In today’s rapidly evolving digital economy, blending technological intelligence with human expertise is redefining the boundaries of business transformation. With AI shifting from automation to autonomous decision-making and contextual intelligence becoming the new competitive edge, enterprises must blend the power of AI with human ingenuity.
In this exclusive interview, Gautam Singh, Head – Analytics Data & AI at WNS, shares how the company’s AI+HI philosophy, Agentic AI innovations, and data modernization strategies are enabling enterprises to unlock real-time, value-driven outcomes.
From tackling post-merger data fragmentation to building hyper-contextual AI for India’s diverse markets, he offers an insider’s view into how WNS is shaping the next generation of intelligent, ethical, and scalable enterprise solutions.
WNS has championed the AI+HI model. How is this hybrid approach transforming traditional BPM services into real-time, value-driven operations?
At WNS, the AI+HI (Artificial Intelligence + Human Ingenuity) model reflects our core belief that machines can amplify human potential but not replace it. This hybrid approach is changing traditional Business Process Management (BPM) by moving beyond efficiency to deliver predictive, real-time, and outcome-based operations. Our domain experts, working alongside AI systems, provide contextual intelligence, ensuring decisions are aligned with the business, customer-focused, and ethically grounded.
For instance, in the insurance sector, our AI models proactively flag subrogation opportunities, while human experts assess legal feasibility and policy context. In shipping, AI accelerates the documentation process, while our professionals identify and address issues and ensure compliance. This collaborative model ensures high accuracy, adaptability, and trust, which are the hallmarks of next-generation BPM.
We have operationalized this model at scale using our AI Utilities Hub that leverages modular, reusable microservices to expedite time-to-value while embedding deep domain excellence. The result: faster information comprehension, smart automation, and measurable business impact, such as 80% improvement in document processing and 97% accuracy in claims detection. AI+HI is no longer an aspiration; it is a proven operating philosophy for real-time, value-centric transformation.
How is WNS leveraging Agentic AI to create autonomous workflows and decision-making capabilities across complex enterprise environments?
Agentic AI is a cornerstone of WNS Analytics’ transformation strategy. Unlike traditional automation, which executes static workflows, Agentic AI enables autonomous, adaptive, and context-aware decision-making across dynamic business environments.
Our proprietary framework for Agentic AI implementation helps clients assess readiness, map decision complexity, and design agent-based architectures tailored to specific business outcomes. These agents don’t just execute; they sense, plan, reason, and act with domain awareness.
Our flagship product, SKENSE, is a prime example. Built on a multi-agent framework, it automates high-context research tasks by addressing user queries, pulling structured and unstructured data, validating sources, and generating real-time reports with 99% accuracy. In client deployments, it has cut turnaround times by 85% and costs by over 90%.
As enterprises struggle with aging digital infrastructure, what strategies can help accelerate data modernization without completely overhauling legacy systems?
Updating data infrastructure is critical, but a complete replacement of legacy systems is often impractical, especially in heavily regulated or operationally complex sectors. A practical and layered approach to modernization enables organizations to overcome traditional roadblocks while driving business continuity.
This begins with establishing a unified data strategy that aligns with enterprise-wide goals. Modern tools, such as Generative AI (Gen AI)-powered components for Extract, Transform, and Load (ETL), data cleansing, and migration, can significantly reduce disruption and accelerate time-to-value.
Intelligent data fabric architectures further enable seamless data access across systems, decoupling insights from outdated tech stacks and allowing real-time decision-making without complete system overhauls.
For example, smart use of AI in the healthcare sector is to improve their Customer Relationship Management (CRM) systems by finding and fixing data errors, hence enabling a resilient supply chain. These AI agents learn over time, improving accuracy while freeing human teams to focus on higher-value, strategic activities.
By adopting modular, microservices-based architectures, organizations can introduce agility and intelligence incrementally. Data modernization, at its core, isn’t about replacing legacy systems; it is about augmenting them to unlock scalable, future-ready capabilities.
Mergers and acquisitions often lead to fragmented data ecosystems—how can GenAI and Agentic AI help unify and make sense of such complex, unstructured datasets?
Mergers and acquisitions often result in fragmented data ecosystems, with inconsistencies across formats, definitions, and governance frameworks. Traditional integration methods struggle to address semantic gaps and contextual mismatches between merging organizations, delaying value realization and increasing operational risk.
Gen AI and Agentic AI are now playing a transformative role in addressing these challenges. Gen AI can be used for tasks such as summarizing unstructured data, transforming formats, and de-duplicating records, speeding up the initial stages of integration while improving data quality.
Agentic AI introduces an additional layer of orchestration. Specialized AI agents can automate entity matching, standardize taxonomies, detect governance conflicts, and identify risks without requiring constant human intervention. These systems are designed to learn over time, preserving institutional memory and enabling continuity in decision-making across the merged entity.
When used effectively, this AI-powered approach can significantly accelerate post-merger integration, reduce compliance exposure, and improve confidence in business decisions. By turning fragmented data into a unified, intelligent foundation, enterprises can transform post-merger complexity into a competitive advantage.
With India’s vast and diverse consumer base, how can enterprises better harness native customer data to drive more contextual, predictive AI solutions at scale?
India’s consumer ecosystem is uniquely complex, shaped by vast behavioral, linguistic, and cultural diversity. This offers a powerful opportunity to build hyper-contextual AI models that reflect real-world nuances. At the same time, such diversity introduces challenges in terms of data standardization, bias mitigation, and model interpretability.
To address this, leading organizations are adopting three foundational pillars: data contextualization, inclusive model training, and ethical AI governance. AI systems must go beyond ingesting large volumes of data; they must be guided by deep domain excellence to reflect local patterns, regional behaviors, and cultural sensitivities. Human-in-the-loop frameworks ensure that models remain relevant and responsible, not just technically accurate.
Gen AI is also helping unlock new value by processing unstructured, vernacular content, enabling businesses to analyze voice, text, and visual data that were previously underutilized. Multi-agent AI systems can then dynamically segment users, forecast intent, and deliver personalized experiences across diverse digital environments.
Importantly, ethical AI frameworks play a critical role in maintaining fairness, transparency, and trust, ensuring that personalization enhances user value without veering into manipulation. In a market as diverse as India, these responsible, human-centered practices are essential for meaningful and scalable AI adoption.