Deep Diving into Smart and Integrated Digital Systems

The Digital Operating Layer of ClimateTech
ClimateTech includes technologies that transform how energy is produced, how materials are manufactured, how buildings operate, how food is grown, and how infrastructure responds to environmental pressures. Across these areas, physical innovation increasingly depends on the ability to understand operating conditions and coordinate assets in real time.
Renewable energy needs to respond to demand and grid constraints. Industrial equipment requires precise monitoring and control, buildings must coordinate energy consumption, occupancy, weather, and electricity prices, distributed technologies such as batteries, electric vehicles, heat pumps, and smart meters create new operational relationships that need to be managed.
Digital Intelligence and System Integration is the ClimateTech family that enables this coordination. It brings together sensors, connectivity, data infrastructure, digital twins, artificial intelligence, and control technologies to collect information from physical assets, interpret their condition, and translate that understanding into operational decisions.
Its climate relevance comes from the physical outcomes it enables: lower energy and material consumption, greater renewable integration, reduced equipment losses, longer asset life, more flexible infrastructure, and better measurement of environmental performance.
This article explores the operational challenge this family addresses, the scale of its potential contribution, the technologies being developed, and the companies translating digital capabilities into measurable ClimateTech applications.

What Is Digital Intelligence and System Integration?
Digital Intelligence and System Integration is a ClimateTech family that connects physical assets with the information and decision-making capabilities required to operate them more effectively.

The family covers the complete path from observing a physical condition to implementing and verifying an operational response:

A solution may address one part of this process or integrate several capabilities within the same platform. For example, a sensor company may focus on generating previously unavailable equipment data, while a digital-twin company may use existing data to simulate asset performance. An optimization platform may then translate those models into operational setpoints.
A digital technology belongs to this ClimateTech family when it creates a credible connection between digital activity and a measurable physical or environmental outcome. This connection distinguishes operational ClimateTech solutions from general-purpose software and makes performance verification an essential part of the family.

However, this information remains distributed across equipment, departments, software platforms, and organizations. Different systems may use incompatible communication protocols, data formats, naming conventions, and time intervals and legacy machines may offer limited connectivity, while newer technologies frequently introduce additional platforms that must be integrated into the existing operational environment.
Several specific barriers emerge from this fragmentation:

• Limited operational visibility

A facility may have thousands of available measurements without a unified view of how equipment, energy, materials, and environmental conditions interact. Operators can see individual data points while still lacking the context required to understand the complete process.

• Data without physical context

A sensor reading has limited value when it cannot be reliably connected to the asset, location, process stage, engineering drawing, or maintenance record it represents. Preparing this information for analytics can require extensive manual work before a model can generate useful results.

• Insights separated from operational control

Analytics may identify an inefficiency or predict an equipment failure, yet the recommendation still needs to enter existing maintenance, production, or control workflows. The distance between identifying an opportunity and implementing an action can prevent digital insights from producing physical results.

• High integration costs

ClimaTech solutions need to connect with infrastructure designed decades earlier. Every new facility may have different equipment, software, data availability, safety requirements, and operating practices. Extensive customization increases deployment time and makes it difficult to reproduce a successful pilot across multiple sites.

• Limited evidence of environmental performance

Energy and emissions savings require a defined baseline and appropriate adjustments for changes in weather, production, occupancy, or operating conditions. Without measurement and verification, it becomes difficult to separate the effect of the digital intervention from other changes occurring within the asset.
These barriers are especially relevant for technologies such as digital twins. The National Institute of Standards and Technology identifies the absence of common approaches to vocabulary, interoperability, trustworthiness, verification, validation, and uncertainty quantification as a major obstacle to reliable digital-twin deployment in manufacturing. (NIST)

The problem addressed by this ClimateTech family is therefore precise: physical assets generate increasing amounts of information, while operational environments still struggle to convert that information into coordinated, repeatable, and verifiable action.
Digital Intelligence and System Integration technologies work on closing this operational gap.

The Scale of the Operational Opportunity
Digital Intelligence and System Integration is an area of opportunity in the operations of some of the world’s most energy-intensive activities. By example global final energy consumption exceeded 450 exajoules in 2024. Industry represented nearly 40% of this demand, while buildings accounted for approximately 30%. Industry also contributed around two-thirds of the increase in global energy demand recorded since 2019. (IEA, Energy Efficiency 2025 – Industry; IEA, Energy Efficiency 2025 – Buildings)
This means that even incremental improvements in how industrial equipment, buildings, electricity networks, and other physical assets are operated can influence substantial volumes of energy and emissions.
The International Energy Agency has modeled the potential contribution of existing AI-led operational applications under a widespread adoption scenario. These figures help illustrate the scale of performance that the broader Digital Intelligence and System Integration family could influence:

The IEA’s figures focus on AI-led applications, although the potential described depends on the wider technology family. AI optimization requires sensors capable of observing physical conditions, infrastructure that can connect and contextualize operational data, models that represent asset behavior, and control systems that can safely implement recommendations.
These estimates describe potential under widespread adoption. Reaching them requires overcoming barriers related to data access, interoperability, digital infrastructure, skills, regulation, cybersecurity, and integration with existing assets.
Digital technologies also create additional energy demand. Data centres consumed approximately 415 TWh of electricity in 2024, representing around 1.5% of global electricity consumption. The IEA projects that this demand could reach approximately 945 TWh by 2030, with AI as the main driver of growth. (IEA, Energy and AI: Executive Summary, 2025)

The climate value of this family should therefore be evaluated through its net physical contribution: the energy and resources required by the digital solution compared with the efficiency, flexibility, capacity, resilience, or emissions improvements it enables.

Examples include:

• Wireless industrial sensors
• Smart electricity, gas, heat, and water meters
• Machine-vision systems
• Environmental monitoring devices
• Remote sensing and satellite data
• Batteryless and energy-harvesting sensors
• Connected equipment and Industrial Internet of Things devices

Edge intelligence processes part of this information close to the physical asset. Instead of sending every raw measurement to a central cloud environment, edge devices can filter data, detect anomalies, run machine-learning models, and trigger immediate alerts or responses.
This is particularly relevant for industrial facilities, energy infrastructure, remote agricultural assets, and other environments where connectivity may be limited, latency matters, or operational data must remain within the facility.
For ClimateTech applications, the sensor must generate information that supports a clear physical decision. Sampling frequency, calibration, installation requirements, power consumption, communication range, durability, and maintenance costs all influence whether the technology can scale across thousands of assets.

Interoperability and contextualized data infrastructure
Once data has been captured, it must be connected across equipment and software that may have been designed at different times and for different purposes.
Industrial and infrastructure environments frequently combine programmable logic controllers, SCADA systems, industrial time-series databases, building management systems, maintenance platforms, geographic information systems, meters, engineering documents, cloud applications, and enterprise software.
Data integration technologies create the infrastructure needed to exchange information between these components. Their capabilities can include:

• Industrial connectivity and protocol conversion
• Application programming interfaces
• Edge-to-cloud data pipelines
• Data normalization and time synchronization
• Asset and process models
• Semantic layers and knowledge graphs
• Identity, access, and data-governance tools

Interoperability requires more than moving data from one platform to another. The information must retain its operational meaning. A pressure reading, for example, should be connected to the correct piece of equipment, process stage, measurement unit, operating condition, and point in time.
Contextualized data infrastructure creates these relationships. It allows different applications to understand how measurements, assets, engineering diagrams, maintenance records, production information, and environmental conditions relate to one another.
Standards such as OPC Unified Architecture support platform-independent communication and information modeling across machines, operational systems, and enterprise applications. OPC UA also includes security capabilities such as encryption, authentication, access control, and auditing. (OPC Foundation)
For founders and operators, this layer often determines deployment repeatability. Reusable connectors, standardized data models, and consistent integration methods can reduce the amount of custom engineering required for each new facility.

Digital twins and physics-informed simulation
Digital twins create synchronized virtual representations of physical assets, processes, facilities, or infrastructure networks. They combine operational data with information such as geometry, engineering rules, material properties, historical performance, and environmental conditions.
A digital twin can help operators:

• Represent the current condition of an asset
• Diagnose the cause of abnormal performance
• Predict degradation or failure
• Test operating scenarios
• Compare infrastructure designs
• Estimate capacity under changing conditions
• Plan maintenance and capital investment
• Evaluate the effects of extreme weather

Applications can range from modeling an individual pump or industrial structure to representing an entire building, production process, electricity network, or infrastructure corridor.
Physics-based models use engineering equations to represent how an asset behaves. Data-driven models learn relationships from historical and real-time information. Hybrid or physics-informed approaches combine both methods, allowing the model to learn from operations while respecting known physical constraints.
Model credibility becomes especially important when the digital twin influences safety limits, maintenance decisions, infrastructure investment, or automated control. The National Institute of Standards and Technology identifies verification, validation, uncertainty quantification, interoperability, and trustworthy data management as essential requirements for digital-twin deployment in manufacturing. (NIST)
A visually detailed model therefore provides only one part of the value. Its assumptions, accuracy, data quality, update frequency, uncertainty, and connection with operational decisions determine whether it can support real-world deployment.

AI optimization, automated control, and orchestration
The final group translates connected and contextualized information into operational decisions.
AI and optimization technologies can evaluate large numbers of variables and identify actions that balance several objectives simultaneously. An industrial facility may need to reduce energy consumption while maintaining production volume and product quality. A building may coordinate comfort, weather, occupancy, energy prices, and grid demand. An electricity network may balance renewable generation, storage, flexible loads, and infrastructure constraints.
Relevant technologies include:

• Demand, generation, and weather forecasting
• Anomaly and fault detection
• Predictive maintenance
• Operations research
• Model-predictive control
• Reinforcement learning
• Multi-objective optimization
• Automated scheduling
• Distributed energy resource coordination
• Flexibility and orchestration platforms.

The output may be a recommendation presented to an operator, a maintenance task created automatically, an optimized production schedule, or a new setpoint communicated to an existing control system.
The appropriate level of automation depends on the consequences of error and the maturity of the technology. High-impact applications require defined operating limits, human oversight, explainable recommendations, audit records, fallback procedures, and safe local control during connectivity failures.
The U.S. Department of Energy highlights the integration of real-time data from machines, products, processes, and supply chains into digital twins as a foundation for continuous, human-in-the-loop industrial decision support. (U.S. Department of Energy)

How the four groups connect
These groups create value through their interaction:
Sensors observe the physical asset → integration technologies organize and contextualize its data → digital twins and models interpret possible conditions → optimization and control technologies select and implement an action.
System integration connects each stage and allows the resulting decision to enter existing operational workflows. The environmental outcome can then be measured and compared with an appropriate baseline.
This combined architecture transforms digital capability into measurable ClimateTech performance.

Startups and Scale-Ups Across the Operational Loop
Companies within this ClimaTech family can be positioned according to the stage where they create their primary value: observing physical conditions, connecting operational information, modeling asset behavior, or translating intelligence into action.
Many companies operate across more than one stage. The following classification reflects their principal capability and is deliberately representative rather than exhaustive.