We recently had an in-depth discussion with Sreekanth Menon, a seasoned industry expert who leads the AI/ML practice at Genpact Analytics. Sreekanth leads the implementation of his AI/ML projects worldwide and has spent over 20 years fostering innovation and industry expertise. He has been instrumental in incubating and launching over 50 of his advanced analytics solutions in global markets, partnering with Fortune 500 customers to drive business transformation through innovative AI-driven solutions and practices.
In this exclusive interview, Sreekanth shares valuable insights on Genpact’s AI strategy. The strategy builds on his four pillars: AI at Scale, AI-Driven Insights, AI-Enabled Autonomous Processes, and AI for Operations. He also sheds light on Genpact’s generative AI strategy, the different use cases they are developing, and their vision for the future of Large Language Models (LLM).
Explore the partnerships Genpact is fostering to operate AI more efficiently, and understand how Genpact is equipping its talent pool to adapt to the rapidly changing AI landscape.
What is Genpact’s AI strategy?
Genpact has been developing and refining AI capabilities for many years. This has enabled us to create innovative, industry-specific solutions for our clients. Engagement models across industries are changing rapidly, and even more so with the advent of generative AI. Today, businesses want to offer any service, anywhere, anytime. They still want an equal mix of traditional and modern tools. Genpact’s AI strategy has resulted in a state-of-the-art framework for such hybrid demands.
Genpact’s AI strategy is based on four pillars.
- AI at scale: Building full-stack AI applications and multi-year AI/ML managed services with DataOps and MLOps
- AI-powered insights: Integrate descriptive to predictive insights to enable priority account scaling.
- AI-enabled autonomous processes: Automate, optimize, and redefine business processes, operating models, and customer models through an AI lens.
- AI for operations: Adopting AI to transform and modernize operations.
These foundations are powered by an agile and ethical framework that enables AI developers to bridge the gap between idea and implementation.
“Promoting responsible AI adoption is a key strategic imperative at Genpact.”
Are you developing a GenAI strategy? How are you looking to leverage GenAI for your clients?
Our Generational AI strategy relies on a democratization-through-incubation paradigm with a strong foundation for responsible AI. Genpact’s generative AI strategy considers the different stages of adoption: process, people, tools, technology incubation, and ultimately democratization. This will allow us to establish a Large Language Model (LLM) Center of Excellence (CoE) to identify technology foundations, enhance tool stacks, streamline processes, and deliver self-service generative AI apps to employees across the enterprise. will be able to provide
A Center of Excellence can serve as a change management hub for designing, integrating, extending, and democratizing prototypes into enterprise-grade solutions. They help develop internal employees into the roles of Immediate Engineers, Immediate Compliance Checkers, Customer Protection Officers, and other related roles. These centers serve as hubs for scaling generative AI prototypes into design, integration, and enterprise-grade solutions.
Genpact’s Gen AI service for clients follows three approaches:
- Generative AI for Enterprise LLM Build custom, fine-tuned foundational models using DataOps and FMOps to enable enterprise deployment of generative AI applications.
- Generative AI-enabled business processes Automate, optimize, and redefine your business processes with search, generation, classification, clustering, summarization, and extraction generation AI capabilities.
- Generative AI for Operations Empower developer and analyst workflows in technology services and digital operations.
What use cases is Genpact currently developing?
Genpact’s gen AI competencies are leveraged at the intersection of various industries and their service lines. In the healthcare sector, for example, we are focused on hyper-personalized care and patient experiences, centered on value-based care. The banking potential of Gen AI cannot be overstated. LLM-powered apps can help banks grow revenue, manage risk, improve customer experience, drive innovation, and reduce costs.
The same goes for insurance companies, which are document-oriented companies. By some estimates, insurance companies spend 30-40% of their time on administrative tasks. The exhaustive nature of these tasks can be mitigated by using the LLM application.
What do you think about the future of LLM?
Enterprise-level traditional AI/ML solutions are on the cusp of a major overhaul. A language is an interface. Companies today are all about rapid engineering. However, the complexity of prompt engineering varies. At Genpact, his approach towards enterprise-grade gen AI solutions goes through three phases.
- fast tuning,
- A few shots of tuning, and
- Fine-tune LLM.
Moreover, as the underlying model matures, more emphasis will be placed on protecting sensitive information. This strategy will focus on ensuring companies have the right talent and management teams to remain competitive and maximize their AI investments. The future of LLM will more or less go something like this:
- Multimodal Mastery: Improved natural language understanding, problem-solving ability, and integration of different data types.
- Dynamic knowledge integration: Reinforce learning with fewer examples to reduce training time and resources, along with real-time adaptation to new information and seamless integration with existing knowledge.
- hyper personalization: Improved responsiveness and adaptability to user input, changing requirements, and customized solutions.
- Responsible AI integration: Ensure safety, ethics and collaboration in AI development.
What partnerships are you promoting as part of AI @ Genpact?
Throughout its journey, Genpact has formed strategic alliances with industry pioneers and leaders. We recently partnered with two of the biggest companies in the AI industry. For example, Genpact partnered with his AWS to help accelerate the digital transformation of companies around the world. Meanwhile, our partnership with Dataiku will help build differentiated solutions that address key challenges organizations face when implementing MLOps and responsible AI at scale, including data governance, model management and compliance requirements. increase.
Genpact’s partnership aims to enable organizations to operate AI more efficiently. Genpact is exploring opportunities for co-innovation with hyperscalers in the Gen AI space.
How are you equipping your talents to drive this sudden change in the ecosystem?
Upskilling is Genpact’s strength. We have been at the forefront of skill development for over a decade. Our work covers AI/ML in general. The reason behind our success can be attributed to our learning platforms Genome and ML Incubator.
Genpact’s Genome is a learning platform that showcases a curated list of coursework tailored to keep our strong workforce of over 100,000 up to date with global digital dividend trends. For example, Genome’s gen AI channel is designed to help employees understand the rubrics of how LLM works and how it can be applied to build enterprise-grade solutions. Genpact’s ML Incubator is a flagship, meticulously structured hybrid learning program for data science aspirants that drives contextual learning in parallel with their normal workflow. The program includes Data Science, Data Engineering, and Augmented Intelligence Advisory, each of which is an important skill in today’s data/analytics environment. The program addresses the scale of reskilling/upskilling efforts required to ensure that large global digital organizations have resources with AI/ML skills readily available.
