Despite rising investments, enterprises are struggling to move AI from pilots into scalable production. Why? Lenovo answers.
For years, enterprise AI revolved around fragmented pilot projects, in which organisations tested isolated use cases without fully addressing how systems, data, and operations would ultimately integrate at scale. That experimental phase is now giving way to production intent, but it is exposing a deeper structural gap between deployment ambition and execution readiness.
In an exclusive interaction with EFY, SK Venkataraghavan, Director, Solutions and Services Group, Lenovo India, said that enterprises are moving toward a hybrid AI model that connects personal systems, enterprise infrastructure, and integrated intelligence layers into a unified architecture designed for scale.
“We structure enterprise AI across three pivots: personal AI, enterprise AI, and their integration,” Venkataraghavan said, describing Lenovo’s approach as a shift from siloed deployments to a connected framework.
According to insights from a CIO playbook referenced in the conversation, around 86 per cent of organisations are preparing to move AI initiatives from pilot to production, while nearly 98 per cent are increasing AI-related investments. However, the gap between intent and execution remains significant, as enterprises struggle to scale beyond controlled environments.
“The market is still very nascent, most implementations until last year were still in the POC stage,” he said, adding that the transition is now forcing enterprises to rethink architecture choices as they move away from highly custom-built silos toward more standardised and platform-led deployments.
He noted that early adoption was dominated by enterprise-specific custom architectures, but this is beginning to shift as organisations encounter scaling challenges where infrastructure design, total cost of ownership, and deployment consistency become critical constraints rather than secondary concerns.
At the architectural level, enterprises are also moving beyond the debate of edge versus cloud. Edge computing is becoming essential in distributed environments such as manufacturing and energy, while cloud systems are increasingly used for aggregation, orchestration, and analytics, creating a layered execution model rather than competing choices.
However, the most critical barrier is no longer model capability. Integration complexity, governance readiness, talent availability, and organisational alignment are emerging as the defining constraints in moving AI from pilot success to production-scale reliability.
“Integration, change management, acceptance, and also whether the systems and their organisations are ready to embrace AI is more complex than evaluating an AI,” Venkataraghavan emphasised. “It is no longer in the domain of IT. This is the biggest challenge every customer has.”
Taken together, the shift signals a clear transition from fragmented experimentation to structured, platform-led deployment, in which infrastructure maturity and integration readiness are becoming the real determinants of enterprise AI success.
