Tiger Analytics began at a time when enterprise analytics was still largely about solving focused business problems. Clients wanted help with churn, marketing attribution, forecasting, customer acquisition, and other specific use cases. Fifteen years later, that conversation has changed dramatically.
As enterprises move from data science projects to cloud-scale data modernisation and now to artificial intelligence (AI)-first transformation, Tiger Analytics is also repositioning itself for the next phase. Pooja Agarwal, Co-founder and Head of Business Operations, Tiger Analytics, says clients are no longer looking at AI merely as a pilot, proof of concept, or chatbot. They want AI in production, embedded into workflows, automation, and business value creation.
Agarwal has played an instrumental role in building and scaling Tiger Analytics since its inception. With more than two decades of experience across computer architecture, chip design, and business operations, she brings a rare combination of technical depth and operational leadership to the company’s growth journey. Before co-founding Tiger Analytics, she worked at NVIDIA as a Lead ASIC Engineer, where she contributed to the hardware design of graphics processing unit (GPU) chips and developed deep expertise in advanced semiconductor technologies.
As a founding leader, Agarwal has helped transform Tiger Analytics from a small team of data science specialists into a global organisation of over 7,000 professionals, serving large enterprises across sectors. She leads multiple business functions, including finance, talent management, marketing, and revenue operations, ensuring that the company’s growth is supported by governance, operational discipline, and scalable systems.
In a conversation with Dataquest, Agarwal spoke about Tiger Analytics’ 15-year journey, the shift from analytics to enterprise AI, India’s role in the company’s global delivery model, and why the next phase of AI services will be shaped by value, governance, and execution.
Tiger Analytics is nearing its 15-year milestone. How do you look back at the company’s journey, especially given how much the data and AI landscape has changed?
We started as a very small team. None of us, as co-founders, came from a large consulting company leadership background. So, from the beginning, we operated on first principles. We tried things, tested them, learnt from them, and stayed agile.
In the early days, the focus was largely on data science and figuring out how to get value out of data. The problems were typically smaller. Clients would come to us with specific business problems and ask how data science or advanced analytics could help solve them.
Over time, the market itself changed. We moved from data science to big data, and then to cloud and hyperscalers. Enterprises began dealing with larger volumes of data and needed help not just with analytics, but also with data transformation, cloud migration, and end-to-end solutions.
In the last two to three years, the conversation has shifted again. It is now about how AI becomes part of the roadmap and how it can become a value-creation partner. Clients are no longer interested only in AI as a pilot or proof of concept. They want AI in production, whether through automation, daily workflows, or agents working together to solve business problems.
When Tiger Analytics started, data was often seen through different lenses, such as business intelligence, analytics, and reporting. What was the client problem statement then, and what is it now?
Fifteen years ago, clients looked at advanced analytics in smaller pockets. They would ask us to solve specific problems in marketing, customer acquisition, churn, budget attribution, or forecasting. These were important problems, but they were usually contained within a function.
At that time, a company like ours was seen as a specialist partner that could solve high-end analytics problems. For larger technology or data-layer work, clients would often go to bigger IT consulting companies.
That has changed. Today, clients are asking us to solve much larger business problems. They may come with a supply chain optimisation problem, or a finance transformation problem, or a broader enterprise-level question. The scope has moved higher. They are not just asking for analytics use cases; they are asking for business outcomes.
As large enterprises move towards AI-led transformation, what are you seeing in terms of data modernisation and enterprise readiness?
Our philosophy has always been to align with the client’s roadmap rather than go in with a fixed solution that we want to sell. We typically begin with a discovery phase and try to understand the problem the client is trying to solve. We work closely with senior stakeholders, including chief executive officers, chief analytics officers, chief data officers, and equivalent leaders on the client side.
Large enterprises are very focused on getting their data foundation right. They want to make sure the data layer is properly set up in terms of governance, quality, automation, frequency of updates, and accessibility. Earlier, this was important for analytics models. Now, it is also critical for AI agents.
The context layer is also becoming increasingly important. AI agents cannot do much without the right enterprise context. That is where our experience helps, because we see similar problems across customers and we also work closely with hyperscalers and ecosystem partners to deliver these solutions.
You said clients are no longer looking at AI merely as a chatbot or tool. What does that shift mean in practical terms?
Clients are now coming with a clearer roadmap on how AI can deliver business value. AI is no longer seen only as a tool, query layer, or chatbot. Enterprises want AI to solve end-to-end business problems.
This could mean embedding AI into daily workflows, automating parts of a process, or using agents to support enterprise execution. The question is moving from whether AI can work in a limited use case to how it can create value inside the business.
That shift also makes the data foundation more important. For AI to deliver meaningful outcomes, enterprises need the right data layer, governance, accessibility, and context.
How is Tiger Analytics changing internally to stay relevant in this AI-first market?
Most of our workforce is technical. We have data scientists, data engineers, and analytics professionals. So, for us, the focus is not on turning everyone into an AI engineer. It is about making our existing teams AI-enabled.
We are running internal initiatives to help our people think and deliver with an AI-first mindset. Our leadership is focused on transforming our delivery into AI-first delivery. The idea is to look at everything from an AI lens and ask how we can deliver more value to clients using the right AI-led approach.
We are taking both a top-down and bottom-up approach. From the top down, we want our proposals and solutions to include AI wherever it can create value. From the bottom up, we are upskilling employees so that they think of themselves as part of an AI-first company.
Tiger Analytics has grown into a global organisation of over 7,000 professionals. How do you differentiate yourself in a market where large IT services firms and specialist analytics firms are also competing aggressively?
We continue to see ourselves as a specialist AI and analytics company. Our strength comes from the depth of our data science, data engineering, and analytics capabilities, and from the fact that we have worked on these problems across industries for many years.
The differentiation is not just in saying that we can build AI solutions. It is in understanding the data layer, the business problem, the operating context, and the path to value. We have grown significantly, but we have tried to maintain the agility and problem-solving culture with which we began.
Our clients trust us because we have travelled with them across different waves, from data science and advanced analytics to cloud migration, and now to AI-led transformation.
What role does India play?
India is very important to Tiger Analytics’ global delivery and talent model. A large part of our workforce is based in India, with Chennai being one of our major hubs, along with teams across other locations and remote delivery models.
India supports global clients in a significant way. We also work with some India-based clients and global capability centres (GCCs). However, our business remains largely driven by global enterprise markets.
India gives us access to strong analytics, engineering, and AI talent. As the market evolves, the role of India is also evolving from delivery to deeper problem-solving, innovation, and AI-led transformation support for global clients.
Which industry verticals are driving demand?
We have traditionally been very strong in consumer packaged goods (CPG) and retail, and we continue to see strong momentum there. These sectors have been early adopters of data-led transformation because the business value is often visible in areas such as demand forecasting, supply chain, pricing, promotions, customer engagement, and marketing effectiveness.
We are also seeing strong growth in banking, financial services, and insurance (BFSI), pharma, life sciences, and other enterprise sectors. Pharma, in particular, is seeing a lot of movement around AI adoption.
Across industries, the use cases may differ, but the underlying demand is similar. Clients want to use data, analytics, and AI to improve decision-making, operations, finance, supply chains, human resources, and customer experience.
Are some sectors moving faster than others when it comes to AI adoption?
Yes, adoption patterns differ by industry. CPG and retail have moved faster in many areas because the business use cases are clearer and the value can be demonstrated more quickly.
BFSI has also been an early adopter of technology, but it is more compliance-focused. There are stronger requirements around governance, validation, internal quality assurance, and regulatory checks. So, transformation can be slower, even though the appetite for technology is strong.
We are also seeing useful AI use cases across functions such as human resources and finance. These areas are attracting interest because the business value can be clearly demonstrated.
Do you see AI exposing deeper data problems inside enterprises?
Yes. As enterprises move towards AI, they are realising that the quality of outcomes depends heavily on the quality of their data and context. AI can expose gaps in data foundations, governance, accessibility, and business process clarity.
For AI to work well, enterprises need strong data foundations, updated data, proper governance, and the right context layer. Without that, AI agents or models will not deliver meaningful value. So, in some ways, AI is forcing enterprises to look more seriously at the data layer and the operating discipline around it.
What are Tiger Analytics’ priorities for the next 18 to 24 months?
One of our top priorities is to continue being at the forefront as an AI-first services partner. We are already a large pure-play AI and analytics company, and we want to maintain that leadership as the market evolves.
Another focus is to ensure that AI strengthens our value proposition. There are questions in the market about how AI will affect services companies. We see AI as a growth opportunity, not as a threat. Our aim is to show that we can deliver more value to clients by using AI across our services and solutions.
We are also thinking carefully about the company’s future growth path, including a possible initial public offering (IPO) roadmap. Timing will matter, given market volatility and how investors look at AI-led services companies. The goal is to position Tiger Analytics in the right way for the next phase of growth.
As the data and analytics industry moves into the AI era, what do you think will define the next phase?
The next phase will be defined by value creation. Enterprises will not be satisfied with experiments alone. They will want AI to work inside the business, improve processes, support decisions, and deliver measurable outcomes.
Data foundations, governance, context, and domain understanding will matter even more. AI cannot be separated from these layers. Companies that can bring all of this together, data, analytics, AI, business context, and execution, will be better positioned to help enterprises move from experimentation to impact.
This article was originally published in DATAQUEST on June 19, 2026.
