Artificial Intelligence

Separating IT and OT is still necessary, but the perimeter is no longer enough. Industrial systems now exchange data with historians, cloud platforms, remote vendors, patch servers, backup platforms, and monitoring tools. A mature model combines governance, asset visibility, segmentation, identity, remote access, endpoint protection, vulnerability management, backup, monitoring, and incident response.

An inventory updated once a year cannot support real risk management. Organizations need visibility into connected assets, critical systems, normal flows, obsolete devices, vulnerable systems, engineering workstations, backup status, and monitored assets. Visibility transforms inventory from documentation into a security capability. Without visibility, decisions are based on assumptions. With visibility, they are based on facts.

A network diagram does not protect an industrial environment. What matters is controlling which systems communicate, through which protocols, on which ports, and for which purpose. This is critical for SCADA, DCS, PLC networks, safety systems, engineering stations, historians, backup servers, patch servers, remote access platforms, and OT DMZs. The goal is to reduce propagation and limit the blast radius.

Remote access is necessary for vendors, integrators, and internal teams, but it is also a major entry point. It must rely on named users, strong authentication, approvals, time-limited sessions, role-based permissions, jump servers, session recording, logging, access reviews, and SOC monitoring. Remote access must be approved, limited, monitored, and auditable.

Operator and engineering workstations are highly sensitive because they operate processes and modify controller logic. Protecting them requires more than antivirus. It requires control over administrator rights, USB usage, boot sequence, BIOS settings, application installation, OS hardening, patch levels, golden images, backups, physical access, change management, and security logging. The goal is to preserve the integrity of the industrial function.

Patching in OT is difficult because vendor validation, compatibility, rollback, safety, and maintenance windows must be considered. Vulnerability management should focus on risk reduction, not only patch deployment. Depending on the case, the right action may be to patch, isolate, monitor, restrict access, disable a service, or apply virtual patching.

Having backups does not guarantee recovery. The real question is whether the organization can restore the right systems, in the right order, within the required time, without additional risk. Recovery must cover servers, workstations, PLC programs, DCS configurations, safety logic, firewall rules, switch configurations, HMI projects, licenses, and documentation. Backups must be tested and procedures documented.

OT monitoring requires industrial context. A new RDP session, PLC programming activity, or communication change may be normal during maintenance but critical during production. Detection should focus on unauthorized engineering activity, remote access anomalies, controller logic changes, abnormal protocol behavior, lateral movement, firewall events, malware alerts, backup failures, patch failures, and configuration changes.

An OT cyber incident can quickly become a production crisis. Response must involve cybersecurity, operations, maintenance, engineering, management, vendors, and communication teams. A strong plan defines leadership, escalation, vendor coordination, isolation authority, production decisions, restoration validation, executive communication, evidence handling, and restart approval.

Demand compliance from your Cloud providers — or rethink your Cloud strategy immediately.

Below are the non-negotiable requirements imposed by the framework:

Compliance is now a strategic governance issue — not merely a technical IT matter.

One of the most costly cybersecurity misconceptions is assuming that the Cloud provider is fully responsible for security. The DGSSI framework reinforces the principle of shared responsibility, and ignorance is no longer a valid defense.

Your organization remains accountable for securing what it places in the Cloud.

The new framework elevates this responsibility from best practice to regulatory obligation.

Modern industrial environments demand more than fast and reliable data exchange. They require contextualized, interoperable, and secure information flows capable of powering smart industrial systems. Protocols such as OPC UA address these needs by introducing object-oriented data models, platform independence, and built-in cybersecurity mechanisms designed for next-generation industrial automation systems.

By transforming raw signals into meaningful information, industrial communication protocols now support advanced analytics, predictive maintenance, energy optimization, and digital twins. In this context, communication networks are no longer passive carriers; they actively enable industrial intelligence embedded within industrial automation and control systems.

This transition marks a shift from basic connectivity toward communication architectures aligned with Industry 4.0 principles.

With increased connectivity comes increased exposure to cyber threats. Today’s industrial communication protocols must incorporate authentication, encryption, access control, and continuous monitoring to protect interconnected industrial control systems.

Cybersecurity is no longer an add-on but a fundamental design requirement within modern industrial automation systems. A modern industrial protocol is evaluated not only by its performance and reliability, but also by its ability to protect critical assets and ensure long-term operational resilience in the era of digital transformation in industry.

Proper project scoping isn’t about listing features, it’s about defining boundaries, complexity, dependencies, and assumptions within the digital architecture and transformation roadmap.

When scoping is incomplete, hidden technical constraints remain undiscovered, integration complexity is underestimated, data ownership is unclear and governance flows are not validated across digital systems and platforms.

Every vague area in the scope becomes a future negotiation, and negotiations during delivery are never comfortable in digital transformation programs. A project cannot be agile if its foundations are unclear from a digital governance and architecture perspective.

This is a common pattern in digital projects and digital consulting engagements. To build trust or secure the deal, pricing is sometimes set lower than real complexity requires in complex digital transformation initiatives.

The intention is positive:
“We’ll make it work.”
“We’ll optimize internally.”
“It’s fine, we’ll manage.”

But digital transformation does not reward structural underestimation in digital execution environments. If pricing is not aligned with scope reality, teams start under pressure, scope becomes a battlefield, change requests multiply and quality risks increase across the digital project lifecycle.

What started as a commercial gesture turns into frustration on both sides, because in the end, clients don’t remember the attractive price, they remember the tension experienced throughout the digital delivery process.

One of the most overlooked causes of digital project failure is a weak or skipped Sprint 0 in digital transformation projects. Sprint 0 is not administrative overhead, it’s where technical reality meets strategic ambition and where digital architecture decisions are validated.

This phase is an opportunity to validate architecture choices, integration patterns, API availability, security constraints, and data readiness before full-scale digital implementation begins. I have seen integration work delayed simply because Swagger documentation for APIs was not available at the start of a digital transformation program.

Development teams were ready, business was aligned, but without proper API definition and validation, integration became guesswork… And guesswork in digital projects always costs time and increases digital transformation risk exposure.

A rushed Sprint 0 creates invisible technical debt before the first sprint even begins within the digital delivery framework.

Fast timelines are reassuring, but ambitious planning that ignores API maturity, environment readiness, decision cycles, validation committees, and integration dependencies… Isn’t efficiency in digital project management. It’s deferred disappointment in digital transformation execution.

Speed without structural clarity simply moves the problem into the future, and the future always comes faster than expected in complex digital ecosystems.

As industrial processes became more complex, industrial automation and control systems expanded beyond individual machines. System integration emerged as a critical capability, enabling coordination across production lines, plants, and utility systems. SCADA and DCS platforms evolved to provide centralized supervision, alarm management, and historical data collection across integrated industrial automation systems.

At this stage, automation systems began to generate large volumes of operational data. However, this data was primarily used for monitoring and troubleshooting rather than strategic analysis. Control remained the core function, but the foundations for data-driven operations and future industrial intelligence were quietly being laid.

The digital transformation of industry marked a decisive shift in the role of automation. Advances in connectivity, Industrial Ethernet, standardized protocols, and computing power enabled industrial automation systems to interact with enterprise IT platforms, cloud infrastructures, and analytical tools.

Automation moved from executing logic to interpreting context. Technologies such as advanced analytics, machine learning, and digital twins now allow systems to detect patterns, anticipate failures, and optimize performance in real time. This shift represents the emergence of intelligent automation embedded directly within operational environments.

Intelligence is no longer external to the automation layer; it is increasingly integrated within smart industrial systems, supporting predictive capabilities and continuous improvement aligned with Industry 4.0 principles.

Despite growing autonomy, intelligent automation does not eliminate the human role. On the contrary, it reshapes it. Modern industrial automation systems support operators, engineers, and managers by transforming complex data into actionable insights. Decision-making becomes faster, more informed, and less reactive.

This collaboration between human expertise and machine-driven industrial intelligence is a defining characteristic of next-generation smart industrial systems and advanced industrial automation and control systems.

In 2026, IT RUN evolves from fixing incidents to actively managing digital experience and business perception. The focus is no longer limited to restoring service quickly - it is about ensuring users never feel disruption in the first place. 

● New technical indicators emerge, like transaction success rates, page load times, API response times. 

● Introducing Experience Level Agreements (XLAs) alongside traditional SLAs:

○ Customer Satisfaction Score (CSAT): % of tickets rated 4/5 or 5/5, Monthly satisfaction score per application/service. 

○ Effort & Friction metrics:, User effort score (response to question: How easy was it to resolve your issue ?), % tickets reopen rate 

○ Business Impact metrics: Business Disruption Index (measure of incidents affecting business-critical IT services, weighted by criticality), % Availability of dashboards during financial closing periods, …

○ Digital adoption metrics: Adoption rates of an application (Active users vs Licensed users), Self-Service analytics (% of services accessed with 0 support ticket needed)

○ Proactive experience metrics: % of incidents resolved by automatic monitoring before users report, Incident recurrence rate (% of incident recurrently happening on a monthly basis), Number of recurring defects permanently eliminated 

In 2026, IT RUN is no longer evaluated only on technical stability — it is accountable for measurable financial exposure. The organization must quantify, report, and actively manage the economic impact of technology disruptions, and also closely demonstrate the contribution of its digital platforms to the company’s financial results. 

● Calculating the cost of outage per minute: Calculate the revenue per minute for each critical digital channel, average transaction value and volume per minute, Measure production loss in manufacturing environments (units/hour), Estimate SLA penalties owed to clients due to downtime, Include reputational impact indicators (customer churn %, lost conversions). 

● Linking incidents to financial KPIs: Classify incidents by business domain (Sales, Supply Chain, Finance), Associate incident severity with estimated financial impact, Track cash flow delays caused by ERP or billing interruptions, Measure the cost of emergency changes (involving overtimes for production teams and unplanned costs) vs planned changes. 

● Thinking of IT RUN as a Contributor to company’s revenue Protection: Quantify avoided losses through proactive incident prevention, quantify the optimized infrastructure cost through performance tuning and move-to-cloud strategies, calculate vendor penalty exposure via strong licensing governance using the appropriate tools. 

● Adopting a Technology Business Management (TBM) Mindset: Translate technical services to business services (For example: No longer considering a “Payment API”, but rather “Order-to-Cash Service”), Measure the Total Cost Of Ownership (TCO) per application/service, Align and invoice application/services costs to the business units consuming them. 

In 2026, IT RUN cannot scale effectively without automation and artificial intelligence embedded into its core operating model. As system complexity increases and user expectations move toward real-time responsiveness, manual incident handling and reactive monitoring become operational bottlenecks. Automation is no longer a productivity improvement initiative - it is a structural requirement for stability, efficiency, and proactive service management. 

● AIOps for predictive incident detection : Use machine learning to identify anomalies before they impact users and trigger automated alerts or remediation 

● Self-healing infrastructure: Automate automatic restart, failover, scaling, and configuration rollback without human intervention

● Automatic incident classification: AI-assisted ticket classification, prioritization, and assignment to reduce response time. 

● Automation of repetitive tasks: Automate patching, access provisioning, environment provisioning, and routine system checks. 

● Automation KPIs as performance indicators: Measure automation rate, percentage of incidents auto-resolved, and reduction in manual effort.

As IT RUN becomes directly linked to revenue, resilience, and business performance, leadership roles must evolve accordingly. The modern IT leader is no longer purely technical — He becomes a “Head of Digital Value” and must operate at the intersection of technology, finance, and business strategy, translating operational performance into measurable value. 

• From technical manager to value leader: Shift focus from infrastructure supervision to business impact management, financial accountability, and strategic alignment with enterprise objectives. 

• Communication with CFO & COO: Act as a bridge between IT operations and executive leadership by translating system performance, risks, and investments into financial and operational insights. 

•  Data-driven decision-making: Base prioritization, investments, and improvements on measurable metrics such as revenue impact, cost efficiency, risk exposure, and experience indicators. 

• Strategic storytelling through dashboards: Use executive dashboards to communicate performance trends, financial exposure, and operational resilience in a clear and compelling way that supports strategic decisions.

Today, process industries face new pressures including fluctuating energy markets, stricter environmental regulations, aging assets, and growing demands for efficiency and transparency. These challenges expose the limitations of traditional, isolated DCS architectures within modern industrial automation systems.

To respond, industrial operators increasingly require end-to-end process visibility, real-time performance monitoring across multiple sites, predictive maintenance and asset health insights, and seamless integration with planning, maintenance, and energy management systems.

These evolving requirements have accelerated the convergence between DCS, IT systems, and digital platforms, reinforcing the role of DCS in broader digital transformation in industry strategies.

Modern Distributed Control Systems (DCS) architectures are evolving beyond pure control systems into foundational components of smart industrial systems. The adoption of Industrial Ethernet, open communication standards, virtualization, and OPC UA enables secure data exchange between control layers, edge systems, and enterprise applications.

In mining, oil & gas, and power generation, high-resolution process data generated by DCS feeds advanced analytics platforms for efficiency optimization, emissions reduction, and energy performance management. In water and utilities, data-driven DCS architectures support intelligent process automation, enabling leakage detection, energy optimization, and improved service reliability.

The DCS now acts as a trusted operational data source, bridging real-time industrial automation systems with analytics engines, digital twins, and decision-support platforms. This transformation reflects the emergence of industrial intelligence embedded within industrial automation and control systems.

Defined roles, escalation paths, and structured decision-making processes reduce ambiguity and improve responsiveness in hybrid cloud environments. Mature cloud governance models improve operational efficiency and reduce escalation cycles and decision latency, strengthening overall cloud infrastructure resilience.

Beyond real-time alerts, advanced cloud observability tools analyze performance patterns, infrastructure utilization, workload behavior, and weak signals to prevent service degradation before users are impacted. IT service disruptions typically represent 5–10% of the annual operational impact within large enterprise IT environments, highlighting the strong business value of predictive monitoring and early detection in cloud infrastructure operations.

Metrics such as cloud service availability, mean time to recovery (MTTR), cybersecurity posture, cost efficiency, and infrastructure scalability provide more strategic value than purely technical indicators. Aligning cloud infrastructure KPIs with business outcomes can significantly reduce reactive incident handling and improve overall service availability and performance stability.

Post-incident reviews, systematic root cause analysis, and continuous improvement frameworks are essential to prevent recurring disruptions and improve cloud operations maturity. Organizations with structured cloud incident management practices report a substantial reduction in recurring infrastructure issues and enhanced operational stability.