In this interaction with Gaurav Gupta, the discussion explores how manufacturing companies are redefining digital transformation beyond automation and IT modernization. The conversation highlights the growing role of data, AI, connected products, and intelligent decision-making in building agile and globally scalable industrial businesses. It also sheds light on the operational and cultural challenges manufacturers face as they transition from traditional, stability-driven models to adaptive, insight-led enterprises.
1. What does digital transformation mean for a manufacturing company like ELGi today?
Digital transformation at ELGi is less about digitizing yesterday’s processes and more about building tomorrow’s growth engine in a world where industrial growth increasingly depends on adaptability and intelligence, not just scale.
For ELGi, this means intentionally designing the enterprise—and the solutions we deliver—to be intelligent, connected, and outcome-led. As global manufacturing becomes more complex, long-term success is shaped by how quickly organizations can sense change, translate signals into action, and continuously improve performance for customers.
We therefore view digital as an enterprise-wide growth platform. Transformation extends well beyond factory to encompass customer engagement, product engineering, supply chain orchestration, talent development, and financial governance. Externally, this same philosophy is reflected in platforms such as ELGi Air~Alert, which moves compressed air from being a passive utility to an intelligent, monitored, and insight-driven system for customers.
These capabilities allow ELGi to scale globally with consistency while operating with regional intelligence and local relevance across key markets such as India, Australia, North America, and Europe. Digital is no longer a support function; it is a core enabler of adaptive, intelligence-led growth—both for ELGi and for the customers who depend on us.
2. What are the biggest challenges in driving digital change in a traditional industrial setup?
The primary challenge lies in transitioning from operating models built for predictability to those designed for continuous evolution and growth. Traditional industrial organizations are optimized for stability and control, whereas digital transformation requires faster decision cycles, cross-functional collaboration, and shared intelligence across the enterprise.
Siloed systems and localized decision-making slow responsiveness—internally and at customer sites. As solutions like Air~Alert begin to generate continuous operational data, organizations must evolve from reviewing information periodically to acting on insights continuously and proactively.
Adoption remains critical. Digital capabilities deliver value only when teams trust insights and allow systems to influence how work gets done.
At its core, the change is about moving from individual experience to enterprise-wide intelligence.
3. How do you ensure that digital initiatives deliver real business value and not just technology upgrades?
As we are in an evolution stage, some digital initiatives are foundational like setting up data fabric, cloud modernization etc, however, most of the digital initiatives at ELGi are anchored to a clear business outcome—accelerating growth, improving customer uptime, increasing forecast accuracy, unlocking working capital, or improving execution discipline. Technology is selected only where it materially improves how quickly and confidently decisions can be made.
4. What role is data playing in improving decision-making across the organization?
Data is becoming the foundation on which ELGi builds intelligence into everyday decision-making—internally and across customer ecosystems. Integrated data connects market signals to supply readiness, engineering decisions, service performance, and financial discipline.
In customer environments, platforms such as Air~Alert convert operational data into actionable insight—enabling earlier intervention, better energy efficiency, and more predictable outcomes. Internally, the same principles apply, moving from retrospective reporting to forward-looking, insight-led decision-making.
AI accelerates this by surfacing patterns, exceptions, and priorities at scale. As AI capabilities mature, we are also seeing the early shift toward agent-like systems—where intelligence does not just inform decisions but can recommend, prioritize, and increasingly coordinate actions across workflows. To scale responsibly, these capabilities must be built with strong data governance, cybersecurity, and human oversight.
5. What advice would you give to companies just starting their digital transformation journey?
Anchor digital transformation in the future you want to win, not the systems you want to modernize. Digital transformation should begin with a clear view of the capabilities required to compete and grow over the next several years. For manufacturers, this increasingly includes connected products, real-time visibility, intelligent decision support, and secure digital foundations.
Strong data quality, modern platforms, and governance come first. From there, AI—and eventually agent-based intelligence—can be introduced where it meaningfully improves execution, responsiveness, or customer outcomes.
Most importantly, treat digital transformation as a capability-building journey. The goal is not to deploy more technology, but to create an organization—and a customer calue proposition—that learns faster, acts earlier, and scales intelligence with confidence.
