Tiger Analytics has undergone a major evolution, transforming itself from an analytics specialist into a full-stack AI transformation partner capable of engineering end-to-end value. In an extended conversation, Senganal Thirunavukkarasu (Head of Enterprise Tech) and Harish Seetamaraju (Senior Director, Analytics Consulting) outlined how Tiger Analytics is redesigning AI delivery models, enabling enterprises to scale generative and agentic AI far beyond prototypes.

Their central thesis: AI impact comes only when enterprises integrate data, engineering, platforms, applications, and change management holistically. AI cannot sit in isolation; it must be woven into processes, decisions, and the enterprise operating model itself.

AI Strategy Must Translate to Business Value — Not Just Model Accuracy

Harish sets the context for Tiger Analytics’s shift: “We were offering a lot of pointed AI solutions. Today, we have expanded our services across the full stack to engage with our customers more as a transformation partner.”

Simply building a model is not enough. If upstream pipelines fail or downstream user experiences are disconnected, value collapses.

Harish explains this with a demand forecasting example: “Somewhere something breaks upstream, that breaks the whole experience, the model will still run with that data, but the output is no longer meaningful.”

The remedy? Stream-aligned, cross-functional pods.

Senganal elaborates: “A better approach is a team that is stream-aligned, all focused towards a certain business goal. Having this ownership across the entire value chain makes it possible to iterate faster and build better solutions.”

Tiger’s engineering practice intentionally merges the software engineering discipline with AI development, a crucial differentiator in an industry still dominated by siloed functions.

Full-Stack AI Engineering: From Strategy and Pipelines to Applications and DevOps

Tiger’s evolution required major internal changes. “We built this whole practice around ML engineering to bring engineering maturity into the whole analytics space,”
says Senganal.

This includes upskilling talent into T-shaped and Pi-shaped engineers, creating accelerators and reusable frameworks, standardising MLOps and LLMOps practices, and building end-to-end platforms, not just models.

One example is a global confectionery major where Tiger now manages 180+ ML applications across 80+ markets with 100% SLA compliance.

Senganal highlights the complexity: “Different markets had different versions of applications… As the scale grew, it became very hard to manage. We started with standardising their workflow, and now manage around 180 ML applications.”

This is not analytics work; it is enterprise engineering at scale.

Agentic AI: Moving Beyond Curiosity to Deployment

The last two years have driven a shift from experiments to execution.

Harish notes: “There is a sense of POC fatigue. People are tired of experiments, now they want to see: can I actually deploy this?”

This demand has pushed Tiger into building production-grade AI platforms from day one.

Senganal describes a major insurance engagement: “They wanted to build an internal AI-assisted developer platform. There was no POC. Right from the get-go, it had to be built for production.”

Stream-aligned pods focused on platform architecture, guardrails and AI gateways, use case onboarding, and innovation and experimentation.

Within three months, Tiger delivered the MVP and enabled multiple business units to build solutions on top.

Scaling AI Across Geographies: Lego-Like Accelerators

Scaling AI isn’t just about throughput; it’s about localisation, compliance, data quality, and market context.

Senganal explains: “A model built for one market will need tweaking for another. The data is different, the metrics they care about are different.”

Tiger solves this through composable accelerators: “We build Lego blocks. Depending on the market, we slot in the right blocks. Because they come from the same base, it is easier to maintain over time.”

An example: a planogram compliance AI app where markets differ wildly, from low-end phones and no Wi-Fi in Latin America to cloud-first workflows in North America.

Driving Human Adoption: Culture, Skills, Trust and Explainability

Harish stresses that AI transformation requires reversing the traditional priority order:

“To make the strategy effective, the order of priority reverses; you need the operating model, people, and processes in place.”

Key elements include multi-level stakeholder engagement, clear communication of why changes matter, AI explainability, human-in-the-loop guardrails, early adopters as evangelists and upskilling and literacy programmes.

Senganal adds: “There is no single answer; one size fits all does not make sense. It depends on maturity, constraints, and business impact.”

Where AI Is Accelerating: Customer, Tech, Unstructured Data Functions

Tiger sees the fastest adoption of generative and agentic AI in:

• Sales and marketing

• Customer service and contact centres

• IT service management and developer productivity

• Product management and engineering operations

• Knowledge management workflows

Harish observes: “All of these are language-driven, very conducive to becoming more agentic.”

Meanwhile, sectors like manufacturing and oil & gas are experimenting, but are still early in scaling.

The Next Frontier: AI That Reimagines Enterprise Processes

The final part of the conversation reveals Tiger’s larger philosophical shift.

Harish explains the enterprise evolution: “We used to see AI placed within a certain part of the organisation. Now AI can redefine how the enterprise operates, how the business is conducted.”

This is no longer about fixing a function. It’s about reimagining workflows through agents.

He gives a supply chain example: “We used to talk about demand planning systems, now we are talking about self-adjusting supply chains, where an agent monitors inventory, another agent forecasts, another predicts stockouts, another places orders.”

This shift from point solutions to AI-native enterprise design is what Tiger sees as the biggest opportunity ahead.

Conclusion

In its journey from analytics specialist to enterprise AI transformation partner, Tiger Analytics has embraced an essential truth: real AI value emerges only when you re-architect how data flows, how teams collaborate, how decisions are made, and how technology is delivered.

Through stream-aligned teams, reusable accelerators, LLMOps guardrails, AI gateways, and deep investment in change management, Tiger is redefining what AI-at-scale looks like, moving enterprises beyond POC fatigue into a future where AI reshapes entire processes, not just isolated use cases.

As Harish puts it, “Instead of a single-pointed AI solution, how can we now start reimagining the entire process with AI? Where do agents fit in? Where do humans fit in?”

With enterprises now seeking AI-native operating models rather than isolated models, Tiger’s integrated philosophy is positioning it at the centre of the next great transformation wave.

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