Building A Digital Future: Architects Must Adapt To Ai Era
The Rise of AI-Ready Architecture: A Wake-Up Call for Architects
As Artificial Intelligence continues to revolutionize industries worldwide, a pressing concern has emerged: the need for architecture that can seamlessly integrate with AI. The traditional architectures that once supported data, infrastructure, applications, and user experience are no longer fit for the pace and demands of AI evolution.
According to Bala Prasad Peddigari, chief innovation officer at TCS, traditional architectures are showing signs of strain. “We’re facing performance bottlenecks, scalability limitations, and integration complexities,” he warned during his address at the NIIT StackRoute Digital Architect Conclave 2025. These challenges are not only hindering the growth of AI systems but also threatening to undermine their very foundations.
The problem lies in the way traditional architectures have been built. They were designed with a linear approach, where data and applications were processed one by one, without considering the broader context. This narrow focus has led to performance bottlenecks, as systems struggle to handle the sheer volume of data generated by AI-driven applications.
Scalability is another major issue facing architects. Traditional architectures are not designed to scale horizontally, which means that adding more power or resources to the system can be a cumbersome process. In contrast, many modern AI systems require exponential scaling to keep up with their ever-increasing demands. This has resulted in “lift-and-shift” strategies, where existing architectures are simply scaled up, rather than reimagined from the ground up.
Integration complexities are also a major obstacle facing architects. As AI systems become increasingly interconnected, integrating them with traditional architecture becomes a daunting task. The resulting systems often suffer from a lack of cohesion, making it difficult to identify and address issues as they arise.
The consequences of these limitations cannot be overstated. Many pilots in AI remain unscalable because they lack foundational AI-ready architectures. This means that even the most promising AI applications are unable to reach their full potential due to the constraints imposed by traditional architecture.
An AI-ready architecture is one that has been designed with AI in mind from the outset. This includes considerations such as data flow, processing power, and integration with other systems. An AI-ready architecture must be able to handle the vast amounts of data generated by AI-driven applications, process it in real-time, and provide seamless integration with other systems.
One key aspect of an AI-ready architecture is its ability to handle the complexities of edge computing. Edge computing refers to the practice of processing data closer to where it’s generated, rather than sending it all the way to the cloud for processing. This approach has several benefits, including reduced latency and improved security.
However, traditional architectures are often ill-equipped to handle edge computing. They require a centralized infrastructure that can manage and process large amounts of data, which can be a major bottleneck in edge computing environments.
To address this challenge, architects must adopt a decentralized approach to architecture design. This involves creating systems that are capable of processing data locally, without relying on a centralized infrastructure. This requires a fundamental shift in the way we think about architecture, moving from a traditional “one-size-fits-all” approach to a more modular, flexible framework.
Real-time processing is another critical aspect of AI-ready architecture. As AI systems become increasingly sophisticated, they require faster and more efficient processing power. Traditional architectures are often unable to keep up with these demands, resulting in performance bottlenecks and reduced accuracy.
To address this challenge, architects must adopt a real-time processing approach that prioritizes speed and efficiency above all else. This involves creating systems that can process data at the speed of thought, without sacrificing accuracy or reliability.
In reality, the need for AI-ready architecture is no longer a consideration; it’s a requirement. The traditional architectures that once supported data, infrastructure, applications, and user experience are no longer fit for the pace and demands of AI evolution. Architects must adopt a new approach to design, one that prioritizes scalability, integration, and real-time processing.
By adopting an AI-ready architecture, architects can unlock the full potential of AI systems and create a better future for all. The question is not whether we need AI-ready architecture but how we’re going to make it happen. It’s time for architects to wake up to the reality of this challenge and adapt their approach accordingly.