AI summit focuses on turning compute into capability

A session titled “Building AI Readiness: From Compute to Capability” at the India AI Impact Summit 2026 examined how access to graphics processing units (GPUs) can be translated into sustainable innovation and market-ready AI solutions.

As generative AI moves from experimentation to production-scale deployment, the discussion focused on a key structural shift within the ecosystem: from prioritising peak compute performance to building infrastructure, software environments and business models aligned with specific AI workloads and commercial pathways.

For Indian start-ups and developers using the IndiaAI compute platform, speakers observed that the challenge is no longer limited to hardware availability. Instead, it lies in identifying viable use cases, optimising performance, managing costs and scaling responsibly. GPU selection now involves considerations such as memory architecture, interconnect efficiency, total cost of ownership and deployment models. AI readiness, panellists noted, depends as much on technical decision-making and organisational preparedness as on processing power.

Change Management and SME Adoption

Dr Panneerselvam M, Chief Executive Officer of the MeitY Startup Hub at the Ministry of Electronics & Information Technology, emphasised that AI adoption requires more than infrastructure. He highlighted the importance of change management, particularly for small and medium-sized enterprises (SMEs) and legacy businesses.

“It is very interesting that, unlike in the past where most software was first adopted by enterprises and then percolated into individual usage, we are now seeing a first-of-its-kind change,” he said. “When we discuss AI readiness, from compute to capability, I would also add one more ‘C’: change management.”

He noted that many Indian SMEs are owner-managed or family-run businesses, which face distinct challenges in adapting to new technology adoption cycles.

Informed Investment and Experimentation

Timothy Robson, AI Business Development Director (EMEAI & APeJ, Datacenter GPU) at AMD, stressed the importance of informed investment and accessible experimentation environments. He said organisations should fully understand both the technology and their route to market before committing significant capital.

Robson pointed to initiatives designed to lower barriers to entry, including development cloud platforms and access to compute resources for testing specific use cases. He added that cost efficiency and clarity around total cost of ownership are central to enabling start-ups to scale sustainably.

Dr Thomas Zacharia, Senior Vice President, Strategic Technology Partnerships and Public Policy at AMD, underlined the role of strong research and innovation ecosystems in determining leadership in AI.

He observed that countries leading in AI typically have robust research foundations, where ideas are rigorously tested and validated. Sustained investment in innovation labs and start-up ecosystems, he said, enables new technologies to emerge and transition into enterprise and public sector adoption.

From Infrastructure to Innovation

The session concluded that AI competitiveness will be shaped not solely by access to compute resources, but by the ability to convert infrastructure into innovation. This requires strong research capabilities, active start-up participation, market clarity and organisational readiness.

As India expands its sovereign compute capacity and broadens access through national platforms, the next phase of growth will depend on how effectively these resources are transformed into real-world deployment and globally competitive solutions.