Hitachi's Industrial AI for Mission-Critical Infrastructure

From the rise of water and steam-powered factories in the late 18th century to the breakthroughs of mass production, automation and digital transformation – manufacturing has continually evolved through dedicated innovation.

Today, the industry is entering the age of cognitive manufacturing, where AI goes beyond automating processes to deliver intelligent prediction, faster decision-making and advanced problem-solving capabilities.

While many AI providers are rooted purely in the digital landscape, Hitachi Group brings more than 100 years of expertise in designing and powering the world’s critical physical infrastructure, combining industrial heritage with cutting-edge AI innovation.

Yuriy Yuzifovich, Premkumar Balasubramanian and Ram Ramachander are leaders across the Group and shared their expertise on industrial AI with Manufacturing Digital.

Read the full story in the May 2026 edition of Manufacturing Digital.

Responsible for shaping the vision, strategy and growth roadmap for AI at GlobalLogic, Yuriy pioneers the use of autonomous, knowledge-grounded agents.

Premkumar Balasubramanian, Chief Technology Officer and Head of AI at Hitachi Digital Services

Premkumar first joined Hitachi Digital Services in 2020 where he leads technology strategy and innovation across cloud, data, IoT and AI.

Ram joined Hitachi through its Consulting arm in 2014 and now leads group growth initiatives across the EMEA region alongside leading efforts toward carbon neutrality.

Why is it important to ensure industrial AI is safe?

Yuriy: The stakes in industrial AI are fundamentally different than in general enterprise IT. If a standard AI chatbot hallucinates, you get a poorly written email or a bad marketing draft. If an industrial AI hallucinates, you risk damaging multi-million-dollar equipment, halting a production line, driving up scrap rates, causing environmental damage or - worst of all - compromising the physical safety of workers on the factory floor.

Because industrial AI operates in the physical world, it cannot simply be probabilistic; it must be highly reliable, deterministic and fully auditable. Furthermore, safety goes beyond just preventing AI errors - it requires an ironclad cybersecurity posture. Even the most perfectly reasoned AI system must be hardened against adversarial attacks to ensure the physical machinery it controls cannot be maliciously manipulated. 

Premkumar: Industrial AI operates in mission critical, physical environments – not digital sandboxes. We’re embedding AI into power grids, transportation systems, factories and heavy equipment. In these settings, failure isn’t an inconvenience; it’s a safety, reliability and economic risk. Unlike consumer or enterprise AI, errors, hallucinations or unpredictable behaviour are unacceptable. A failed transformer, a missequenced control action or a missed anomaly can cascade into outages, safety incidents or regulatory violations. That’s why industrial AI must be engineered for determinism, resilience and failsafe operation from day one. At Hitachi, this mindset comes naturally – we’ve spent decades building and operating the systems that society depends on. 

Ram: Ensuring industrial AI is safe is essential because, unlike in other areas of frontier tech application, there is no room for a 'fail fast' approach in critical industrial environments. Errors in factories, power grids or rail networks can lead to catastrophic billion-dollar losses, severe environmental damage and, most critically, impacts to human life. 

How can this be achieved?

Yuriy: We achieve this through a fundamental shift in AI architecture. Instead of treating Large Language Models (LLMs) as the "source of truth" or the core reasoning engine - which is exactly what leads to hallucinations - we use them strictly for what they do best. 

We harness their immense power to process messy, unstructured data, extracting explicit knowledge from a massive variety of PDF manuals and maintenance reports, alongside tacit knowledge captured from handwritten notes and direct communication between experts and AI. This is all transformed into a strict, deterministic knowledge base. We process and validate this knowledge heavily in the cloud with human experts in the loop. Only then do we push the highly verified, compiled intelligence down to the edge - right to the factory floor - for real-time execution. 

At the edge, we use formal logic for core intelligence and traditional machine learning for processing sensor data. In addition, we use this formal logic as a safety harness around decisions from generative and world models. These models can still provide creative solutions to unforeseen circumstances, but they must be strictly validated for safety before execution. Finally, we use LLMs for human-machine communication. We call this hybrid AI framework, pioneered by my team at Hitachi, Reliable AI. 

Premkumar: Safe industrial AI is achieved through engineering rigour and architectural grounding, not probabilistic optimism. First, AI must be designed into the system, aligned with physics, control theory and operational constraints – not bolted on as an opaque model. This is where Neurosymbolic AI becomes critical. By combining data driven learning with symbolic reasoning, rules and domain knowledge, we can ensure models are grounded in first principles, operating envelopes and known system behaviour. 

Second, we apply formal validation and verification techniques – using symbolic constraints to prevent unsafe actions, enforce invariants and operating limits and validate model behaviour before and during deployment.  

Third, AI must operate within human-in-the-loop and human-on-the-loop architectures, where accountability and authority remain clear. 

Finally, this is reinforced by continuous monitoring, explainability and lifecycle governance, so models remain reliable as systems, data and conditions evolve. 

In industrial environments, learning alone is not enough. Trust comes from AI that can reason, explain and prove its behaviour – especially when the cost of failure is measured in safety, uptime and societal impact. 

Ram: Realising AI safety necessitates a rigorous, engineering-led approach which in turn requires meticulous model testing, transparent procurement, full auditability and secure deployment capabilities. Alongside this, human-in-the-loop design and a holistic focus on functional safety and cyber resilience is needed, designing for security from the ground up to protect critical infrastructure.

What makes industrial data fit for purpose for AI? 

Yuriy: Manufacturing environments are becoming exponentially more complex, driven by a massive proliferation of sensors - both built natively into new equipment and retrofitted onto legacy lines. The sheer volume and velocity of this telemetry have surpassed human cognitive limits; even with the best training and experience, it is no longer possible to manage these systems safely without AI assistance.

However, raw sensor data alone isn't truly "fit for purpose" until it has context. This is where a deterministic knowledge base serves as the critical connective tissue. It unifies the high-speed streaming data from the factory floor with the historical context trapped in manuals, quality control logs and maintenance histories, as well as unstructured "tribal knowledge". By processing this diverse data together, the AI transforms raw noise into decisive action - such as knowing exactly when to safely halt a line to preempt an imminent, predicted malfunction. 

Premkumar: Industrial data is purpose built by nature. Unlike open or unstructured enterprise data, industrial environments generate high-fidelity, time synchronised, contextualised data from sensors, control systems, historians and maintenance systems. We know what data is needed, how it’s generated and what decisions it should inform. This allows AI models to be trained with intent, grounded in physics and process understanding. The result is higher signal-to-noise, faster deployment and far more reliable outcomes. 

Ram: Industrial data becomes fit for purpose for AI when it is effectively managed through advanced unified platforms such as Hitachi's Lumada Unified Data Layer (UDL). The UDL is crucial for AI in manufacturing as it works to connect disparate operational systems and then normalises and contextualises their data. This process, often enhanced by AI agents and validated by human domain experts, transforms raw data into consistent, actionable intelligence essential for scalable and repeatable AI applications.

How is AI changing manufacturing today?

Yuriy: Right now, it is all about human-machine teaming. We are shifting from AI acting as a back-office analytics tool to functioning as an active "co-pilot" on the factory floor. By connecting the cloud and the conveyor belt, we are empowering frontline operators and maintenance personnel. When an anomaly occurs, an operator no longer has to spend hours digging through manuals or jumping between disparate IT systems to pull machine-specific maintenance logs, quality standards or replacement histories. Instead, they can interact directly with an AI agent at the edge. This agent not only provides real-time, validated diagnostic steps based on the exact state of that specific machine, but also takes action - seamlessly helping to create procurement requests and schedule the required maintenance. 

My team is currently working with one of the largest manufacturers of personal care products to implement exactly this capability on the floor. We are designing the system to deliver a multidimensional impact: reducing downtime, cutting waste and lowering equipment maintenance costs. But just as importantly, by supercharging these workers with an AI co-pilot, our goal is to ensure much more accurate maintenance records while fundamentally improving frontline morale and retention. 

Premkumar: AI is already delivering measurable impact by: 

• Improving asset reliability through predictive and prescriptive maintenance
  
• Optimising yield, energy use and throughput in real time 
• Reducing unplanned downtime and quality losses 
• Augmenting frontline workers with decision support, not replacing them 

This isn’t theoretical – it’s driving hard improvements in efficiency, resilience and unit economics right here and right now. 

Ram: Today, one of the most significant developments of AI in the manufacturing sector is the emerging but impactful rise of agentic AI. While it's still early days for this more autonomous form of AI (including physical AI implementations), manufacturing is beginning to benefit significantly from its maturity. Agentic AI provides some immediate advantages by leveraging large language models (LLMs), domain knowledge and visual data to create intelligent agents within the manufacturing process. 

For example, at Hitachi Rail's digital-first facility in Hagerstown, Maryland, which is built on our Lumada Unified Data Layer (UDL), we are developing a GenAI-powered worker support tool. This system integrates multimodal LLMs with an expert system and knowledge graphs to provide smart, real-time assistance to manufacturing operators. It guides them in understanding cause-and-effect relationships, quickly identifying root causes of issues, and preventing problems that could slow down production or compromise quality. This not only enhances worker productivity and improves product quality but also significantly reduces the probability of human error, directly affecting health and safety.

Consequently, AI is also fuelling the urgent need for enhanced 'digital capability' across all industries, including manufacturing. Even now, forward-thinking manufacturers are beginning to explore how digital tools, often underpinned by AI principles, can optimise their internal processes, improve efficiency and enhance their supply chain readiness by moving towards more advanced automation and robotics fuelled by these intelligent agents.

Alongside this monumental shift in manufacturing, we must talk about the elephant in the room – energy. We're seeing an unprecedented surge in electricity consumption driven by AI and the vast data centres required to power it, intensifying the pressure on our existing grids. For manufacturers, this means operating in an environment where reliable power is under increasing scrutiny, and the cost and security of energy supply are becoming critical business considerations. So, while not necessarily inside the production line itself, AI is fundamentally altering the energy landscape which manufacturing relies upon.

Where do you see AI changing manufacturing in 12 months?

Yuriy: Over the next 12 months, we will see a rapid shift from isolated AI features to what I call the "Agentic Transformation". Right now, the industry is heavily focused on disconnected point solutions, like using AI solely for predictive maintenance alerts or building a "digital twin". In the next year, we will move into "Agentic Fusion" - organically integrating predictive maintenance, digital twins and AI co-pilots into a single, continuous human-machine integrated workflow. 

We are moving toward ecosystems of AI agents that actively collaborate to orchestrate work. For example, a Maintenance Co-Pilot Agent won't just throw a warning light to tell an operator what is wrong. It will proactively query the machine's digital twin and previous maintenance records to diagnose the root cause, check enterprise systems for replacement part inventory, auto-draft the procurement request and then safely and visually guide the frontline technician through the physical repair process using voice interaction, allowing the technician to keep both hands free. In short, AI will evolve from a passive advisory layer into an active execution layer, dramatically reducing the time between detecting a physical problem and resolving it. 

Premkumar: Over the next 12 months, we’ll see a variety of things take place. We’ll see AI move from pilots to scaled deployment; greater adoption of closed loop optimisation, not just insights. Tighter integration between IT, OT and engineering workflows will take place, and there will be an increased focus on governance, safety certification and lifecycle management. The conversation will shift from “Can AI work?” to “How fast can we scale it responsibly?” 

The most meaningful impact will come from practical Physical AI deployments in wellbounded parts of the manufacturing system, rather than broad, autonomous factories. We’ll see AI move closer to the physical process – embedded at the edge, integrated with machines, sensors, robotics, and control systems – to: 

• Enable closed loop optimisation in specific operations such as quality inspection, intralogistics, equipment health and energy management 
  
• Make automation more adaptive, able to handle variability in materials, operating conditions and demand 
  
• Improve operator effectiveness, with AI acting as a real-time copilot rather than a replacement 

Critically, these systems will operate within defined constraints, grounded in physics, rules and operating envelopes. This is Physical AI that is engineered for reliability, not generalised autonomy. 

Ram: Firstly, AI's escalating energy demand will continue to push manufacturers towards sophisticated, AI-driven energy management. They will increasingly need to leverage digital tools to intelligently manage their own electricity consumption, optimising everything from peak load balancing to integrating distributed energy resources. The ability to 'orchestrate' energy use, like what we see in advanced electrified mobility solutions, will become essential to reduce operating costs and ensure uninterrupted production.

Secondly, the pressure placed on the grid by AI's growth will mean a much stronger, more urgent push for grid modernisation and expansion. This will encourage more investment in new transmission lines, substations and digital control systems. Such focus on a more resilient, flexible and digital grid, crucial for sustained growth and the integration of new technologies, will directly benefit manufacturers. 

Finally, I believe AI will become more integrated into manufacturing's strategic 'digital growth' initiatives. This means moving beyond energy management to a broader application of AI for enhanced operational intelligence, predictive maintenance and critically, the accelerated adoption of agentic AI, advanced automation and robotics. Manufacturers will need to start planning ahead for the speedy uptake of these technologies in order to remain competitive and define their future, or they risk being disrupted like we've seen in other industries with the rapid evolution of LLMs. This will also drive new applications in improving worker health and safety by preventing errors and providing intelligent assistance. It will be about using AI to create more efficient, resilient, and future-proofed industrial operations, aligning with the broader goal to make Europe's economy more robust and sustainable. ​​​​​​​

How can AI be integrated into operations without causing disruption? 

Yuriy: The key to initial deployments is gaining trust and providing immediate value to the floor personnel. Non-disruptive AI assistance - seamlessly reading sensor telemetry and surfacing disparate data - should fundamentally make jobs on the floor easier. With the AI handling the immediate cognitive load, the role of experienced personnel shifts from reactively firefighting to keep the line running, to proactively optimising the process for less waste and higher throughput. Running the system in this assistive mode also gives us the time to perfect the knowledge base. In my experience, true tacit knowledge cannot be extracted through forced, scheduled interviews; it is only captured organically through shoulder-to-shoulder human-machine collaboration on the factory floor. This takes time. 

Once that knowledge base is solidified, the AI can safely transition into more autonomous, proactive work. Ultimately, integrating AI without disruption requires more than just deep technical expertise. It requires deep domain expertise and a commitment to gradual, evolutionary change - perfectly aligning with the Japanese tradition of Kaizen, or continuous improvement. 

Premkumar: Successful integration starts with respect for operations. You must deploy AI incrementally, alongside existing workflows, beginning with decision support and progressing to automation. Use of digital twins and simulations will enable you to validate behaviour before production rollout. And you must train and engage operators early – trust is as important as accuracy. When AI is introduced as an operator ally, not a black box, adoption accelerates naturally. 

Read the full story in the May 2026 edition of Manufacturing Digital.