In an industry where scale, precision, and cost efficiency are critical, how is JK Tyres using enterprise innovation to transition from conventional manufacturing models to intelligent, AI-driven factories?
At JK Tyres, we view digital innovation as a strategic enabler for building the factory of the future, one that seamlessly integrates automation, connected devices, and AI-driven intelligence. Our focus is not on replacing conventional factory management systems, but on augmenting them with real-time insights, where multiple processes are orchestrated through AI, digital twins simulate outcomes before execution, and agentic AI enables deeper, continuous collaboration between humans and machines.
The direction we are moving toward is one where machines continuously sense, learn, and adapt, allowing manufacturing operations to evolve from a largely forecast-driven model to a more demand-responsive state. In this future-ready setup, factories are designed to be self-optimising, capable of dynamically adjusting to market signals, improving efficiency, and responding to change with minimal manual intervention. For us, enterprise innovation is about creating intelligent, resilient manufacturing ecosystems that can scale sustainably while remaining agile to business needs.
Sharad Agarwal, Chief Digital and Information Officer (CDIO), JK Tyre & Industries Ltd.
With increasing pressure on automotive manufacturers to digitise operations and adopt AI-driven models, what has been JK Tyres’ approach to embedding AI across manufacturing and customer-facing processes?
Our AI journey at JK Tyres began in 2018 with a deliberate focus on getting the fundamentals right, particularly around data. We recognised early on that meaningful AI adoption is only possible when data is reliable, accessible, and consistent across the enterprise. To address this, we implemented a centralised data lake that brings together data from manufacturing systems, supply chain operations, quality processes, and customer touchpoints, creating a single source of truth. This data foundation has enabled better visibility, faster insights, and a more integrated approach to decision-making across the organisation.
Building on this strong data backbone, we have progressively introduced AI and machine learning use cases across both manufacturing and customer-facing functions. Our focus has been on applying AI where it can drive tangible impact, automating repetitive processes, improving speed and accuracy, and augmenting human decision-making. From optimising operational workflows to enhancing customer experience, these initiatives are designed to scale responsibly while aligning with business priorities. For us, AI is not a one-off initiative but a continuous capability that is steadily being embedded into how the organisation operates and evolves.
Can you share a specific AI use case that has delivered tangible business impact?
One of our most impactful AI implementations has been in warranty and claims processing. Earlier, tyre claims involved manual inspections, physical documentation, and SAP-based data entry, resulting in turnaround times of several days. Today, through a native mobile application for dealers and distributors, AI and ML engines assess tyre images, identify faults, and estimate remaining usable life automatically. This has reduced claim processing time from days to around fifteen minutes, eliminated manual interventions, and significantly improved customer and dealer experience.
In parallel, we are also leveraging IoT through our Smart Tyre initiative, where embedded sensors capture real-time data such as pressure and temperature. These data points will further strengthen predictive maintenance and claims intelligence as we continue to expand our AI-driven capabilities.
Have you been able to measure the impact of AI-enabled warranty claims, and what benefits have you seen so far?
Yes, the impact has been both measurable and meaningful. The most significant outcome has been a dramatic reduction in turnaround time for warranty claims, from several days to roughly fifteen minutes. This improvement has directly enhanced customer and dealer satisfaction, while also improving operational efficiency.
Beyond speed, the automation of inspection and data entry has eliminated many manual interventions, reducing dependency on physical inspections and minimising the risk of human error. The process is now far more consistent and scalable, enabling teams to handle higher volumes without proportional increases in effort or cost.
This initiative also reflects how we see ourselves as a technology-driven manufacturing organisation. By leveraging AI, ML, cloud platforms, and IoT, through innovations like our Smart Tyre initiative with embedded sensors capturing real-time pressure and temperature data, we are laying the groundwork for more advanced use cases, including predictive maintenance and more intelligent claims processing in the future.
How are you empowering employees to take advantage of AI-enabled processes
Reskilling and upskilling employees is a strategic initiative. It begins with a mindset wherein employees view AI as an enabler that helps them to complete repetitive, error prone tasks easily and correctly without much effort.
We have a structured program wherein we are working with a team of cross-functional people from HR, training, operations and AI experts to map the skills and role of the employees and align them with the requirements of AI tools. Naturally some may require more training, while others may not require that much based on the specific role.
People are excited about the new tools and how it is enabling them to do things faster, better and more accurately.
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