Growth enablers: Success will depend on enterprise readiness

Success in AI depends on enterprise readiness, focusing on strong data, technology, and operational foundations to scale AI effectively and drive sustainable business value, says Rajesh Varrier.

September 14, 2026 22:59 IST

Rajesh Varrier

By Rajesh Varrier

As AI moves from experimentation to enterprise deployment, a new reality is emerging: success is no longer determined by access to AI alone. The real differentiator is readiness. While organisations globally are investing heavily in AI, many still struggle to turn those investments into business outcomes. Cognizant’s recent research among AI decision-makers across major markets found that nearly two-thirds of enterprises report significant gaps between their AI ambitions and current capabilities. The challenge is not innovation; it is building the data, technology and operating foundations needed to put AI to work at scale.

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The harder work lies in connecting intelligence to the data, workflows and decisions that run a business. AI is adaptive and highly dependent on context, meaning its outputs are shaped by the information, processes and environment in which it operates. Without the right context, even sophisticated AI systems can struggle to deliver relevant outcomes. Simply adding AI to existing technology environments does not make it reliable or scalable. It requires the underlying systems, data and processes to be ready to support AI in real-world business settings.

To create lasting competitive advantage, enterprises must develop the capabilities to design, integrate, govern and continuously improve AI, rather than treating it as another technology to purchase and deploy. As enterprises work to close this readiness gap, IT services companies have a key role to play. With deep engineering expertise, experience managing complex environments and a proven record of delivering, the industry is well positioned to help organisations move from isolated AI deployments to industrialised AI capabilities across the enterprise.

That journey begins with strong foundations. First, data must become an AI asset. The value of AI depends on the quality and context of the information it can access. Data must be connected, governed and discoverable, with clear ownership and traceability. Equally important is unlocking the business knowledge trapped within legacy applications, databases and processes. Modernisation is no longer just about reducing technical debt; it is about making enterprise knowledge accessible to intelligent systems and enabling it to be used effectively.

Second, cloud and computing foundations must be designed for AI economics. AI workloads place unique demands on computing power, storage, networking and orchestration. Enterprises need the flexibility to determine where workloads should run based on factors such as security, data location, regulatory requirements, latency and cost. The goal is to build a technology estate that supports AI at scale while balancing performance and efficiency.

Third, AI requires production-grade engineering and operations. Moving from a successful pilot to an enterprise capability requires integration with business workflows, monitoring, governance, and accountability. AI cannot remain an overlay on top of legacy systems. It must be embedded into the processes through which work gets done, with the controls needed to ensure consistent performance and outcomes.

This also changes the role of modernisation. In the AI era, it is about creating an environment where data, technology and intelligence can work together reliably at scale. The systems most likely to constrain AI are often those that are hardest to integrate, secure and evolve.