As AI moves from pilot projects to core infrastructure, insurance technology leaders are rethinking everything from software delivery to customer distribution. In this conversation, CIONOW explores how Zinnia is embedding AI into its engineering workflows, modernizing operations without compromising stability, and positioning its India team as a global innovation hub, not just a delivery center.
How is AI changing the insurance technology stack in practical terms?
In practical terms, AI is becoming part of the technology stack itself, not a separate layer of experimentation. At Zinnia, we are building an in-house coding-agent platform that is pre-skilled on the technology stacks used by different engineering teams, so developers can work with agents that understand more of the context in which they build.
That internal capability is complemented by third-party tools such as Claude Code and Cursor, which we are standardizing for engineering use. The goal is to give developers the best combination of enterprise context and frontier-model capability so they can code, test, troubleshoot and deliver more efficiently. The broader shift is from AI as an occasional productivity tool to AI as an embedded part of software delivery and business processes. Productivity matters, but the real measure is whether it helps us shorten cycle times, improve quality and create better outcomes and more value for our clients and customers.
What AI capabilities will distinguish leading insurers to personalize distribution, anticipate customer needs, and deliver the right offerings responsibly?
AI can play a significant role by ensuring the usage of data insights and intelligence more effectively to understand consumer personas, anticipate needs and match offerings to the customers they are most relevant for. The opportunity is not simply to automate existing processes, but to make distribution more targeted, more personalized and ultimately more effective. The organizations that standout will be those that can bring together data, AI and distribution capabilities to improve how products are positioned surfaced and delivered while doing so responsibly and with the right governance
How has the role of a technology leader in the insurance sector changed in recent years?
What has changed most is the expectation of what technology should do for business. It is no longer enough to make an old process a little bit faster. The big chance now is to use technology and AI to get rid of entire layers of hard work, friction and manual steps where those steps do not add value. This lets people stay focused, on the decisions and interactions that truly need expertise.
Operations and the call center are examples. AI assistants that know the SOP knowledge can help employees learn faster cut down training time and solve questions quickly. The even bigger chance is to go past just helping the employee and redesign the workflow. This way routine steps, look-ups and hand-offs can be finished automatically. This reduces handling time and, in some cases, removes the effort altogether.
That is the shift toward agentic operations: moving from manual indexing and hand-offs to AI-led automation and workflow orchestration. It requires technology leaders to think across process design, data, governance and change management, and to ask not only how technology can improve a process, but which parts of the process should no longer require human effort at all.
Where is Zinnia India focusing most of its technology investment today?
A major focus for us is expanding the technology and data capabilities behind Zinnia’s products and platforms, including Zinnia Live, Zahara and Zinnia Market Connect, and using automation and AI to improve how insurance is distributed, serviced and operated. This is not about AI as a standalone experiment. We already have capabilities in production, and our dedicated AI team is focused on advancing them further and applying AI where it can create measurable business and customer value.
Alongside the product work, we are investing in AI-enabled productivity across our teams. Engineers and other team members are using frontier models and tools such as ChatGPT, Codex, Claude, Claude Code and Cursor to accelerate problem-solving, coding and delivery. The objective is not simply to do the same work faster, but to materially improve speed, quality and outcomes while giving teams more capacity to focus on higher-value problems.
How are you approaching modernization while keeping core systems stable and compliant?
Modernization in insurance needs to balance speed with stability, governance and regulatory requirements. At Zinnia, one part of that modernization is building AI-powered enterprise capabilities that can support operations across areas such as call centers, IT operations, product design and HR operations.
The objective is to have machines handle the work that does not require human judgement, and free people to focus on the areas that need deep knowledge, specialist expertise or core decision-making. That means faster turnaround times and query resolution, fewer unnecessary hand-offs and dependencies, and more capacity directed toward higher-value work.
We are approaching this in a phased way, with the right guardrails around data, governance and compliance so that innovation does not come at the expense of resilience or trust
What are your biggest priorities as CEO of Zinnia India right now?
One of my biggest priorities is scaling the impact of the technology and innovation capability we have already built in India. We are well beyond an execution-only model today, with teams here contributing across technology, product, data, AI and broader business outcomes. The opportunity now is to deepen that ownership and increase the impact India has on Zinnia’s global priorities.
I would go further than viewing this only through the traditional GCC lens. India is already a global innovation capability for Zinnia. My focus is on bringing together strong engineering, product, data and analytics talent, giving teams ownership of problems with global impact, and creating the environment, leadership depth and accountability that allow that talent to operate at its full potential.
What is the most important leadership lesson you have learnt while building and scaling teams?
One of the most important lessons I have learnt is that great talent creates the value when people are put in a position to use their skills to the fullest. Leadership is not about finding strong people. Leadership is, about creating the environment, clarity and ownership that let people apply that strength to meaningful problems. India has talent across technology, product, data and analytics. When people are trusted with problems given real accountability and support people work at the edge of their capabilities.
The impact can be significant. My role is to remove the barriers that get in the way of that and make sure our strongest people are spending their time where their skills can create the more value for organizations building in India, talent alone is not the differentiator. The advantage comes from how effectively you enable that talent to operate experiment, make decisions and own outcomes.
