OT Is Evolving: From ICS to a Digital Operations Backbone

Connectivity is the foundation. Shop floor integration is a strategic shift in enterprise transformation.
Modernization is a risk management strategy. Manufacturers must address legacy debt through a structured, phased approach to reduce risk, improve reliability, and maintain stable operations.
AI drives operational autonomy. Moving intelligence to the edge shifts the plant from a reactive approach to a self-adjusting, proactive system.
Tech Mahindra’s approach to operational technology OT transformation delivers a resilient digital backbone, ensuring more stable operations, faster issue resolution, and better control across the shop floor.

The Reality of OT

For decades, manufacturers designed operational technology (OT) for deterministic production and operational control. However, in the age of digital manufacturing, we are witnessing a fundamental pivot. OT is no longer confined to a plant-floor function; it now serves as the core pillar of modern manufacturing and enterprise transformation.

Specific structural shifts drive this evolution. First, a transition towards software-defined automation, virtual programmable logic controllers (PLCs), and containerization breaks traditional hardware lock-in. Simultaneously, IT/OT convergence anchored by a unified namespace (UNS) removes data silos to create a single source of truth for the entire organization. Decision-making also moves to the edge; processing data where it is generated allows the shop floor to act in real-time rather than reacting to delayed insights. Next, the improvements to OT cybersecurity, zero-trust architectures, and proactive threat management protect assets without disrupting production uptime. Finally, these forces enable autonomous operations, shifting the factory from reactive troubleshooting to a self-adjusting, resilient system.

The Shift: OT as the Digital Operations Backbone

To transform manufacturing, enterprises must focus on the core elements of OT. Whether in process, discrete, or hybrid manufacturing, the proposed key pillars drive the shop-floor efficiencies and outcomes modern enterprises require.

Connected Shop Floor

OT environments are inherently complex, with a wide range of industrial control system (ICS) OEMs, diverse industrial network protocols, and multiple hardware and software systems across machines.

To manage this complexity, a centralized, connected platform is essential. It enables all shopfloor ICS systems to connect and share data across different industrial network protocols, whether Ethernet-based or serial communication protocols.

Here’s a rundown on key initiatives enabling a connected shop floor.

Machine connectivity unifies ICS systems across the floor.
Use case-driven data modeling to align data and achieve plant KPIs.
IT/OT convergence with UNS architecture establishes a flexible, scalable data layer that serves as a single source of truth.
Shopfloor 5G enables high-speed, accurate, and secure data streams with scalable connectivity.
Low-code and No-code platforms expedite changes on the shop floor with minimal dependency on specialized skills.

OT modernization is a key initiative to reduce downtime risks and improve overall reliability.

Infrastructure Transformation and Modernization

Most of the OT stack is legacy. This reliance creates five immediate challenges: hardware unavailability, unplanned downtime, skill scarcity, low reliability, and high maintenance costs. Operating production on such outdated OT systems also poses significant operational risks. To manage this, manufacturers need a structured modernization approach that starts by assessing the current stack, identifying obsolete assets, and upgrading them in a planned, phased manner. Additionally, modernization from OT obsolescence also demands:

Continuous asset lifecycle visibility
Risk-based modernization planning (short/mid/long term)
Controlled upgrades of PLCs, SCADA, MES, and historians
Hardware-independent and virtualized control strategies

Modernization is no longer just a hardware replacement. Instead, specific technological shifts are driving the transformation. Here’s a rundown:

Software-defined Automation via Virtual PLCs: PLCs run on IT hardware, the cloud, and edge devices. Giving complete hardware independence and the possibility of containerization.
Edge AI and Analytics: Processes data directly on the factory floor, enabling real-time decisions such as instant quality inspections. These capabilities are further enhanced by AI at the edge, generating actionable insights as operations unfold.
Operational Digital Twin: Connect factories and production lines to 3D virtual environments. By using real-time PLC data, these platforms simulate new product launches, labor planning, and sensor layouts. This allows manufacturers to optimize shop floor efficiency in a virtual world before physical execution.

When these technologies are deployed into active operations, they accelerate the transition towards paperless operations and expose energy waste throughout the factory. Maintenance teams can use these insights to move away from fixed schedules toward condition-based monitoring (CBM), improving reliability over time.

AI is no longer limited to IT. It is now being adopted across the shop floor, improving efficiency, strengthening analytics, and driving better operational performance.

AI-powered OT Innovations

AI is moving into OT systems, extending beyond IT-driven use cases. Across industrial control, supervisory, and manufacturing execution system (MES) layers, it supports faster responses, improves decision accuracy, and reduces manual effort on the shop floor.

AI in OT is primarily used to automate decision-making, improve quality, and speed up operations. This is already visible across three key areas.

Operating Model for OT Management

Operations management of OT systems is intricate. Sustaining complex environments requires centralized oversight, continuous monitoring, and industrial-grade support to manage the scale and availability of modern plants. For this to work, OT has to be managed as a governed, production-first service that fits how manufacturing actually runs.

At its core, the future-ready model follows five principles.

Production First - Every OT decision prioritizes safety, uptime, and OEE
Line-aware Operations - Incidents, changes, and patches are contextualized to line criticality
Secure by Design - Zero-trust OT security embedded into core operations
AI-driven Proactivity - Transition from reactive to predictive to fully autonomous operations
Standardization at Scale - Uniformity across operations without any disruptions

Execution of OT Operations

Turning the vision of a digital operations backbone into a reality requires a disciplined framework that bridges the gap between strategy and the shop floor. TechM's approach to the implementation depends on five imperatives.

Phased OT Transformation Roadmap (3–6–9 Months)

The transformation is structured into an outcome-based approach across solution areas:

OT platform lifecycle management
IT/OT integration
24×7 ICS support
Patch and vulnerability management
Security, networks, and service desk

Each phase (Discovery → Optimization → Modernization) is tied to clear KPIs, ensuring predictable value realization and risk-controlled transformation.

KPIs and SLAs

To ensure enterprise-grade accountability, performance, control, and transparency, the approach establishes:

Availability targets for OT infrastructure and critical systems
SLA-based response and resolution times
MTTR tracking and incident recurrence reduction
Explicit dependency assumptions, such as vendors, site access, and maintenance windows

Operating Model

By deploying a centralized OT managed services model, supported by onsite teams, offshore CoEs, and OEM ecosystems, the transformation provides:

24×7 OT monitoring and incident management
End-to-end asset visibility via CMDB and OT discovery
Predictive intelligence using AIOps
Structured L1–L3 support with OEM coordination
ServiceNow-based OTSM aligned with ISA and ITIL standards

With this model in place, operations yield measurable outcomes:

98% SLA adherence
Reduced MTTR and repeat incidents
Predictable cost and scalable support across global plants

Governance and Ownership

Accountability requires clear decision rights and defined escalation paths. This model establishes critical roles to ensure compliance across global, multi-plant operations. Roles and responsibilities are defined based on core functions:

OT service ownership
OT infrastructure operations
OT security and compliance
Vendors/OEMs support

AI-enabled OT Operations

From support to autonomy, AI agents play an increasingly prominent role in OT operations. They eliminate human dependency, improve response times, and enable proactive and self-healing OT environments. Agents can now automate diagnostics, RCA, patching, MES health monitoring, asset management, and SOP generation. Serving as a co-pilot for OT engineers, agents drive autonomy, speed, and consistency. A few notable examples of agents in OT include:

Diagnostics and auto-resolution agent
Log intelligence and RCA agent
MES health agent
OT patch orchestration agent
Vulnerability correlation agent
Service desk copilot (L1/L1.5)

Finally, this AI-enabled approach to OT transformation results in higher OT availability (99.5–99.9%), reduced cybersecurity risk, and faster issue resolution with fewer repeat incidents. It also helps lower the total cost of ownership through standardization and the use of AI, while supporting more consistent operations across plants.

Proactive OT Security: Zero-trust Without Production Disruption

As OT environments become more connected and autonomous, security needs to keep up without affecting production. Therefore, OT security is approached with a risk-based prioritization. This includes zero-trust access, network segmentation, controlled remote access, and continuous vulnerability monitoring. Additionally, patching and updates also account for production schedules while complying with industry standards.

Holistically, this proactive security prioritization protects operations, reducing risk without interrupting the plant.

Legacy systems are exposed to cyber threats and vulnerabilities and need to be addressed through proactive OT security measures.

Conclusion

The era of isolated, hardware-bound OT is over. Manufacturers now face a clear choice: continue managing the risks of legacy debt or build a foundation for digital performance.

Transitioning to a digital operations backbone turns the shop floor from a disconnected cost center into a strategic engine for growth. By integrating AI, establishing connectivity, and adopting an autonomous operating model, enterprises achieve the scale and resilience required to compete.

This is the new standard for modern manufacturing. Those who build this backbone today will own the operational advantages of tomorrow.

TAGS: Artificial Intelligence Manufacturing

Frequently Asked Questions

Our FAQ section is designed to guide you through the most common topics and concerns.

Operational technology is evolving as manufacturing becomes more data-driven, connected, and adaptive. Traditional ICS focused on deterministic control within isolated plants. Modern manufacturing requires real-time data sharing, enterprise integration, and faster decision-making. As a result, OT now supports analytics, AI at the edge, cybersecurity, and integration with IT systems, positioning it as a central digital foundation for operational performance rather than a stand-alone control layer.

A connected shop floor allows machines, control systems, and applications to exchange data across diverse protocols and vendors. This connectivity enables unified visibility, consistent data models aligned to KPIs, and a single source of truth through architectures such as a unified namespace. It also supports faster change implementation, improved traceability, and scalable integration of new technologies without disrupting production.

Modernization addresses risks created by aging hardware, unsupported software, and skill shortages. A phased, risk-based approach improves system reliability, reduces unplanned downtime, and enhances asset visibility. Updating platforms also enables stronger security controls such as segmentation, continuous monitoring, and safer patching practices. Together, these changes lower failure risks while protecting production systems from growing cyber threats.

AI enhances OT by enabling faster, data-driven decisions directly on the shop floor. Applications include self-tuning control systems, real-time quality inspection using vision analytics, and adaptive scheduling within manufacturing execution systems. By moving intelligence closer to physical processes, AI helps operations shift from reactive responses to predictive and self-adjusting behaviors with reduced manual intervention.

Managing OT at scale requires centralized governance, standardized processes, and production-first priorities. Key practices include line-aware incident management, continuous monitoring, structured lifecycle management, and security embedded by design. Combining onsite expertise with centralized support models and AI-driven analytics helps maintain high availability, reduce repeat incidents, and ensure consistent performance across multiple plants.

About the Author

Jayesh Monpara

Delivery Head - Connected Operations & MES Practice, Tech Mahindra

Jayesh Monpara is a seasoned industry professional with over 23 years of expertise in manufacturing IT/OT and digital transformation. He specializes in IIOT, MES/MOM, IDF, SCADA, EDGE, PLCs, and cloud-based solutions, adopting a practical approach to developing scalable smart manufacturing systems. His experience spans diverse sectors, including process, discrete, and hybrid industries. Jayesh has also gained substantial international exposure through assignments in the USA, Europe, and APAC, further cementing his reputation for delivering effective global manufacturing solutions.

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Jayesh Monpara

Delivery Head - Connected Operations & MES Practice, Tech Mahindra

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The Biopharma Industry: The Story Thus FarOver the past five decades, biopharma has enjoyed a remarkable run, delivering robust growth (~7% CAGR) and demonstrating a unique resilience. By developing defensible intellectual property in pursuit of innovative therapies, the industry has withstood economic downturns and sustained margins that few other sectors can match.This resilience is a core feature of the business model, established long before the modern digital transformation wave. While ‘digital’ has helped the industry amplify scale and efficiency, it hasn't fundamentally altered the scientific core. Competitive advantage remains ‘baked in the kitchen’—discovery and development—where scientific insight is poured into carefully guarded experiments (recipes), perfected over years of effort and masterful human judgment.In observing the technology trajectories of top biopharma players, I have noticed a high responsiveness to digital modernization (Cloud, Data, SaaS) among CIOs and CDOs. However, business leadership—CEOs and CMOs—has rarely seen digital technology as a fundamental pivot. The organizations were always data-savvy, becoming increasingly literate in harnessing external data sources. Traditional AI (classical ML and simulations) was commonplace, yet the impact was felt only on the margins. The process of developing therapies remains famously slow (10–15 years) and expensive ($1B+). The ‘Big Solve’ remains elusive because the fundamentals of scientific decision-making have remained materially unchanged.The Generative AI DifferenceGenAI represents a tectonic shift. The combination of generative algorithms, novel training techniques, and accelerated compute costs creates a compelling business case for AI at the core of the business. In our conversations with industry leaders, as reflected in analyst surveys, executives are excited about GenAI for good reason.The impact is broad-ranging, moving well beyond incremental automation. McKinsey & Co. estimates that GenAI could unlock between $60M and $100M in annual value across drug discovery, clinical trials, and commercialization. With deep proprietary data and scientific know-how, the sector is structurally well-positioned to harness GenAI into a durable advantage.With GenAI, for the first time, AI-augmented digital systems can participate inside the scientific workflow—capable of not just analyzing outcomes but influencing what gets tested next. AI is moving from the margins to become a co-pilot in the kitchen. This creates a massive opportunity, but with a warning sign: proceed with caution. An industry’s reputation is only as good as its clinical safety. Every step requires extraordinary vigilance to uphold clinical standards.The mandate is twofold: accelerate the kitchen with AI—without compromising the recipe that delivered decades of resilience.Accelerating The KitchenExtending the metaphor from the chef’s fire to the table, acceleration must span the full value cycle—from bench to bedside. Speed in one function alone does little if bottlenecks persist elsewhere. Accelerating the kitchen means improving every station: discovery, clinical development, and commercialization.Discovery: Self-Learning PipelinesA closed-loop learning model is the most significant opportunity to accelerate discovery. GenAI is already showing signs of success, particularly in AI-augmented decisions around predicting experiments or suggesting sequencing. The traditional linear pipeline is transforming into a true learning cycle, where the twin engines of lab experimentation (human-centered) and computation (algorithm-centric) work in a continuous loop. The goal: reduce wasted cycles, eliminate low-probability paths sooner, and reach viable leads faster.The marketplace for AI-native platforms like AlphaFold (Google DeepMind) or BioNeMo (Nvidia) is maturing rapidly. With these integrated capabilities, there is potential to compress target-to-lead timelines by 3–12 months and save tens of millions of dollars. Our discussions with R&D teams confirm the value of AI in eliminating targets that looked promising on paper but failed under simulation.Development: Cycle-Time And Early DecisionsAI in clinical development offers massive potential for both time and cost savings. Even minor insights from advanced analytics are vital for improving trial design, site selection, and patient identification. Simulations and stress-testing protocols can meaningfully influence recruitment and safety.The idea is to augment—not replace—the judgment of clinical and biostatistics teams. Fast foresight helps organizations decide what to pursue earlier in the cycle when productivity curves are still manageable. From an implementation perspective, we are increasingly collaborating with clients on ‘bespoke development’ for AI-assisted protocol authoring and the creation of synthetic datasets across the clinical cycle.The idea is to augment—not replace—the judgment of clinical and biostatistics teams. Fast foresight helps organizations decide what to pursue earlier in the cycle.Commercialization: Insights Beyond ApprovalImpact does not stop at approval. Across launch planning, market access, and field engagement, AI enables ‘live’ learning systems. Signals from prescribing behavior and payer dynamics can be integrated in real time. Over time, a feedback loop takes shape in which commercial insight informs development strategy, and vice versa. This is critical in markets where differentiation windows are increasingly narrow.Safeguarding The RecipeAs GenAI moves closer to the core, the organizational risk profile changes. What used to be a question of data protection at the edges now touches the heart of how science is conducted. IP protection remains front and center, but new exposure points for leakage are emerging. There is a need to fortify security mechanisms to factor in AI-related threats as a strategic domain.In an AI-augmented world, reverse inference, supplier risk, and ‘black-box’ fragility are emergent challenges that biopharma must strategically contend with. Furthermore, the specter of stiffer regulations demands carefully crafted compliance. Safeguarding the organization’s assets now includes protecting AI algorithms, models, and data. This necessitates a pivot in organizational design to demonstrate credibility, explainability, and auditability to regulators on demand.Cyber Risk: Scientific Core ExposureWith AI at the core, the attack surface for cyber threats expands to include proprietary biology, training data, and model parameters. These ‘crown jewels’ are now encoded within the process. AI assets become a new class of sensitive IP, often harder to monitor when compromised. Addressing these risks requires AI-native cybersecurity, treating models and data as protected assets by design rather than as an afterthought.Regulatory Lens: Upstream EngagementRegulators are signaling a measured approach—keeping the door open while deep-diving into the scope of AI-augmented filings. They want to know how decisions were reached, requiring verifiable evidence and clear human oversight. In this environment, governance is not a ‘compliance tax’; it is a precondition for speed.AI assets become a new class of sensitive IP, often harder to monitor when compromisedSeizing the Initiative: Proactive Organizational DesignLeaders are not waiting for regulations to force their hand. They are redesigning their organizations to bring AI governance to the forefront. In our client conversations, a pragmatic federated model has emerged as the preferred choice. Much like a sports league, central functions act as rule-setters, while functional teams (Discovery, Clinical, and Commercial) are the players running the plays within those guardrails.Key principles include treating AI artifacts as regulated assets, separating public foundation models from proprietary layers, and aligning cyber, legal, and scientific leadership voices into a unified front.Looking Ahead: Gaining Enduring AdvantageWith GenAI, digital capability moves from supporting science to participating within it. What comes next will be shaped less by technical novelty and more by how deliberately GenAI is integrated into the core.From what we observe at Tech Mahindra, the most durable progress is made when GenAI is elevated from episodic experimentation to an enterprise-wide thread. The early leaders are setting flywheels in motion that will accelerate innovation while preserving the trust that is the lifeblood of this industry. Sustained momentum and small daily successes will define the next phase of global leadership.

A New Era of AI: From Isolated Activity to Enterprise-Wide ValueWhen 15 CXOs converged in Helsinki for an executive dialogue hosted by Tech Mahindra, a clear conclusion emerged: a fundamental redefinition of the AI conversation. Through this discussion, most leaders echoed a similar view that enterprises have moved past AI experimentation, with tools deployed, pilots underway, and embedded capabilities across functions. However, enterprise leaders are seeking answers to the most consequential challenge: how to translate an isolated AI activity into enterprise-wide value. Simply put, how do organizations go from pilot to platform?As geopolitical analyst James Crabtree reflected during the session:We have entered a phase where volatility is not an interruption to strategy, it is the backdrop against which strategy is executed. Leaders can no longer wait for clarity before acting. The real test is whether organizations are designed to make decisions under pressure, with speed and judgement, while still maintaining control and accountability. Those that can do this consistently will separate themselves very quickly from those that cannot.A clear distinction matters; on one hand, a pilot proves technical feasibility, on the other, a platform changes the way an enterprise operates. A pilot may improve a workflow, but a platform reshapes decision-making, productivity, customer service, and growth. In today’s business climate, that shift is what separates curiosity from competitive edge.The Real Challenge: Adoption Vs. ROIAcross the Nordics, AI adoption is no longer the issue. Nearly 99% of companies are already using AI capabilities in some form, making the region one of the fastest movers in uptake 1. This has failed to translate into value at the Nordics - only 4% of Nordic companies report meaningful financial returns from AI, even as leaders expect AI-driven impact by 2029 to be 2–3x higher than that of global competitors1.The key driver for this disconnect lies in where enterprises invest. Approximately 40-50% of AI investment spend at Nordic companies is allocated to off-the-shelf productivity tools, compared with 8-11% among global and EU peers, while leading global enterprises are placing far greater emphasis on transformative, end-to-end use cases that redesign workflows and operating models to deliver higher ROI. Thus, widening the gap between AI ambition and realized business impact. This needs urgent, decisive leadership attention.Leaders at the helm must therefore rigorously discuss whether AI should not be treated as a broad innovation agenda with diffuse ownership and loosely defined success metrics. The discussion should be anchored in a simple yet crucial question: where is the return?The answer to this question lies not in theory or technical demonstrations. But in tangible outcomes, including revenue growth, cost efficiency, faster cycle times, better service quality, stronger resilience, and improved decision precision.From AI Capability to Board-Level OutcomesThe organizations that will lead in the next phase are those that stop viewing AI as a layer added onto existing processes and start treating it as a platform for transformation. That requires a shift in mindset across the following six areas.Focus On Value Pools, Not Scattered Use Cases AI transformation should be sequential rather than pursued all at once. Therefore, enterprise leaders should identify a small number of high-value domains across operations, supply chain, customer experience, product development, and decision support where AI can materially improve performance. Strategic concentration matters more than fragmented experimentation.Rebalance Investment Toward End-to-End Transformation Organizations are rethinking and redirecting their AI spending towards integrated and high-impact use cases that go beyond stand-alone tools, assistants, and incremental enhancements. Thus, greater opportunities lie in redesigning end-to-end workflows, operating models, and execution systems - unlocking the true platform value.Put Business Leaders in the Driver’s Seat The full potential of AI is realized when commercial and operational accountability remains with the business and P&L leaders. Thus, ensuring outcomes that are reflected in growth, customer retention, or speed-to-market.Govern at the Enterprise Level In decentralized operating environments, AI can quickly become a patchwork of pilots, vendors, and duplicated effort. To move from pilot to platform, organizations need executive sponsorship, clear decision rights, centralized prioritization for strategic initiatives, and governance that enables scale without losing control.Design for People, Not Only for Process AI transformation is beyond technical, it is organizational. Employees need to understand how AI supports their work, where human judgment remains essential, and why new ways of working are worth embracing. Today, co-design, transparency, and psychological safety are not soft issues; but practical requirements for adoption at scale.Build Workforce Readiness as a Core Leadership Priority The value of AI is realized only when the workforce is ready to use it well, meaning reskilling, digital fluency, and leadership literacy. Organizations that focus skill as a central to execution are the ones that convert potential into performance.Why the Nordics May Be Uniquely PositionedThe dialogue at Helsinki is appropriate in the Nordic context as many of the capabilities required for responsible AI transformation are already deeply embedded in the region’s business culture. They are as follows:Sisu: The Resilience to Stay the Course AI transformation is not a one-quarter initiative. It requires perseverance, strategic patience, and the willingness to continue through ambiguity and complexity. The Finnish concept of Sisu captures exactly that kind of determined resilience. Enterprises that treat AI as a long-term operating shift, not a short-lived technology wave, will be better positioned to realize sustainable returns.Tillit: Trust as a Competitive Advantage In the age of AI, trust is no longer just a governance concern but a growth enabler. Trust in data, trust in decisions, trust between leadership and employees, and trust from customers and regulators all influence the speed at which AI can scale. The Nordic tradition of Tillit, trust, provides a powerful foundation for building AI systems that are transparent, accountable, and widely adopted.Dugnad: Collective Effort Toward Shared Outcomes AI cannot merely be scaled by a technology function, but it requires collaboration across business units, leaders, operators, risk teams, and frontline employees. The Nordic idea of Dugnad, collective effort for a common purpose, is highly relevant here. The companies that succeed will be those that mobilize the organization as a whole, rather than treating AI as the responsibility of a few specialists.The Leadership Imperative AheadThe next phase for enterprise AI is not about the number of pilots an organization launches, but about the effectiveness it delivers and the measurable business value it generates. In simple terms, it means pondering critical questions, including where the most meaningful returns are. What operating models require change? Who is responsible for outcomes? And how do we scale trust, capability, and governance at the same time?In this context, opportunities for Nordic enterprises are significant. Ambition, digital maturity, and cultural ethos built on resilience, trust, and collective action are a set foundation for AI. However, the task is converting these strengths into execution. It is a journey of experimentation to adoption and from capability to board-level outcomes.The Path Forward for Enterprise AIThe future of AI is clear - it has moved beyond fragmented initiatives, and it has now become a discipline, an enterprise-wide driver of value. Enterprises in the Nordic countries that align AI strategy, investment, and accountability are certain to translate capabilities into long-term, sustainable advantage.

The Data Gap Holding Smart Manufacturing BackManufacturing leaders are not asking whether they should digitize any more. They are asking why their $10M investments can’t tell them the root cause of a 2% margin dip in real time.1 The bottleneck is the 'Data Gap' between the PLC and the ERP. Without a consistent structure across the production stack, operational insights remain trapped in silos, leaving leadership to manage by the rearview mirror. The assets that power production, from shop-floor sensors to enterprise systems and Industrial Internet of Things (IIoT) platforms, are not consistently structured across the production stack. This limitation makes it difficult to capture, connect, and act on operational insights at scale.The Barriers for Smart Manufacturing to Go Beyond the Pilot StagesTalent shortages add another layer of difficulty. Nearly half of manufacturers report moderate to significant challenges in filling production and operations management roles, while 69-72% struggle to hire people with IT/OT, data science, and analytics skills.2 These gaps delay the adoption and integration of digital capabilities, making it difficult to turn technology investments into operational outcomes. At the same time, a survey of 600 manufacturing executives found that fewer than half are investing at scale in critical technologies such as IIoT (27%), cloud (29%), and AI (29%).2In short, these trends point to a familiar pattern: digital initiatives stall at the pilot stage due to misalignment across assets, systems, and skills. The International Society of Automation (ISA) helps clarify this complexity by defining automation layers from L0 to L5, which offers manufacturers a practical framework for organizing assets and aligning Information Technology (IT) and Operational Technology (OT) to build a foundation for better, smarter, and more sustainable operations.Manufacturers face persistent shop floor challenges that limit seamless and integrated transactions across their value streams.Operational Hurdles in Modern ManufacturingIn a modern factory, there is no such thing as an isolated incident. A latency issue at the sensor level (L0) ripples upward, manifesting as a reconciliation error in the ERP (L4) hours later. This systemic friction creates a series of interlocking hurdles that drain OEE and inflate the cost of quality:Lack of Shop-floor Connectivity: Machines, sensors, and control systems are often not fully connected, which limits the ability to capture production data consistently.Siloed Data Across the Factory: Information is scattered across multiple systems and tools, forcing teams to switch between screens instead of getting a full picture of production performance.Time-consuming Shop-floor Data Aggregation: Teams spend hours compiling data from multiple sources, often using spreadsheets, which delays analysis and decision-making.Unreliable Shop-floor Data: Manual data entry, inconsistent definitions, and missing signals make teams question whether the numbers truly reflect what’s happening on the floor.Lack of Real-time and Historical Performance Visibility: When data isn’t available in the moment—or easy to look back on—teams struggle to see how operations are running or understand patterns and root causes over time.Micro-stoppages: Small, frequent stoppages often slip under the radar or aren’t logged at all, but together they add up to meaningful productivity losses.Reliability Issues in Critical Machines: Machines fail unexpectedly when they are only fixed after problems arise, which causes unplanned downtime.Limited Traceability: Gaps in tracking materials, process steps, and production events make it challenging to trace quality issues back to their source or meet compliance requirements.Inconsistent Manual Operations: When tasks are performed differently across shifts or locations, leading to varying results, uneven quality, and less stable processes.Quality Defects and Excessive Rework: When issues aren’t detected early or resolved quickly, defects move further down the line, leading to rework, scrap, and delays that could have been avoided with earlier intervention.Increasing Maintenance Costs Due to Legacy Systems: Older equipment and outdated systems require frequent maintenance and are more expensive to operate than embracing newer technologies.Complex Integration Between Shop-floor and Enterprise Systems: Gaps in IT and OT systems make it hard to align production activities with planning, quality management, and business operations.Paper-heavy Operations: Manual records slow down day-to-day work, increase the risk of errors, and limit the effectiveness with which operational data can be digitized and analyzed.These issues might look manageable on their own. But together, they create discord, making it harder for systems to work in sync and reducing the impact of digital initiatives.Building a strong data foundation across ISA-95 layers, from shop floor to enterprise, is a key enabler for AI-driven smart manufacturing.Building the Foundation for Smart ManufacturingTo move past these challenges, manufacturers need to examine how their IT and OT systems are integrated and whether they can scale with the business. Using existing systems as a foundation and bringing in new technologies over time helps make the vision of a smart factory real, not just aspirational. This approach is built on IT/OT convergence principles across the ISA-95 layers.Manufacturers across industries are working to improve overall equipment effectiveness (OEE), gain visibility into production performance, and pinpoint bottlenecks. As per a report, nearly half (49%) of manufacturers cite operational improvements as the primary benefit of investing in smart manufacturing, while 44% highlight financial gains as a key objective.1These capabilities are enabled by a unified, namespace-based IT/OT architecture, factory connectivity, and manufacturing operations management (MOM) solutions, which accelerate operations. Manufacturers are also turning to smart assets to predict issues before they occur, updating equipment to reduce maintenance costs, using AI to improve quality, managing energy more efficiently, and moving toward paperless operations to support sustainability goals. This shift is not about adding more tools but about creating a coherent operational backbone that allows data, decisions, and execution to move together.ConclusionBuilding an ecosystem around IT/OT convergence allows manufacturers to detect issues earlier, improve production performance, and scale operations with greater consistency. Organizations can reduce fragmentation between shop-floor execution and enterprise systems by structuring assets and data across the ISA-95 layers.This clarity helps digital initiatives move beyond isolated pilots and into everyday operations. As manufacturers modernize their assets, improve connectivity, and strengthen operational visibility, they create a foundation that drives efficiency, supports sustainability goals, and enhances long-term resilience.

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The Evolution of Operational Technology (OT): From Industrial Control Systems (ICS) to a Digital Operational Backbone

The Reality of OT