Wipro Gen AI for Marketing
Wipro Gen AI empowers marketers to automate various tasks, boosting productivity and driving top-line performance. By analysing vast amounts of data, Wipro Gen AI identifies consumer behavior patterns, helping businesses identify SEO-friendly, relevant, and high-performing keywords for digital marketing campaigns. Moreover, it offers video tools for creating impactful marketing videos and product demos, effectively increasing brand awareness and driving conversions.
Wipro Gen AI for Supply Chain
Wipro Gen AI fosters seamless communication and collaboration with suppliers, enhancing supplier management. It also improves customer support by providing real-time assistance and automating responses.. Furthermore, it optimizes logistics planning, route optimization, and overall supply chain efficiency through scenario simulation and optimized solutions.
Wipro Gen AI for Contact Center
Wipro Gen AI implements virtual bots with very wide linguistic coverage, enabling them to understand natural queries and generate responses from multiple sources. It also provides virtual training for agents, self-help for creation of content such as FAQs in diverse formats like text, audio, and video, and incorporates next best action and recommendation engines. Wipro Gen AI has developed generative AI powered multi-lingual bots for customer interaction, advisory, and HR operations. Additionally, Wipro has utilized generative AI for automated content creation, including fully synthetized videos.
Wipro Gen AI for Shared Services
At Wipro we are rapidly integrating Generative AI into our offerings for Finance, Legal and HR areas. Our Gen AI framework analyzes HR policies and legal contracts, reducing the effort required for training conversational bots and assisting employees with their queries. In the Finance domain, we explore using Chat GPT sentiment analysis to assess customers based on publicly available information, enhancing credit risk modelling. Additionally, we aim to enhance our Resume Parser solution by generating questions based on any job descriptions, reducing dependencies and expediting hiring processes.
Wipro Gen AI for Banking
At Wipro, we leverage multiple Generative AI use cases across BFSI industry, building rapid prototypes for value assessment and scaling them for production. We envision the adoption of Generative AI across Retail/Commercial Banking, Sustainable Finance, Capital Markets/Investment Banking, Wealth/Asset Management, Private Equity, and Insurance sectors. Our Generative AI framework addresses concerns regarding model/data bias, domain-specific content moderation, and client-specific requirements. Wipro is committed to providing maximum value to our BFSI clients through the Generative AI journey.
Wipro Gen AI for Consumer and Retail
Businesses can deliver personalized shopping experiences by analyzing customer data and generating tailored product recommendations, driving customer satisfaction and sales with Wipro Gen AI,. It optimizes inventory management and demand forecasting through accurate predictions based on historical sales data, improving supply chain operations and reducing costs. Additionally, Wipro Gen AI aids in visual merchandising by generating virtual storefronts and product displays, enabling retailers to experiment with different layouts and designs. Furthermore, it automates customer support through chatbots, providing instant assistance and enhancing the overall customer experience.
Wipro Gen AI for Lifesciences
Wipro Gen AI identifies key accelerators such as enterprise knowledge search, content generation, digital assistance and synthetic data generation, which have wide-ranging applications, empowering lifesciences organizations with advanced capabilities and streamlined processes. Some examples include accelerating medical literature search for drug discovery, leveraging Gen AI for regulatory submission and Health Authority QR, providing Gen AI assistance for patient onboarding in clinical trials, enabling medical information and safety call center assistance, and utilizing synthetic data for building research ML models.