Why Contextual Industrial Data Is Critical for AI-Ready Manufacturing in India

In an interaction with Thiruamuthan, Assistant Editor at Industry Outlook magazine, Anil Bhatia, Vice President & Managing Director – India, Emerson, discusses why contextual industrial data is critical to building AI-ready manufacturing environments and how Indian manufacturers can strengthen their data foundations without disrupting existing operations.

With his extensive experience spanning operations, business leadership, and Asia Pacific markets, he emphasizes the role of secure OT-IT integration, common data standards, industrial data architecture, and leadership alignment in helping manufacturers move from isolated AI pilots toward scalable and increasingly intelligent operations.

Starting with your journey at Emerson, how has your experience across operations, business leadership, and Asia Pacific shaped your understanding of industrial digital transformation?

My journey at Emerson has allowed me to view industrial transformation through operations, business leadership, customer engagement, and Asia Pacific market growth. A key lesson has been that digital transformation is not about technology adoption alone; it is about solving operational challenges with measurable business impact.

Across markets, manufacturers face common pressures around productivity, safety, sustainability, energy efficiency, and competitiveness, but their digital maturity varies significantly. This has reinforced my belief that transformation must be practical, scalable, and grounded in plant realities.

For India, where manufacturing is diverse and fast-growing, technology must understand process context, equipment behavior, available operational/unified database readiness and eventually operator decision needs to deliver real value.

Indian manufacturers are investing heavily in AI, but many still struggle with fragmented operational data. What makes contextual industrial data critical for moving beyond AI experimentation?

AI can create real value only when it is built on reliable, contextualized industrial data. Many manufacturers have large volumes of data across machines, control systems, historians, maintenance platforms, quality systems, and enterprise applications. However, when this data remains fragmented, decentralized or lacks context, AI often stays limited to pilots and isolated use cases. Context gives raw data meaning—it connects a reading to an asset, process condition, operating event, safety implication, or production outcome. For Indian manufacturers, contextual data is the bridge from isolated pilots to comprehensive scale. That will progressively enable AI to support real-time decision-making, predictive maintenance, energy optimization, quality improvement, and more autonomous operations.

Manufacturing environments generate enormous volumes of machine, process, and enterprise data. Where do companies often fall short in turning this data into meaningful operational intelligence?

The biggest gap is usually not data availability, but data usability. Manufacturers collect vast information from machines, sensors, control systems, and enterprise applications, yet it often remains siloed, inconsistent, or uncontextualized within/across functions. Another challenge is treating data programs as IT initiatives rather than business transformation efforts. Operational intelligence requires process experts, reliability engineers, automation teams, IT, and business leaders to work together. Standardization is equally important because different plants may use different tag names, data structures, and reporting formats. To generate meaningful intelligence, companies must focus on clean data, operational context, common standards, and clear business outcomes.

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As factories become more connected, how can manufacturers bring OT and IT data together without losing the operational context needed for accurate AI-driven decisions?

Bringing OT and IT together is essential, but manufacturers must preserve the meaning of operational data. OT data reflects the physical plant—equipment, control loops, processes, safety systems, and operating conditions. IT data brings business context from planning, inventory, quality, finance, and supply chain systems.

The convergence data between IT and OT needs to be linearized. IT typically has high volumes of enterprise data, which might not be relevant for OT, whereas OT will need to follow the Defense in Depth Strategy to secure plant/process/machine data and only relevant integration with IT - tight implementation of policies and procedures. When integrated correctly, the combined view helps improve decisions across production and business performance. However, a vibration trend or temperature reading is useful only when linked to the asset, process stage, operating state, and production objective.

Manufacturers therefore need secure industrial data architecture, common models, and strong governance across cybersecurity, ownership, access, and standards.

AI models are only as useful as the data feeding them. What should manufacturers prioritize to improve data quality, consistency, and context across plants and production systems?

Manufacturers should treat data as a strategic operating asset, not a by-product of automation. The first priority is to define clear standards for data collection, naming conventions, asset hierarchy, metadata, quality checks, and governance. Critical assets and process variables must be connected, monitored, and structured consistently across plants. Context is equally important—data should be linked to equipment, operating states, maintenance history, quality outcomes, and production events. Domain experts must be closely involved because operators, maintenance engineers, and process specialists understand what plant data really means. A phased approach works best: start with high-value use cases, prove impact, and scale the data foundation over time.

Different plants often use different equipment, control systems, and data architectures. How can manufacturers create a common industrial data foundation without disrupting existing operations?

Many manufacturers operate heterogeneous environments, especially in India, where plants have evolved through expansions, brownfield upgrades, and different technology choices. The objective should not be to replace every existing system, but to create a common industrial data layer that connects, normalizes, and contextualizes information across equipment, controls, and applications. This requires open, scalable, and interoperable architecture supported by standard models for assets, alarms, performance metrics, maintenance, and energy data. A practical route is to begin with critical production lines or high-impact assets, demonstrate measurable value, and then replicate the approach across sites without disrupting ongoing operations.

Also Read: Navigating Change in Automation Machinery Manufacturing

From your experience across power and water solutions, how can contextual industrial data help manufacturers improve efficiency, reliability, and operational excellence?

Power and water are sectors where reliability, safety, and efficiency are mission-critical. Contextual industrial data helps teams make better decisions by connecting equipment performance with operating conditions, process history, maintenance records, and production outcomes. For example, it can identify early signs of asset degradation, support predictive maintenance, and highlight abnormal energy consumption patterns before they affect performance. In infrastructure-heavy sectors, even small improvements in uptime, efficiency, or resource use can create significant operational and sustainability benefits. The same principle applies across manufacturing: contextual data helps teams understand not only what is happening, but why it is happening and what action is required.

India is moving toward more autonomous and intelligent factories. What needs to change in industrial data infrastructure before manufacturers can confidently scale AI across production environments?

To scale AI confidently, manufacturers need to move from fragmented data systems to robust industrial data infrastructure. This means reliable connectivity across assets, secure OT-IT integration, standardized data models, contextualization, and strong governance. Cybersecurity must be embedded from the start because connected factories require secure data movement, access controls, network segmentation, and clear policies. Another important shift is from project-led digitalization to platform-led transformation. Instead of running isolated AI pilots, companies should build reusable data foundations that support multiple use cases across plants. Leadership alignment is equally critical, as scaling AI also requires new skills, process redesign, and a culture that trusts data-driven decisions.

Also Read: From Robots to Intelligence: How India’s Auto Plants Are Changing

Looking ahead, how will the convergence of industrial data, AI, and automation reshape the role of manufacturing leaders in driving India’s next phase of industrial growth?

The convergence of industrial data, AI, and automation will expand the role of manufacturing leaders beyond output, cost, and quality.

Leaders will increasingly need to drive digital maturity, data strategy, sustainability, workforce capability, and innovation.

Competitive advantage will depend on how effectively companies use data to improve decisions across the value chain.

For India, this is a major opportunity as the country strengthens its position as a global manufacturing hub. Intelligent operations can help improve quality, reduce waste, optimize energy, and compete globally.

The leaders who succeed will combine technology vision with operational discipline, cybersecurity awareness, and people-centric change management.

What is your personal leadership mantra?

Practice what you preach, be authentic & value-driven.

What key pieces of advice would you offer for the emerging woman leaders?

• Shift from doing to enabling.
• Give feedback sooner and more directly than feels comfortable.
• Build trust before you need it.
• Get comfortable with ambiguity and imperfect information.
• Continue to reflect and correct as needed.