July 06, 2026
IT/OT Convergence Benefits: Operational Efficiency and AI Readiness
Platforms supported by security and networking help manufacturing and industrial organizations adopt a unified approach to IT and operational technology.
Agentic artificial intelligence, autonomous operations and the Internet of Things have added momentum to IT and operational technology (OT) convergence in manufacturing and industrial organizations. Organizations are increasingly looking to IoT maturity to reduce downtime, control costs and increase operational efficiency.
The challenge, however, is that fragmented systems, legacy infrastructure and security concerns continue to limit progress, with potentially serious consequences. Too often, siloed environments result in an unpleasant wake-up call when manufacturing leaders realize they lack a holistic understanding of the operations, costs and risks in their environments. Moreover, disjointed systems make it difficult or impossible to leverage AI agents and automation processes that require integrated, trusted and real-time data.
Platform modernization and data integration, supported by networking and security, help organizations create a unified plant floor approach. With the right foundations in place, organizations can converge IT and OT environments to enable scalable, intelligent and secure digital transformation.
Accelerate your IoT maturity journey with a CDW assessment or transformation workshop.
Agentic artificial intelligence, autonomous operations and the Internet of Things have added momentum to IT and operational technology (OT) convergence in manufacturing and industrial organizations. Organizations are increasingly looking to IoT maturity to reduce downtime, control costs and increase operational efficiency.
The challenge, however, is that fragmented systems, legacy infrastructure and security concerns continue to limit progress, with potentially serious consequences. Too often, siloed environments result in an unpleasant wake-up call when manufacturing leaders realize they lack a holistic understanding of the operations, costs and risks in their environments. Moreover, disjointed systems make it difficult or impossible to leverage AI agents and automation processes that require integrated, trusted and real-time data.
Platform modernization and data integration, supported by networking and security, help organizations create a unified plant floor approach. With the right foundations in place, organizations can converge IT and OT environments to enable scalable, intelligent and secure digital transformation.
Accelerate your IoT maturity journey with a CDW assessment or transformation workshop.
Convergence between IT and OT has become a strategic priority for manufacturing and industrial organizations pursuing IoT maturity, AI and automation. However, many environments still have long-standing gaps between IT systems management and the operational technologies controlling physical operations and industrial assets. These silos impair visibility and data integration, increase inefficiency and downtime risks, and make it difficult to adopt emerging technologies at scale. One common symptom of outdated systems, for example, is an overreliance on manual rework to make data accessible and useful.
The solution is IT/OT convergence: a single, intelligent environment that aligns OT data, IT governance, security and operations. Convergence is more than a dashboard expansion or a limited connectivity play. It is an overarching strategy that CDW assesses across seven core areas: culture and synergy, strategy and governance, people and skills, process alignment, technology alignment, data management, and security and compliance. All seven are essential for true convergence. For example, connecting OT to the network without proper security increases risk by widening the attack surface without the necessary segmentation, governance and OT-specific monitoring.
In practice, physical systems and software technologies are often the easiest fix. Shifting people and processes is where many organizations struggle to achieve maturity. Others establish islands of connectivity and automation, but find it difficult to scale these advances across business units or product portfolios. As a consequence of these challenges, many organizations stay in a reactive posture, lacking unified visibility and unable to leverage capabilities such as AI-enabled predictive maintenance or agentic AI.
A holistic approach to IT/OT convergence encompasses data integration, networking, security and platform capabilities. Ultimately, IT/OT convergence helps organizations advance beyond silos by establishing one intelligent environment characterized by connectivity and integration. When organizations can transform data into actionable insights at an enterprise scale, they can improve performance, build resilience and drive innovation.
46%
The percentage of manufacturing COOs who say that data and IT/OT infrastructure are a top challenge for implementing AI in operations
Source: mckinsey.com, “From Pilots to Performance: How COOs Can Scale AI in Manufacturing,” Dec. 15, 2025
Accelerate your IoT maturity journey with a CDW assessment or transformation workshop.
Convergence between IT and OT has become a strategic priority for manufacturing and industrial organizations pursuing IoT maturity, AI and automation. However, many environments still have long-standing gaps between IT systems management and the operational technologies controlling physical operations and industrial assets. These silos impair visibility and data integration, increase inefficiency and downtime risks, and make it difficult to adopt emerging technologies at scale. One common symptom of outdated systems, for example, is an overreliance on manual rework to make data accessible and useful.
The solution is IT/OT convergence: a single, intelligent environment that aligns OT data, IT governance, security and operations. Convergence is more than a dashboard expansion or a limited connectivity play. It is an overarching strategy that CDW assesses across seven core areas: culture and synergy, strategy and governance, people and skills, process alignment, technology alignment, data management, and security and compliance. All seven are essential for true convergence. For example, connecting OT to the network without proper security increases risk by widening the attack surface without the necessary segmentation, governance and OT-specific monitoring.
In practice, physical systems and software technologies are often the easiest fix. Shifting people and processes is where many organizations struggle to achieve maturity. Others establish islands of connectivity and automation, but find it difficult to scale these advances across business units or product portfolios. As a consequence of these challenges, many organizations stay in a reactive posture, lacking unified visibility and unable to leverage capabilities such as AI-enabled predictive maintenance or agentic AI.
A holistic approach to IT/OT convergence encompasses data integration, networking, security and platform capabilities. Ultimately, IT/OT convergence helps organizations advance beyond silos by establishing one intelligent environment characterized by connectivity and integration. When organizations can transform data into actionable insights at an enterprise scale, they can improve performance, build resilience and drive innovation.
Accelerate your IoT maturity journey with a CDW assessment or transformation workshop.
Downtime, Data and Risk in Manufacturing
~$170K
The average per-hour cost of unplanned downtime in industrial organizations
Source: ABB, “Modernization for Resilience: Unlocking Competitive Advantage Through Life-Cycle Management,” October 2025
44%
The percentage of organizations that do not have robust systems for moving data effectively
Source: Blue Prism, “The Global Enterprise AI Survey 2025,” March 2025
65%
The percentage of manufacturing executives who rank operational risk as their first or second concern related to smart manufacturing initiatives
Source: deloitte.com, “2025 Smart Manufacturing and Operations Survey: Navigating Challenges to Implementation,” May 1, 2025
Downtime, Data and Risk in Manufacturing
~$170K
The average per-hour cost of unplanned downtime in industrial organizations
Source: ABB, “Modernization for Resilience: Unlocking Competitive Advantage Through Life-Cycle Management,” October 2025
44%
The percentage of organizations that do not have robust systems for moving data effectively
Source: Blue Prism, “The Global Enterprise AI Survey 2025,” March 2025
65%
The percentage of manufacturing executives who rank operational risk as their first or second concern related to smart manufacturing initiatives
Source: deloitte.com, “2025 Smart Manufacturing and Operations Survey: Navigating Challenges to Implementation,” May 1, 2025
- CORE CONVERGENCE CAPABILITIES
- OVERCOMING COMMON CHALLENGES
- FOUNDATION FOR MODERNIZATION
Connected devices, edge-to-cloud computing and platforms are the backbone of IT/OT convergence, enabling seamless data flow, real-time processing and scalable integration across environments. What matters most, however, are the operational outcomes that these capabilities deliver: increased resilience, more uptime and faster intelligence that helps organizations reduce reliance on reactive processes.
DEVICE CONNECTIVITY: From machine sensors on production lines to computer-vision cameras, devices facilitate the use of AI and automation in the physical world. Device connectivity involves the secure onboarding, monitoring and management of connected assets across distributed environments. Device deployments should be guided by strategic objectives that connect data and analytics to specific business outcomes — for instance, leveraging video intelligence insights to optimize workflows and improve operations. They enable the data collection that, at scale, drives real-time visibility into what’s happening across environments and empowers organizations through predictive insights.
EDGE-TO-CLOUD DESIGN: Hybrid infrastructures balance edge and cloud capabilities to help organizations optimize performance and increase flexibility — for example, leveraging the edge for fast decision-making near devices and the cloud for long-term storage and analytics. At the same time, plant managers often want to retain control, access and ownership of data through on-premises solutions. Rather than moving every workload to the cloud, organizations can establish a data acquisition layer close to the production environment, enabling local control over operational data while building toward broader integration over time.
DATA STREAMING: Data capture, usage and trust are essential for AI and a major roadblock for many organizations. Manufacturers effectively use only 43% of the data they collect, with the remainder being inaccessible or too untrustworthy to be actionable. Teams often spend significant time reworking spreadsheets manually to make data usable, slowing operations and maintaining reliance on historical data as opposed to forward-looking, predictive insights. Data integration platforms ease these pain points by handling high-volume telemetry with real-time ingestion and normalization, making data standardized and otherwise usable for subsequent analysis and automation. Platforms also support data streaming at scale by facilitating data collection from disparate systems and managing data flow to avoid bottlenecks.
ANALYTICS AND AI: Traditionally, data served a dashboard function, delivering insights about what already happened in an environment. By contrast, modern data is a decision driver, helping organizations look forward with a better understanding of what’s likely to happen in their environments tomorrow. Analytics and AI help organizations turn raw data into predictive insights and automation opportunities. In manufacturing, that means predictive maintenance, real-time production visibility and robotic coordination.
SECURITY AND COMPLIANCE: The connectivity that enables AI, analytics and automation can create risk if it is not managed properly. Sixty-five percent of manufacturing executives said operational risk is among their top concerns about smart manufacturing, including increased potential for unauthorized access, operational disruption and intellectual property theft. IT/OT convergence requires a rethinking of cybersecurity both holistically and through the lens of OT. For example, OT systems may have different requirements for security patching and uptime. Policies for segmentation, remote access, and identity and access management should be defined for OT environments. Regulatory requirements and other standards must be mapped against the converged environment to identify potential effects on risk and compliance.
Click Below To Continue Reading
Organizations often pursue limited digital transformation pilots without establishing a strategy for scaling across the enterprise. Projects frequently stall for the same reason: IT/OT convergence is complex, and it’s difficult to move past the pilot and into production. CDW’s services can help, from an initial transformation workshop that yields a tailored roadmap to full-scale, end-to-end execution support across people, processes and technology.
CDW’s IT/OT Convergence Maturity Assessment evaluates organizations against seven core areas: culture and synergy, strategy and governance, people and skills, process alignment, technology alignment, data management, and security and compliance.
While most vendors advise customers solely on technology, CDW believes that convergence happens in three areas: physical components, software solutions and organizational dynamics, specifically around IT/OT stakeholder alignment and culture.
CDW’s Transformation Workshop identifies business goals and OT challenges and maps them to emerging technology solutions to transform your business and enable next-generation insights.
The workshop yields insights about technology gaps and estimated ROI, a complexity index for the recommended work, an assessment of initial use cases and roadmaps for successful pilots.
IT/OT convergence is complex, especially in legacy environments with poor systems integration and in mergers-and-acquisitions scenarios where operations may differ markedly from one site to the next. Typically, every manufacturing environment has a unique set of conditions that must be considered as organizations work to increase standardization and build consistency.
LEGACY SYSTEMS: Legacy environments often lack connectivity, requiring integration layers such as edge gateways and protocol translation to enable communication. When OT systems are connected to IT networks, they can pose significant risks if they are not modernized from a security perspective and fully incorporated into the IT security strategy. In addition, legacy environments typically suffer from data silos that limit visibility and prevent unified insights. Integrating systems within a legacy environment is often the most difficult challenge, one made harder by skills gaps or cultural misalignment between IT and OT stakeholders.
DATA AND SYSTEMS SILOS: In manufacturing, 37% of organizations cite the inability to capture, understand, interpret and use data effectively as their biggest internal obstacle to growth. At the same time, research shows that while organizations anticipate investing in AI and analytics initiatives, fewer plan to invest in the underlying technologies that enable these advanced capabilities — an indication that COOs may underestimate the necessity of the right data foundation. Some organizations establish spheres of automation only to find they cannot scale due to siloed systems and data. Leveling up and expanding data connectivity is the key to leveraging platform capabilities that drive ROI.
SECURITY RISKS: Forty-six percent of manufacturing organizations surveyed in 2025 had experienced a cyber incident in the past year. Increasingly, cybersecurity attacks involve both IT and OT systems. This dual-systems tactic, comprising 60% of intrusions, is significantly more common than attacks involving only OT (22%) or only enterprise IT (17%). Moreover, IT security teams have incomplete visibility into OT systems: 62% of organizations said they have about 75% visibility, and 30% said they have about 50% visibility. Closing these visibility gaps is crucial. Converged systems increase the attack surface, with integration points between IT and OT systems posing a particular vulnerability.
KNOWLEDGE GAPS: A red flag for early-stage maturity is overreliance on the institutional knowledge of individual employees, who can become the sole source of information about key equipment and processes. This dependency becomes problematic when these individuals leave the company and take years’ worth of knowledge with them without a clear path for how the organization will adapt. By contrast, when sensor data, connected systems and predictive analytics are the source of data and insights, processes are consistent and the risk of disruption is lower.
Many teams must also adjust to prioritizing predictive data instead of historical information. While manufacturing has long relied on understanding what happened, emerging technologies are designed to help organizations become future-focused: anticipating issues in maintenance, inventory and other areas so they can respond proactively.
TEAM MISALIGNMENT: Differences between IT and OT priorities, skill sets and governance models create friction that can stall projects and must be addressed effectively. As organizations mature, IT and OT teams must collaborate in new ways to align priorities and execution. Historically, for example, OT and production teams were likely not involved in IT’s security incident response planning — just one example of the fact that organizations will need to think broadly about adapting people and processes for the technological changes underway.
Although 59% of manufacturers have moved smart technologies from pilot to production, data issues frequently stall momentum and impede scalability. Accordingly, data integration platforms are a fundamental component of modernization, working in concert with networking and security to provide the foundation for agentic AI, autonomous operations and IoT maturity.
PLATFORM STANDARDIZATION: Unified IoT platforms simplify integration and enable scalability. Over time, manufacturing plants and production lines often accumulate technology stacks that have little consistency between them. Standardization allows for a unified management plane, shared protocols and cross-organizational visibility. Those capabilities, in turn, enable the data streaming and analytics that drive automation and ultimately AI. Moreover, industrial IoT platforms are designed to support high-value use cases that can transform operations, such as predictive maintenance and remote monitoring.
EDGE-TO-CLOUD DESIGN: An edge-to-cloud architecture lets organizations balance local processing with cloud analytics for performance and flexibility. In manufacturing, edge processing is a growing priority for organizations that want to leverage AI-enabled edge devices. Edge compute allows for speed and low latency close to the point of data generation, while the cloud handles more compute-intensive workloads. A single control plane provides the visibility and tools to streamline the management of these resources — ideally, within the same platform that organizations use to bring systems together.
DATA AND AGENTIC AI: Data must be able to move reliably and securely between IT and OT, becoming normalized and integrated across enterprise systems via centralized platforms. That means IT and OT teams must collaborate on data strategy, ensuring that OT data can be integrated without affecting productivity and operations and that IT has full visibility into assets on the network. Moreover, agentic AI requires data that is clean, connected and accessible in real time. For example, agents could collect data from physical AI systems, such as robotics, to create analyses that currently require spreadsheets and manual workflows. To achieve that, however, most organizations will need to mature their data capabilities and establish strong governance.
SECURITY AT EVERY LEVEL: Security plays a critical role in enabling business strategy: 37% of manufacturing organizations plan to secure IT/OT infrastructure to drive positive business outcomes over the next five years. Security must be embedded at every layer, from devices to applications, with data protected by encryption, identity management and access controls. Network segmentation establishes virtual walls between systems, creating secure zones for sensitive data and mitigating the potential impact, while threat monitoring solutions should include OT-specific capabilities. Finally, a zero-trust approach should be a priority across operations: Verify everything and trust nothing.
ORGANIZATIONAL OUTCOMES: Modernization helps organizations overcome the constraints of legacy environments, especially the barriers that legacy systems pose to agility and growth. In manufacturing, leaders are investing in factory automation, sensors and vision systems with the expectation of transforming how products are made and boosting their competitive advantage. The driver for all of these advances is data — integrated, analyzed, used to automate and empower AI systems — with IT/OT convergence as the overarching strategy that brings everything together.
- CORE CONVERGENCE CAPABILITIES
- OVERCOMING COMMON CHALLENGES
- FOUNDATION FOR MODERNIZATION
Connected devices, edge-to-cloud computing and platforms are the backbone of IT/OT convergence, enabling seamless data flow, real-time processing and scalable integration across environments. What matters most, however, are the operational outcomes that these capabilities deliver: increased resilience, more uptime and faster intelligence that helps organizations reduce reliance on reactive processes.
DEVICE CONNECTIVITY: From machine sensors on production lines to computer-vision cameras, devices facilitate the use of AI and automation in the physical world. Device connectivity involves the secure onboarding, monitoring and management of connected assets across distributed environments. Device deployments should be guided by strategic objectives that connect data and analytics to specific business outcomes — for instance, leveraging video intelligence insights to optimize workflows and improve operations. They enable the data collection that, at scale, drives real-time visibility into what’s happening across environments and empowers organizations through predictive insights.
EDGE-TO-CLOUD DESIGN: Hybrid infrastructures balance edge and cloud capabilities to help organizations optimize performance and increase flexibility — for example, leveraging the edge for fast decision-making near devices and the cloud for long-term storage and analytics. At the same time, plant managers often want to retain control, access and ownership of data through on-premises solutions. Rather than moving every workload to the cloud, organizations can establish a data acquisition layer close to the production environment, enabling local control over operational data while building toward broader integration over time.
DATA STREAMING: Data capture, usage and trust are essential for AI and a major roadblock for many organizations. Manufacturers effectively use only 43% of the data they collect, with the remainder being inaccessible or too untrustworthy to be actionable. Teams often spend significant time reworking spreadsheets manually to make data usable, slowing operations and maintaining reliance on historical data as opposed to forward-looking, predictive insights. Data integration platforms ease these pain points by handling high-volume telemetry with real-time ingestion and normalization, making data standardized and otherwise usable for subsequent analysis and automation. Platforms also support data streaming at scale by facilitating data collection from disparate systems and managing data flow to avoid bottlenecks.
ANALYTICS AND AI: Traditionally, data served a dashboard function, delivering insights about what already happened in an environment. By contrast, modern data is a decision driver, helping organizations look forward with a better understanding of what’s likely to happen in their environments tomorrow. Analytics and AI help organizations turn raw data into predictive insights and automation opportunities. In manufacturing, that means predictive maintenance, real-time production visibility and robotic coordination.
SECURITY AND COMPLIANCE: The connectivity that enables AI, analytics and automation can create risk if it is not managed properly. Sixty-five percent of manufacturing executives said operational risk is among their top concerns about smart manufacturing, including increased potential for unauthorized access, operational disruption and intellectual property theft. IT/OT convergence requires a rethinking of cybersecurity both holistically and through the lens of OT. For example, OT systems may have different requirements for security patching and uptime. Policies for segmentation, remote access, and identity and access management should be defined for OT environments. Regulatory requirements and other standards must be mapped against the converged environment to identify potential effects on risk and compliance.
Click Below To Continue Reading
Organizations often pursue limited digital transformation pilots without establishing a strategy for scaling across the enterprise. Projects frequently stall for the same reason: IT/OT convergence is complex, and it’s difficult to move past the pilot and into production. CDW’s services can help, from an initial transformation workshop that yields a tailored roadmap to full-scale, end-to-end execution support across people, processes and technology.
CDW’s IT/OT Convergence Maturity Assessment evaluates organizations against seven core areas: culture and synergy, strategy and governance, people and skills, process alignment, technology alignment, data management, and security and compliance.
While most vendors advise customers solely on technology, CDW believes that convergence happens in three areas: physical components, software solutions and organizational dynamics, specifically around IT/OT stakeholder alignment and culture.
CDW’s Transformation Workshop identifies business goals and OT challenges and maps them to emerging technology solutions to transform your business and enable next-generation insights.
The workshop yields insights about technology gaps and estimated ROI, a complexity index for the recommended work, an assessment of initial use cases and roadmaps for successful pilots.
IT/OT convergence is complex, especially in legacy environments with poor systems integration and in mergers-and-acquisitions scenarios where operations may differ markedly from one site to the next. Typically, every manufacturing environment has a unique set of conditions that must be considered as organizations work to increase standardization and build consistency.
LEGACY SYSTEMS: Legacy environments often lack connectivity, requiring integration layers such as edge gateways and protocol translation to enable communication. When OT systems are connected to IT networks, they can pose significant risks if they are not modernized from a security perspective and fully incorporated into the IT security strategy. In addition, legacy environments typically suffer from data silos that limit visibility and prevent unified insights. Integrating systems within a legacy environment is often the most difficult challenge, one made harder by skills gaps or cultural misalignment between IT and OT stakeholders.
DATA AND SYSTEMS SILOS: In manufacturing, 37% of organizations cite the inability to capture, understand, interpret and use data effectively as their biggest internal obstacle to growth. At the same time, research shows that while organizations anticipate investing in AI and analytics initiatives, fewer plan to invest in the underlying technologies that enable these advanced capabilities — an indication that COOs may underestimate the necessity of the right data foundation. Some organizations establish spheres of automation only to find they cannot scale due to siloed systems and data. Leveling up and expanding data connectivity is the key to leveraging platform capabilities that drive ROI.
SECURITY RISKS: Forty-six percent of manufacturing organizations surveyed in 2025 had experienced a cyber incident in the past year. Increasingly, cybersecurity attacks involve both IT and OT systems. This dual-systems tactic, comprising 60% of intrusions, is significantly more common than attacks involving only OT (22%) or only enterprise IT (17%). Moreover, IT security teams have incomplete visibility into OT systems: 62% of organizations said they have about 75% visibility, and 30% said they have about 50% visibility. Closing these visibility gaps is crucial. Converged systems increase the attack surface, with integration points between IT and OT systems posing a particular vulnerability.
KNOWLEDGE GAPS: A red flag for early-stage maturity is overreliance on the institutional knowledge of individual employees, who can become the sole source of information about key equipment and processes. This dependency becomes problematic when these individuals leave the company and take years’ worth of knowledge with them without a clear path for how the organization will adapt. By contrast, when sensor data, connected systems and predictive analytics are the source of data and insights, processes are consistent and the risk of disruption is lower.
Many teams must also adjust to prioritizing predictive data instead of historical information. While manufacturing has long relied on understanding what happened, emerging technologies are designed to help organizations become future-focused: anticipating issues in maintenance, inventory and other areas so they can respond proactively.
TEAM MISALIGNMENT: Differences between IT and OT priorities, skill sets and governance models create friction that can stall projects and must be addressed effectively. As organizations mature, IT and OT teams must collaborate in new ways to align priorities and execution. Historically, for example, OT and production teams were likely not involved in IT’s security incident response planning — just one example of the fact that organizations will need to think broadly about adapting people and processes for the technological changes underway.
Although 59% of manufacturers have moved smart technologies from pilot to production, data issues frequently stall momentum and impede scalability. Accordingly, data integration platforms are a fundamental component of modernization, working in concert with networking and security to provide the foundation for agentic AI, autonomous operations and IoT maturity.
PLATFORM STANDARDIZATION: Unified IoT platforms simplify integration and enable scalability. Over time, manufacturing plants and production lines often accumulate technology stacks that have little consistency between them. Standardization allows for a unified management plane, shared protocols and cross-organizational visibility. Those capabilities, in turn, enable the data streaming and analytics that drive automation and ultimately AI. Moreover, industrial IoT platforms are designed to support high-value use cases that can transform operations, such as predictive maintenance and remote monitoring.
EDGE-TO-CLOUD DESIGN: An edge-to-cloud architecture lets organizations balance local processing with cloud analytics for performance and flexibility. In manufacturing, edge processing is a growing priority for organizations that want to leverage AI-enabled edge devices. Edge compute allows for speed and low latency close to the point of data generation, while the cloud handles more compute-intensive workloads. A single control plane provides the visibility and tools to streamline the management of these resources — ideally, within the same platform that organizations use to bring systems together.
DATA AND AGENTIC AI: Data must be able to move reliably and securely between IT and OT, becoming normalized and integrated across enterprise systems via centralized platforms. That means IT and OT teams must collaborate on data strategy, ensuring that OT data can be integrated without affecting productivity and operations and that IT has full visibility into assets on the network. Moreover, agentic AI requires data that is clean, connected and accessible in real time. For example, agents could collect data from physical AI systems, such as robotics, to create analyses that currently require spreadsheets and manual workflows. To achieve that, however, most organizations will need to mature their data capabilities and establish strong governance.
SECURITY AT EVERY LEVEL: Security plays a critical role in enabling business strategy: 37% of manufacturing organizations plan to secure IT/OT infrastructure to drive positive business outcomes over the next five years. Security must be embedded at every layer, from devices to applications, with data protected by encryption, identity management and access controls. Network segmentation establishes virtual walls between systems, creating secure zones for sensitive data and mitigating the potential impact, while threat monitoring solutions should include OT-specific capabilities. Finally, a zero-trust approach should be a priority across operations: Verify everything and trust nothing.
ORGANIZATIONAL OUTCOMES: Modernization helps organizations overcome the constraints of legacy environments, especially the barriers that legacy systems pose to agility and growth. In manufacturing, leaders are investing in factory automation, sensors and vision systems with the expectation of transforming how products are made and boosting their competitive advantage. The driver for all of these advances is data — integrated, analyzed, used to automate and empower AI systems — with IT/OT convergence as the overarching strategy that brings everything together.
Accelerate your IoT maturity journey with a CDW assessment or transformation workshop.
Jill Klein
Head of Emerging Technology and IoT
