Beyond automation: How Axis Max Life is building the future of intelligent insurance

As AI reshapes industries, life insurers are moving beyond traditional digitisation toward intelligent ecosystems that can personalise experiences, automate complex decisions, and proactively serve customers. Axis Max Life Insurance is among the organisations pursuing this shift, embedding AI and data-driven intelligence across underwriting, claims, servicing, and customer engagement.

For Mukul Jain, CTO of Axis Max Life Insurance, the challenge is no longer about digitising insurance. It is about reimagining insurance as an intelligent, always-on experience powered by cloud, data, AI, and increasingly, Agentic AI.

That transformation is already producing measurable results.

Axis Max Life recently achieved its best-ever customer Net Promoter Score (NPS) ranking in the HANSA Research survey and reported an industry-leading claim settlement ratio of 99.8%. The company has also introduced a three-hour claim payout commitment for eligible claims, significantly compressing one of the most critical moments in the customer journey.

“Technology powers our promise of double bharosa,” says Jain. “Every transformation initiative ultimately has to improve trust, customer experience, or operational resilience.”

Reimagining insurance around the customer

Over the last several years, Axis Max Life has undertaken a large-scale modernisation effort across its technology ecosystem.

At the centre of this transformation is Tejas, the company’s next-generation claims platform that combines workflow automation, document management, and intelligent processing capabilities. Even documents such as death certificates are automatically scanned, validated, and processed, reducing manual intervention and accelerating claim settlement timelines.

But claims are only one part of a broader digital transformation story.

The company launched its customer app last year and recorded nearly 1 million downloads within 6 months, while maintaining an app store rating of approximately 4.8. Today, 80-85% of all customer servicing interactions are fully digital, delivered through the company’s website, mobile app, and multilingual WhatsApp platform.

Underpinning these customer experiences is a cloud-first architecture. A majority of Axis Max Life’s technology workloads now run on cloud infrastructure, enabling greater agility, resilience, and scalability, as well as faster innovation cycles.

“Our core platform is also on the cloud,” Jain explains. “While customers may not directly see that transformation, it provides the reliability and resilience that ultimately strengthens customer trust.”

Moving from AI experiments to business outcomes

As AI adoption accelerates across industries, Jain believes organisations often make a critical mistake: deploying AI because it is fashionable rather than because it solves a measurable business problem.

At Axis Max Life, every AI initiative begins with a clear business objective.

“If a use case is not impacting a business metric, we don’t even select it,” he says.

The company’s AI strategy is built around three priorities: improving customer service, increasing seller productivity, and enhancing employee effectiveness.

For sales teams, the company’s AI-powered platform, mSpace, recommends next-best actions, identifies follow-up opportunities, suggests suitable products, and helps advisors personalise customer conversations.

For customers, AI is increasingly becoming an invisible but powerful service layer.

One of the company’s most advanced implementations is an AI-powered email response system. When a customer sends an email, GenAI models identify the intent, retrieve relevant information from internal systems, generate a response, and adapt the tone based on the customer’s context.

If the customer raises a general inquiry, the system responds accordingly. If the customer is unhappy, the system recognises the sentiment and responds with empathy.

Yet what makes the implementation particularly noteworthy is the governance layer behind it. A second AI agent independently validates the response generated by the first model before it reaches the customer.

“We have one model generating the answer and another model validating the answer,” says Jain. “The accuracy is being confirmed before it goes out.”

Today, nearly 30% of customer emails are automatically responded to through this framework.

The rise of agentic insurance

While generative AI has captured most of the industry’s attention, Jain believes the next evolution will be agentic AI.

Unlike traditional AI systems that operate independently, agentic AI introduces orchestration, allowing multiple intelligent agents to collaborate, validate outcomes, and execute workflows autonomously.

Axis Max Life already has several agentic use cases in production, including its email bot, underwriting decision-support systems, renewal-servicing initiatives, and pre-approved offer engines.

One particularly interesting implementation involves underwriting.

Every underwriting decision made by a human underwriter is evaluated by an AI model trained on historical decisions and underwriting patterns. If the AI identifies a discrepancy, it prompts the underwriter to re-evaluate the case.

The goal is not to replace expertise but to improve consistency and decision quality.

This reflects Jain’s broader philosophy regarding AI adoption.

“I think all organisations will move through a continuum—from human in the loop, to human on the loop, to eventually human out of the loop,” he says.

However, that progression will happen only where risks are low, processes are repetitive, and governance mechanisms are strong.

Looking ahead, Jain expects agentic AI to handle nearly 30% of processing activities across IT operations, customer operations, and financial operations over the next two to three years. These functions typically involve structured rules, repetitive workflows, and clear decision frameworks that lend themselves to intelligent automation.

Building intelligence on a data foundation

The company’s AI ambitions are supported by a cloud-based enterprise data lake that serves as a single source of truth across business applications.

This foundation powers fraud detection, underwriting analytics, customer servicing models, and risk assessment capabilities.

For example, AI models can identify potential fraud during onboarding by analysing video interactions, customer behaviour, submitted documents, financial records, and multiple external data sources. The system can flag suspicious cases for enhanced due diligence before policy issuance.

Similarly, predictive models are helping the company move from reactive operations to proactive decision-making by identifying customer distress signals, renewal risks, and servicing opportunities in near real time.

“Data is what allows us to react in real time rather than in a delayed fashion,” Jain explains.

The next frontier

For Jain, the future of insurance will be defined by intelligent journeys rather than isolated technologies.

Customers will increasingly expect insurance to be embedded into everyday life, available through digital ecosystems, personalised to individual life stages, and delivered through frictionless experiences that require minimal effort.

At the same time, organisations will need to balance innovation with privacy, governance, and explainability.

As AI becomes more pervasive, concepts such as AI observability, AI explainability, and data privacy will become just as important as automation itself.

“AI will continue to become more capable and more affordable,” says Jain. “The key is knowing where to use it—and where not to.”

That distinction may ultimately separate successful AI-led insurers from those simply experimenting with technology.

For Axis Max Life, the objective is clear: use intelligence not merely to automate insurance, but to make insurance simpler, faster, and more trusted. In an industry built on promises, that may be the most valuable innovation of all.