With GenAI, billing models more outcome-based: TP India head Maneesh Daga

Teleperformance India says AI is driving a shift from low-end bpo work to outcome-based, higher-value services, reshaping billing models, productivity and hiring strategies

French business process outsourcing company (BPO) Teleperformance, now rebranded TP, with about 90,000 employees in India, counts the country among its most profitable and high-growth markets. TP India head Maneesh Daga, who joined the company last year, says in a video interview to Avik Das that BPO companies are shifting from low-end work to higher-end outcome-driven projects with the help of artificial intelligence (AI). 

 

How is the BPM (business process management) landscape changing with the impact of AI and where do you see the maximum intervention?

 

AI is not only helping us to automate what we used to do but it is also helping us to shift to measurable business outcomes. Operating behaviour has become more predictive because there is a set of data that you look at and then understand it better. So we can think about more demand forecasting, skill-based routing and early detections of things from a delivery standpoint. But we are also moving up the value chain because clients are expecting us to deliver more insights and automation. Scale in TP is important because you are embedding AI in your operating model. So, we are deploying a lot of that in different functions.

 

What sort of productivity benefits are you able to pass on to clients?

 

By using AI and generative AI (Gen AI), we are able to make operational data more predictive and risk-free in processing actionable insights. Our agents, who are our workforce, are more front-facing. They are co-pilots to the customer or to the transaction in the way where they get support and we are able to put higher-quality interaction and faster resolution to the problem that comes to us.

 

From value pricing, there is a lot of focus on moving from models that are more transactional to those that are more outcome-based, creating value and a higher user interface, and those are passed on to the business. An important change my clients are feeling is that we are moving from service-level agreements (SLAs) to business impact. You are embedding that for more outcomes where we pass domain outcomes like fraud deduction and revenue uplifts back to the client.

 

Can you share some examples of how you have moved up the value chain?

 

First, AI is embedded in live delivery and not pilots from a TP standpoint. AI is integrated into day-to-day work both in customer experience and operations of global capability centres (GCCs). Some of those include automating quality checks, intelligent routing, and real time insights, which are delivering measurable productivity gains. The second is the whole workforce model where AI supports agents and supervisors to decide on their next best action, sentiment detection, and coaching cues, which were earlier more human-led. Today I’m partnering predictive models to do that. And third, workforce-management automation is eliminating transactions. It is changing the revenue mix because low-value transactional work is getting reduced by high-margin work around customer experience. The upper end services, which are the GCC services, are coming up. They are helping us evolve from repetitive tasks to more AI-augmented support, which will have an impact on revenue per person.

 

How are you engaging with the GCCs?

 

We are working on their transformation due to the spread we have. We have good places where we can help in setting up GCCs through basically AI-led automation and operation models because the spread helps them do that. We have a good spread within the country, so we can bring in. We are also looking at a lot of work around digital data and analytics operations, which have customer insights, revenue analytics, and decision support.

 

How does the billing model change in this environment?

 

The model of billing is moving out from full-time equivalents (FTE) to outcome capacity billing. We are rebasing the FTEs, reducing the lower value effort, automating tasks, and freeing the time of resources for complex tasks. The fees are more for platform fees or in terms of an outcome-based model where you give benefit to the client and take a share of it, which is like the gain-share model. We will start seeing a lot of this impact coming to the margin profile of the services that we really do.

 

With humans and agents working together, how will it reshape your hiring strategy?

 

The important part is how I build a team that is capable of putting in AI skills -- a team which has future skills such as people who understand AI, augmented operations, digital CX analytics, and newer product lines that we are building our muscles on. We’re moving away from classroom learning to inflow work with the use of AI books -- real client problem solving as part of day-to-day training. We are trying to work and build AI literacy at scale where we start with common AI foundation rules to demystify AI and data automation, and this creates value where human judgment remains critical.