As companies race to adopt AI, a growing concern lurks in the background: Will AI-driven efficiency lead to job cuts even as profits rise? While businesses look to AI for growth, employees wonder if automation will replace their roles.
In an exclusive interview with AIM, Gautam Singh, business unit head of WNS Analytics, mentioned that they are expecting revenue growth with generative AI in play. “About 5% of our revenue in the fiscal year 2025 is expected to be influenced by generative AI. I can cite examples where we’ve achieved 30-40% efficiency improvements through analytics, AI, and automation initiatives,” he said.
For fiscal 2025, WNS forecasts revenue (excluding repair payments) between $1,293 million and $1,357 million, and ANI between $206 million and $218 million.
“Currently, about $300 million out of our $1.3-1.4 billion in revenue is analytics-influenced, and that proportion is steadily growing,” Singh noted.
WNS Analytics is the data, analytics, and AI practice of WNS. It provides industry-specific productised services that are tailored to specific client needs.
These services are delivered through an integrated approach of its industry-specific proprietary AI assets and subject matter experts to drive outcomes for the clients. This approach is further strengthened by its proprietary AI labs and strategic partnerships.
Singh noted that WNS Analytics has adopted outcome-based models, where it essentially backs the performance by agreeing to be compensated based only on the value or revenue benefits it delivers to clients. These types of propositions are becoming increasingly attractive to clients, as they minimise their risk and ensure the company gets paid only when the results are delivered.
It’s All About Upskilling
As WNS Analytics anticipates a significant increase in revenue influenced by AI, it becomes equally vital to focus on upskilling employees to harness the full potential of these technological advancements. “We have partnered with several training providers to train our employees on generative AI specifically. So far, over 16,000 of our 60,000 employees have completed this training,” he said.
They also run the Curiosity Project, an AI learning week, where experts from institutions like Harvard University and Nanyang Business School help train their teams. This is part of a comprehensive and ongoing training program across the company, especially for their analytics teams, though it extends well beyond them. The goal is to equip everyone in the company to use generative AI tools.
For example, Copilot is installed on all their employees’ computers, and everyone is being trained on how to use it to improve work efficiency.
Data is AI’s Oil
Another factor behind successful AI automation is the proper utilisation of data.
“Firstly, it’s important to note that automation primarily enhances efficiency, but the real value comes from how analytics can drive higher outcomes. Automation can get you to the baseline, but delivering exceptional results requires deeper analytics insights,” Singh said.
Next comes the data to make all of it work. A reason why AI and data are closely linked is that data often exists in silos, coming from various legacy systems clients have used over the years.
Additionally, there are new data sources like unstructured data, image data, audio data, and social media data that can complement the more traditional, structured data from legacy systems.
“The power of new technologies like AI and generative AI lies in their ability to combine and process this diverse data to create even more powerful insights,” the business unit head said.
Explaining how companies can unlock data’s potential, Singh stated that one common pitfall with data initiatives is that everyone tends to focus on consolidating their data into data lakes or warehouses. The issue with this approach is that it targets the wrong end of the process.
“My advice to clients is to begin with the business use case and work backwards. Instead of immediately building a full data lake, start by creating a ‘data pond’, a smaller, focused dataset that specifically addresses that business need,” he said.
Automation Approach of WNS Analytics
As a pure-play analytics company, WNS Analytics aims to tackle clients’ challenges by leveraging both technology and analytics to address business needs and opportunities. It focuses on use cases that drive revenue growth as well as cost optimisation and efficiency.
One aspect of their business is directly offering analytics services and the other is integrating analytics expertise into WNS’s BPO/BPM operations.
WNS, being a business process management (BPM) company, manages entire business processes for clients. In today’s landscape, all of these processes are being enhanced by analytics interventions powered by technology and analytics from its team. This means that the work WNS does is influenced by the horizontal support its analytics team provides to WNS’s vertical-specific offerings.
“To be specific, in most client conversations, we examine which parts of the business process can be automated and which can be boosted by analytics. By ‘boosted’, we mean adding value through our interventions. We develop both automation tools and platforms that augment or boost the value clients get from these processes,” Singh mentioned.
This approach applies across industries. For instance, in the insurance sector, WNS focuses on claims management, determining which parts can be automated and which can be optimised. In CPG companies, it analyses supply chains to identify areas for automation, either through WNS or directly through our analytics services.
In 2022, WNS acquired a company called Vuram, specialising in hyper-automation, which has become a critical component in these discussions.
As part of the broader WNS entity, its focus is on automation combined with analytics, tools, methodologies, platforms, and algorithms to enhance nearly all of the client propositions and conversations.
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