As companies rush to adopt AI, a growing concern lurks in the background: Will AI-driven efficiency lead to job cuts even as profits rise? As businesses look to AI for growth, employees are wondering if automation will replace their jobs.
In an exclusive interview with AIM, Gautam Singhhead of the WNS Analytics business unit, mentioned that he expects revenue growth thanks to the generative AI at play. “Approximately 5% of our revenue in fiscal 2025 is expected to be influenced by generative AI. I can cite examples where we have achieved 30-40% efficiency improvements through analytics, AI and automation initiatives,” he said.
For fiscal 2025, WNS projects revenue (excluding reparation payments) between $1,293 million and $1,357 million, and ANI between $206 million and $218 million.
“Currently, about $200 million of our $1.3 billion to $1.4 billion in revenue is influenced by analytics, and this proportion is constantly increasing,” Singh noted.
WNS analysis is the data, analytics and AI practice of WNS. It provides industry-specific product services tailored to specific customer needs.
These services are delivered through an integrated approach of its industry-specific proprietary AI assets and subject matter experts to drive results for clients. This approach is further strengthened by its proprietary AI laboratories and strategic partnerships.
Singh noted that WNS Analytics has adopted outcomes-based models, where it essentially underpins performance by agreeing to be compensated solely based on the value or revenue benefits it delivers to clients. These types of propositions are becoming increasingly attractive to clients because they minimize their risks and ensure that the company only gets paid when results are delivered.
It’s all about improvement
As WNS Analytics anticipates significant AI-influenced revenue growth, it becomes equally critical to focus on employee upskilling to harness the full potential of these technological advancements. “We have partnered with several training providers to train our employees specifically in generative AI. So far, more than 16,000 of our nearly 63,000 employees have completed this training,” he said.
They also lead the Q-Riosity 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, ongoing training program across the company, particularly for their analytics teams, although it extends well beyond them. The objective is to equip everyone in the company to use generative AI tools.
For example, Copilot is installed on all employees’ computers, and everyone is trained on how to use it to improve work efficiency.
Data is the oil of AI
Another factor behind successful AI automation is the proper use of data.
“First, it’s important to note that automation primarily improves efficiency, but the real value comes from how analytics can drive better results. Automation can get you to the grassroots, but to get exceptional results requires deeper analytics,” said Singh.
Next comes the data to make it all work. One of the reasons AI and data are closely linked is that data often exists in silos, coming from various legacy systems that customers have used over the years.
Additionally, there are new data sources such as unstructured data, image data, audio data, and social media data that can complement the more traditional structured data in existing 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,” said the business unit head.
Explaining how businesses can unlock the potential of data, Singh said one of the common pitfalls of data initiatives is that everyone tends to focus on consolidating their data into data lakes or warehouses. The problem with this approach is that it targets the wrong side of the process.
“My advice to clients is to start with the business use case and work backwards. Instead of immediately creating an entire data lake, start by creating a “data pool,” a smaller, focused data set that specifically addresses the needs of the business,” he said.
WNS Analytics Automation Approach
As a pure-play analytics company, WNS Analytics aims to address its clients’ challenges by leveraging both technology and analytics to address business needs and opportunities. It focuses on use cases that drive revenue growth and cost optimization and efficiency.
One aspect of their business is to directly offer analytics services and the other is to integrate analytics expertise into WNS’s BPO/BPM operations.
WNS, as a business process management (BPM) company, manages entire business processes for clients. In today’s landscape, all of these processes are improved by analytical interventions powered by technology and its team’s analytics. This means that the work WNS does is influenced by the horizontal support its analytics team provides to WNS’s specific vertical offerings.
“To be more specific, in most customer conversations we look at which parts of the business process can be automated and which can be enhanced with analytics. By “boosted”, we mean adding value through our interventions. We are developing both automation tools and platforms that augment or augment the value that customers derive from these processes,” Singh said.
This approach applies to all sectors. For example, in the insurance industry, WNS focuses on claims management, determining which parts can be automated and which can be optimized. In consumer goods companies, it analyzes supply chains to identify areas for automation, either through WNS or directly through our analytics services.
In 2022, WNS acquired a company called Vuram, which specializes in hyper-automation, which has become a vital part of these discussions.
As part of the broader WNS entity, the focus is on automation combined with analytics, tools, methodologies, platforms and algorithms to improve almost every customer proposition and conversation.
