India has evolved to a hub that offers capability at scale: Siemens

Industry 4.0 in 2026 will be defined not by individual technologies, but by how multiple capabilities are engineered together to deliver measurable outcomes. 

Pankaj Vyas, CEO & Managing Director, Siemens Technology and Services, tells us more. Excerpts from an interview: 

DQ: What big Industry 4.0 trends should we expect in 2026?

Pankaj Vyas: Industry 4.0 in 2026 will be defined not by individual technologies, but by how multiple capabilities are engineered together to deliver measurable outcomes. The business implication, therefore, is clear. Organisations that move from experimental AI to engineered, repeatable platforms delivering KPIs such as uptime, yield, energy efficiency and safety will set the pace.
 
As such, in 2026, four shifts will dominate. First, agentic and multi-agent workflows will move from experimentation to production-grade pilots, with engineers orchestrating systems of agents that write, validate and optimise code while humans govern intent and safety. 

Second, the digital twin – a virtual model of a physical asset – is becoming a standard engineering tool. It lets them ask "what if?" and see the results instantly. Companies can test different factory layouts or find ways to save energy before making any physical changes. 

Third, sustainability will become a built-in design requirement: product, supply-chain carbon transparency, and material-selection systems will be integrated with engineering flows so lower-carbon outcomes are built in, not reported after the fact. 

Lastly, the information technology (IT) and operational technology (OT) boundary will continue to fade as edge-cloud architectures, interoperable APIs, and real-time control stacks become fundamentals for scaling industrial AI.

DQ: How close are we to a multi-agent engineering ecosystem that can self-orchestrate complex tasks?

Pankaj Vyas: We are getting close in capability in certain domains, but remain deliberately conservative in adoption. Architecturally, the building blocks exist: large language models that generate, critique and validate outputs, and orchestration layers that assign tasks across agents. 

However, several constraints exist. First, agents require verified, trustworthy signals from IT and OT systems. This means reliable cross-system observability is foundational. Second, assurance layers are essential to validate agent recommendations against rules before any actuation, particularly in an industrial setting. 

Recent industry progress indicates that agentic AI is transitioning from labs into controlled industrial settings, yet organisations remain cautious. This is primarily because agentic systems can expand the attack surface, introduce new failure modes, and raise questions of traceability and auditability. 

2026 will see an increasing adoption of multi-agent systems inside tightly defined guardrails. Humans will remain the architects and final decision authority for safety-critical actions, while agents will automate routine, well-specified tasks such as code scaffolding, diagnostics triage and cost optimization. 

With that explainability, multi-agent orchestration can shift from a promising demo to a dependable engineering capability.

DQ: What are the key factors to be considered for AI when moving from PoCs to deployments? What new architecture choices should matter most now -- interoperability, security, data sovereignty)?

Pankaj Vyas: Moving from PoCs to real deployments is a shift from experimentation to operational scale. At that stage, three factors matter most.

First, interoperability. AI systems must integrate seamlessly with existing IT and operational environments across ERP, MES, and industrial control systems. If models cannot plug into live workflows and legacy systems, insights remain isolated and don’t translate into impact.

Second, security and digital trust. Deploying AI expands the attack surface from models and agents to data pipelines and orchestration layers. Security cannot be added later; it has to be built into the architecture through zero-trust principles, continuous validation, and strong product-level security and response mechanisms.

Third, data sovereignty and governance. Regulatory and enterprise constraints increasingly require data to remain local, traceable, and auditable. Architectures must support local data residency, controlled model deployment, and clear lineage so organisations can explain what data trained which model, and why it made a particular decision.

Beyond these foundations, production systems need safeguards that validate AI outputs before they trigger real-world actions. In practice, this means hybrid edge-and-cloud architectures for latency, policy controls around automation, and strong observability to link AI decisions to business outcomes.

DQ: What makes India the go-to R&D hub for industrial AI, digital twins, automation, and energy-efficient tech?

Pankaj Vyas: India has evolved from a cost-arbitrage base to a hub that offers capability at scale, making it a preferred R&D destination. This evolution has happened due to several unique advantages that our nation offers. 

First, the depth and scale of engineering ecosystems. India has a dense network of GCCs and ER&D centres that build end-to-end products, not just components. This means ideas move faster from concepts to deployment. 

Second is our real-world problem-solving abilities. Engineers in India are accustomed to designing for complex operating conditions – price sensitivity, constrained infrastructure, intermittent connectivity, and energy efficiency. If a concept works here, it usually tends to work everywhere globally. 

Third, the breadth of talent that India offers. India has a large pool of engineers who can be trained into cross-disciplinary roles that combine domain engineering, software, and data/AI – exactly what industrial AI and digital twins demand. 

Fourth is our execution maturity. Indian teams now co-design and deliver global platforms, from digital twin implementations and edge automation to sustainability solutions, directly into core product lines. India delivers on speed, scale, and field-tested innovation. 

All these reasons are the key drivers for leading global companies to anchor their industrial AI R&D initiatives in India.

DQ: How is the engineering talent mix changing as companies form AI-native teams, and which cross-disciplinary skills are now non-negotiable?

Pankaj Vyas: The way engineering teams are built is definitely changing. Separate, specialized teams are no longer enough. At Siemens Technology and Services, we intentionally build multidisciplinary teams and invest in OT-IT depth and developer productivity, so AI moves from pilot to real, scalable engineering impact.

Our teams combine software engineering, data science and deep domain expertise. The ‘polymath engineer’, someone who understands physical systems as well as data-driven modelling, is increasingly becoming a baseline requirement.

As the talent mix evolves, some key skills that sit at the intersection of AI, software, and domain engineering which include translating sensor behaviour into usable features, integrating AI with control logic and safety constraints, and validating probabilistic outputs against deterministic physical limits. 

Strong software-engineering fundamentals are equally critical, such as building and operating CI/CD pipelines for model updates, implementing observability to detect data and model drift, using infrastructure-as-code for repeatable deployments, and applying automated testing at scale.

Finally, there is a mindset shift. AI-native teams must think operationally – this means they have to iterate fast, design for failure, and measure outcomes continuously. Cross-functional pods that co-own production outcomes, not just prototypes, are the new standard.