Wipro Collaborates with HPE to unveil On-Premise Gen AI Solution

Wipro Limited has announced a strategic partnership with Hewlett Packard Enterprise (HPE) to introduce the GenAI solution. This collaboration aims to leverage artificial intelligence (AI) to enhance operational efficiency and improve customer experience globally.

The GenAI platform, powered by Wipro’s Smart Operations platform and HPE’s Machine Learning Development Environment, is designed to deliver exceptional results. Field testing indicates potential benefits, including a 50% reduction in Mean Time to Resolution (MTTR) for GenAI applications, a 30% decrease in incident inflow, improved Overall Equipment Effectiveness (OEE), and reduced process cycle time, all contributing to ongoing operational excellence.

Wipro highlighted that industries dependent on customer service, IT support, and operations—such as financial services, healthcare, and manufacturing—stand to gain significantly from this collaboration. Clients will have access to a range of Large Language Models (LLMs) tailored to various business objectives, enhancing decision-making capabilities.

Jo Debecker, Managing Partner and Global Head of Wipro FullStride Cloud, stated, “The co-creation of the GenAI platform underscores our long-standing strategic partnership with HPE and our commitment to delivering advanced AI solutions within our new Customer Experience Center. This center will showcase the potential of HPE’s Machine Learning Development Environment combined with Wipro’s cutting-edge solutions. Together, we will continue to drive innovation to help our clients achieve their business goals.”

Marc Waters, Senior Vice President of Global Sales at HPE, emphasized the synergy between the two companies, saying, “Wipro’s deep technical expertise combined with HPE’s AI technology creates a powerful combination that will accelerate time to value for our customers. By integrating HPE’s Machine Learning Development Environment into Wipro’s GenAI customer experience, we will enable customers to develop and deploy AI models faster, streamline data preparation, and integrate with popular machine learning frameworks.”


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