A major shift is unfolding in India’s cities as TK Elevator’s MAX platform uses artificial intelligence to turn elevators into predictive, self-diagnosing systems. MAX analyses millions of hours of operational data—door cycles, motor vibrations, and power patterns—to detect failures before passengers notice. This technology proves vital for places like hospitals and airports, where reliable vertical mobility is key. CEO & MD Manish Mehan explains how MAX modernises India’s buildings with cloud-enabled intelligence and reshapes the meaning of smart infrastructure in a fast-growing nation.
CEO & MD
TK Elevator India
CIO&Leader: How is AI embedded in TKE’s MAX platform transforming elevator and escalator maintenance from reactive to predictive?
Manish Mehan: TKE’s MAX is a data-driven digital solution that enhances elevator reliability and efficiency by leveraging real data collected from numerous connected units worldwide. The role of AI in TK Elevator’s MAX platform is fundamentally transforming elevator and escalator maintenance by shifting it from a reactive, breakdown-driven approach to a predictive and proactive one. MAX continuously collects real-time operational data from connected equipment, such as performance metrics, usage patterns, error codes, and component behaviour, and securely transmits it to the cloud.
Advanced AI and machine-learning algorithms analyse this data against millions of operating hours across the global TK Elevator portfolio to detect patterns, anomalies, and early signs of wear that conventional inspections do not reveal. Instead of waiting for a failure, the system predicts potential issues in advance, enabling maintenance teams to intervene at the right time.
This predictive intelligence allows technicians to arrive on site with the right insights, tools, and spare parts, or in some cases, resolve issues remotely, significantly reducing downtime and unplanned outages. As a result, maintenance becomes condition-based rather than schedule-based, improving equipment reliability, extending component life, optimising service resources, and enhancing passenger safety and experience.
CIO&Leader: What specific machine-learning signals does MAX analyse to predict failures and reduce downtime in Indian buildings?
Manish Mehan: MAX uses real-time operational, behavioural, and performance signals from elevators and escalators to predict failures and reduce downtime in Indian buildings. It monitors door movements, trip counts, usage intensity, and fault codes—early indicators of wear, component stress, and system issues—so that maintenance can be proactive, particularly in high-traffic settings.
The platform also monitors power-up and reset patterns, as frequent restarts can indicate underlying control or power-supply issues. In addition, performance data from critical components such as motors, brakes, and sensors, including vibration, acceleration, and response behaviour, is analysed to assess wear trends and remaining useful life.
By correlating these signals with historical data from a large global fleet operating in similar conditions, MAX’s machine-learning models identify subtle deviations from normal behaviour, enabling early alerts and targeted maintenance actions before failures occur, thereby significantly reducing unplanned downtime.
CIO&Leader: How does cloud connectivity and real-time data analytics improve safety and performance across high-traffic infrastructure like airports and hospitals?
Manish Mehan: Cloud connectivity and real-time data analytics significantly improve the safety and performance of elevators and escalators in high-traffic infrastructure such as airports and hospitals by enabling continuous monitoring, faster response, and data-driven decision-making. With cloud connectivity, sensor data from every connected unit is securely streamed to centralised servers, where advanced analytics and machine-learning models continuously process it rather than in periodic batches.
This real-time visibility allows operations teams to detect anomalies or performance degradation instantly, whether it’s unusual vibration in a lift motor, repeated door-open delays, or subtle control system errors, and trigger alerts before passengers are affected. In environments like airports, where delays can cascade through tight travel schedules, and hospitals, where reliable vertical transport is essential for emergency transfers and patient movement, early detection of potential faults means issues can be resolved proactively with minimal disruption.
Cloud analytics enables predictive maintenance aligned with facility needs. Instead of scheduled or emergency repairs, maintenance is performed only when needed, reducing breakdowns and extending device life. Centralised dashboards also enable managers to track assets and optimise resources, resulting in safer, more reliable vertical transportation in busy buildings.
CIO&Leader: In India’s context, what challenges did you face while scaling IoT-enabled vertical mobility across legacy and new buildings?
Manish Mehan: For property managers and building owners in India, scaling IoT-enabled vertical mobility solutions like TK Elevator’s MAX across both existing and new buildings involves several practical and market-specific challenges. A primary obstacle is retrofitting older elevator systems, many of which predate IoT considerations. Integrating sensors, connectivity hardware, and control logic into these units often requires structural modifications, custom interfaces, and precise calibration to ensure reliable data collection and cloud communication, thereby increasing project complexity and cost.
Existing infrastructure in numerous high-rises and older commercial properties also lacks standardised digital interfaces, making seamless sensor integration and real-time data transmission more difficult than in new installations built with digital readiness in mind. In the Indian market, cost sensitivity among property owners and smaller developers can further slow adoption of IoT-enabled solutions, as the perceived investment in smart predictive platforms may be weighed against more immediate budget priorities, even when long-term operational savings are significant.
A limited technical workforce and state-to-state regulatory variability present challenges for implementing new digital systems at scale. These factors require tailored technology approaches as TK Elevator expands IoT-enabled maintenance and vertical mobility solutions in India.
CIO&Leader: How is AI-driven vertical mobility contributing to smarter, more energy-efficient buildings and improved tenant experience?
Manish Mehan: AI and connected technologies are playing an increasingly important role in transforming buildings into smarter, more energy-efficient, and more user-friendly spaces. In the case of mobility equipment, by continuously analysing data from connected elevators and escalators, our digital systems, supported by AI, can optimise their operation in real time rather than relying on static schedules or manual adjustments. For example, intelligent dispatch algorithms can group and route cars based on current traffic patterns, reducing unnecessary starts and stops, lowering power consumption, and minimising wait times during peak hours. When buildings experience fluctuating demand, the system can also adjust motor torque, drive profiles, and door-opening timing to improve ride smoothness and reduce energy use without compromising performance.
In the context of energy management, our digital technologies help vertical transport systems work in harmony with the broader building management system (BMS). Based on AI-analysed usage insights during peaks and idle periods, elevators can be set to energy-saving modes when traffic is low (such as late nights in office towers) or when overall building energy demand is high. This coordination not only cuts operational costs but also supports sustainability goals—an increasingly important factor for green building certifications and tenant satisfaction.
From a tenant and visitor perspective, predictive analytics and AI-supported diagnostics reduce unexpected breakdowns and improve uptime, meaning users spend less time waiting or encountering out-of-service equipment. Our algorithms also help personalise experiences, such as destination control systems that reduce crowding and enhance flow in high-rise residential and commercial buildings, making movement within the building feel smoother and more intuitive. Together, these benefits contribute to a more comfortable, reliable, and efficient environment that enhances tenant experience while helping building owners manage costs and reduce environmental impact.
CIO&Leader: From a technology standpoint, which advances in AI, analytics, or connectivity will most significantly redefine vertical mobility systems over the next few years?
Manish Mehan: From a technology standpoint, the next few years will see vertical mobility systems redefined by advances in artificial intelligence, analytics, and connectivity that make them far more autonomous, responsive, and integrated with smart buildings. AI and machine-learning models will evolve from rule-based diagnostics to self-learning systems capable of continuously improving their predictions by analysing vast, diverse datasets, enabling earlier and more precise detection of anomalies and performance degradation.
Enhanced connectivity through 5G and future wireless standards will support low-latency, high-bandwidth data exchange, enabling richer sensor inputs and more reliable remote management.
Finally, tighter integration with building management systems, supported by explainable AI and stronger cybersecurity frameworks, will enable vertical mobility systems to operate as intelligent parts of the broader building ecosystem, delivering higher efficiency, improved reliability, and a better user experience.