WNS Global Services, a leading Business Process Management (BPM) company that offers a range of outsourcing services, is integrating generative AI across its talent acquisition, employee services, sales and marketing operations. Generative AI’s summarisation capabilities are being leveraged to help employees quickly understand policy and other technical documents.
The sales and marketing department at WNS is also leveraging generative AI for data creation and campaign recommendations. Additionally, the integration of generative AI into WNS’ HR Chatbot called Amelia is helping the bot generate contextual and cognitive responses for employees’ queries.
“This technology reshapes productivity by curating original content and automating tasks like data entry, freeing IT professionals to perform more strategic work and take on advisory roles. By adopting an ethical and responsible approach to generative AI, the IT sector can elevate capabilities to unlock real value for sustainable growth,” Keshav R. Murugesh, Group CEO, WNS Global Services told AIM.
In this exclusive interaction, Murugesh discusses how WNS is leveraging the power of Large Language Models (LLMs) not just internally, but to deliver better products and services to its clients.
Leveraging Generative AI
“At WNS, we are advancing our offerings by strategically blending generative AI and proprietary AI/ML models tailored for industry-specific challenges. “In insurance, our NLP model, backed by domain knowledge, identifies subrogation opportunities. Meanwhile, generative AI recommends the most optimal next steps. When it comes to travel, our knowledge engine, paired with generative AI, enhances customer experiences by delivering instant, personalised responses,” Murugesh said.
In the healthcare segment as well, WNS’ ML models combined with generative AI enable accurate medical summarisation, diagnoses, and code identification. “Our approach centres on using LLMs to contextualise industry-specific AI/ML models, which are integrated into our clients’ operational environments. Leveraging our extensive domain expertise, we design efficient prompts to extract high-quality outputs cost-effectively. This approach differentiates us, allowing us to offer customised solutions that drive transformation across the value chain.”
When asked whether WNS is contemplating developing its own LLM, Murugesh said that his firm’s approach involves harnessing the capabilities crafted by leading hyperscalers and specialised industry or function-specific LLMs. “In most cases, LLM foundation models coupled with WNS Triange’s proprietary ML models help us deliver tailored solutions that cater to different functional domains and industries. Wherever required, WNS leverages its AI/ML and domain capability to fine-tune existing foundation models to get specific results.”
Generative AI challenges
While enterprises today are embracing the power of LLMs, they come with their own set of challenges-hallucinations for instance. Despite numerous attempts, the creators of these models have not yet managed to resolve the problem at a technical level.
While WNS is leveraging these models both internally and externally, to mitigate the risks, they have created robust frameworks and solutions to ensure transparency, explainability, and bias reduction. “By incorporating ethical considerations and maintaining the highest security standards, we are cultivating an environment that maximises the advantages of these models while minimising potential risks,” Murugesh said.
AI will augment human capabilities
While generative AI has gained a lot of traction in the last few years, it has also led to concerns about replacing human jobs. A report by consulting firm McKinsey stated that millions of human jobs could be impacted by AI by 2030. We have already started seeing examples of such displacement, when Dukaan, a DIY platform that enables merchants with zero programming skills to set up their e-commerce business, recently laid off 90% of support staff and replaced them with an AI chatbot named Lina.
While such concerns exist among employees of all organisations, WNS is proactively addressing these concerns of its employees. “We recognise the importance of workforce development and strongly emphasise training and upskilling. We aim to foster a continuous learning and innovation culture, equipping our employees with skills to collaborate with AI technologies.”
What AI will truly do is augment human capabilities, according to Murugesh. “The two can coexist – a powerful synergy of AI intelligence and human creativity to drive greater efficiency and productivity. Depending on the nature of the business process, both AI and humans may assume the role of a maker or checker. AI’s accuracy and speed make it a capable maker, while critical thinking and decision-making abilities make humans ideal for the role of a checker. This flexibility ensures a well-balanced approach to tasks.”
AI needs to be regulated
Yet, fully harnessing AI’s potential requires a responsible AI framework to address concerns of ethics, reliability, transparency, and compliance. Moreover, recognising this potential for AI to drive transformative change, Murugesh believes that regulation is necessary for its responsible development and deployment. While AI may not pose existential threats, the potential for misuse or unbridled expansion points to a possibility of unintended repercussions, he said.
“Anchoring responsible AI practices within comprehensive regulatory frameworks becomes pivotal in averting and minimising such inherent risks and securing the positive influence of AI within society. We consciously advocate for implementing AI regulations that emphasise ethical application, transparency, and accountability.”
