Akhilesh Ayer, EVP & Head – WNS Triange (Research, Data, Analytics & AI Business Unit), WNS Global Services, discusses change management, establishing a robust digital presence through generative AI applications and scaling Proof of Concept projects.
By harnessing these AI and digital capabilities, organisations are looking to enhance their operational efficiency and propel growth due to the wealth of data that is now at their disposal.
“The realm of advanced analytics, including AI and ML, offers a plethora of new possibilities across business functions. Organisations can leverage MLOps, AI, DataOps and the power of the cloud to accelerate the scaling up of proof of concepts. Partnering with experienced experts can often accelerate the scaling-up process using pre-built accelerators and productised solutions,” said Akhilesh Ayer, EVP & Head – WNS Triange (Research, Data, Analytics & AI Business Unit), WNS Global Services.
The company collaborates with clients across end-to-end business processes, providing effective solutions for their business challenges. Ayer discusses establishing a robust digital presence through generative AI applications and scaling Proof of Concept projects.
Excerpts from the interview;
How do you work with cross-functional teams to achieve their goals- please give us an example.
We leverage analytics as a powerful tool to lower costs, increase revenue and optimise operational efficiencies. Our approach involves integrating analytics seamlessly into processes, necessitating close collaboration with cross-functional teams within our clients’ organisations.
A unified customer view, made possible by data analytics, is driven through collaborative efforts and inputs from cross-functional teams and data sources. Think of it as a dynamic dossier that continuously updates itself when a customer places an order, explores online options or engages with the contact centre. With such insights into customer behaviour and preferences, businesses can deliver exceptional experiences through personalisation.
For instance, a customer who is part of a hotel loyalty program would be delighted to discover that the hotel has provided her with hypoallergenic bedlinen, based on her request during a previous stay. Achieving a unified customer view requires the collaboration of various teams within the client organisation, including functional leads, operational experts, technologists from the CIO/CDAO organisation, and data and analytics experts.
Can you give examples of how your business unit has helped clients leverage data and analytics to drive business outcomes?
WNS Triange empowers clients to achieve business outcomes by harnessing the transformative potential of AI, analytics, data and research in many ways.
- Revenue Generation through Outcome-driven Engagement: WNS helped a leading UK-based retailer transform their retail media inventory utilisation. By aligning with the client’s objectives of maximising potential revenues from these transformative initiatives, WNS delivered substantial uplift in financial performance.
- ROI Maximisation: WNS collaborated with a global pet care food company to maximise the return on marketing investments. By deploying a sophisticated marketing mix modelling solution, WNS enabled the CPG company to measure the true impact of marketing campaigns across brands, sales channels and marketing levers.
- Creation of New Revenue Streams: WNS enabled a global corporate travel management client to carve out new revenue streams through our Data Monetisation Factory.
- Efficiency Improvement: WNS spearheaded a data-driven transformation journey for a UK-based global insurance major. WNS transformed their legacy data and analytics processes by dismantling silos, streamlining decision-making, harnessing data effectively and fostering trust within the organisation.
- Data Modernisation: WNS played a pivotal role in the data modernisation journey of a regional bank in the US. By streamlining its master data management practices, WNS enabled the bank to establish a cutting-edge wealth data platform in the cloud. This platform provided a 360-degree view of customers, facilitating cross-selling and upselling while enhancing customer satisfaction KPIs.
- Centre of Excellence (CoE): WNS assisted an Australia-based global insurer (with geographically disparate entities) in establishing a global data and analytics CoE. This CoE facilitated reusability, governance and speed-to-market by leveraging best practices across various entities.
How do you see generative AI impacting your function, and how can professionals upskill in this competitive job market?
Generative AI has tremendous potential to revolutionise the business landscape, offering various transformative opportunities for organisations. It can enable businesses to uncover new insights from unstructured data, enhance customer service, improve productivity, build robust and intelligent data pipelines, extract valuable metadata and make informed decisions. Here are a few examples illustrating how generative AI can transform the data and analytics realm across various industries.
- Insurance: Generative AI can help insurance companies analyse vast amounts of data to identify patterns and predict future risks. For instance, it can generate synthetic data to augment the existing data sets, enhancing prediction accuracy and reducing errors. Furthermore, it enables the simulation of different scenarios, helping insurers comprehend the impact of various events on their business. For example, Generative AI can simulate the effects of natural disasters on an insurer’s portfolio, facilitating policy adjustments and pricing changes accordingly.
- Retail: In the retail industry, Generative AI finds utility in analysing customer behaviour and preferences to deliver personalised recommendations. For example, it can generate product recommendations based on a customer’s purchase history and browsing behaviour. Additionally, it can enable virtual try-on experiences, allowing customers to visualise how clothing or makeup will appear on them before making a purchase.
- Supply Chain: Generative AI can generate demand forecasts, assisting manufacturers in planning their production schedules and minimising waste. It also optimises transportation routes and reduces shipping costs by analysing traffic patterns, weather data and other variables. Moreover, Generative AI can identify and mitigate risks within the supply chain, such as supplier disruptions, quality issues and compliance violations.
Generative AI powered by Large Language Models (LLM) can enable computers to understand and analyse human language effectively. It can be used to analyse customer feedback, extract insights from unstructured data and improve customer experience.
Language models like GPT are very effective in drafting and reviewing documents, patents and contracts. They excel at identifying, summarising and highlighting critical points in regulatory documents. Furthermore, they can swiftly find and answer specific queries with large documents and scan historical data to recommend an appropriate course of action.
What advice would you give data leaders to give scale to Proof of Concept projects?
The groundwork for scaling the proof-of-concept projects into full-fledged downstream implementation and the associated change management begins with a comprehensive roadmap for data, analytics and AI initiatives. To effectively scale analytics projects, a fail-fast approach is necessary, involving the development of new hypotheses, prioritising ease of implementation and selecting the right proof of concept to maximise outcomes and impact and deliver initial success.
The realm of advanced analytics, including AI and ML, offers a plethora of new possibilities across business functions. Organisations can leverage MLOps, AI, DataOps and the power of the cloud to accelerate the scaling up of proof of concepts. Partnering with experienced experts can often accelerate the scaling-up process using pre-built accelerators and productised solutions.
Change management, which encompasses cultural transformation, is an important aspect many organisations miss. Data analytics projects must receive sponsorship at the C-level, with the CEO leading the charge. Only then can there be a mindset change toward using data-led insights to guide decision-making across the organisation, thus accelerating the scaling up of proofs of concept?
What emerging technologies are you keen on, and how do they impact the enterprise landscape?
The pandemic has accelerated the pace of digital transformation, prompting clients to seek real-time connectivity with their customers. Customer centricity in every function of the company has become crucial. As a result, clients must maintain an “always on” presence across physical and digital channels to proactively adapt to rapidly evolving customer expectations.
Clients require operations that can swiftly adjust to changing consumer needs and supply chain disruptions. This necessitates the implementation of intelligent operations through a rapid test, learn and scale approach to new capabilities. Clients are also moving up the maturity curve in adopting AI by incorporating AI into enterprise-wide decision-making and automation. Consequently, there is a growing demand for transparent and explainable AI and ML algorithms implementations.
As AI and digital capabilities become all-pervasive, companies gain a competitive edge by leveraging these capabilities to improve efficiency and drive growth based on the extensive volume and variety of data available. Customers’ rising adoption of digital technologies compels companies to establish a strong digital presence to stay relevant.
Global organisations are prioritising sustainability and establishing dedicated ESG functions. This calls for the integration of enterprise-wide data to improve ESG metrics and achieve sustainability goals. Analytics interventions in these areas have become pivotal physical and cultural change drivers.
