India’s enterprise AI push is moving beyond experimentation, but the next phase depends less on model availability and more on how organizations manage data, context, security, and costs. That view comes from Elastic’s India leadership as more initiatives shift from pilots toward production-scale deployments. Atul Ahuja, Area Vice President and General Manager for India at Elastic, said several organizations are already tying AI work to specific business outcomes rather than conventional proofs of concept. Enterprises hold data across structured databases, PDFs, scanned documents, video, audio, and legacy records; bringing that information into AI workflows while preserving context remains a major challenge. Elastic argues that search and retrieval across existing sources via connectors and APIs can avoid lengthy consolidation projects. As AI agents proliferate, inference and token costs also rise, making efficient retrieval and context engineering critical to avoid wasteful loops.
India’s enterprise AI push is moving beyond experimentation, but the next phase depends less on model availability and more on how organizations manage data, context, security, and costs. That view comes from Elastic’s India leadership as more initiatives shift from pilots toward production-scale deployments. Atul Ahuja, Area Vice President and General Manager for India at Elastic, said several organizations are already tying AI work to specific business outcomes rather than conventional proofs of concept. Enterprises hold data across structured databases, PDFs, scanned documents, video, audio, and legacy records; bringing that information into AI workflows while preserving context remains a major challenge. Elastic argues that search and retrieval across existing sources via connectors and APIs can avoid lengthy consolidation projects. As AI agents proliferate, inference and token costs also rise, making efficient retrieval and context engineering critical to avoid wasteful loops.