Industrial AI

From Industrial IoT Data to AI Operational Intelligence

Industrial environments produce copious amounts of data from connected and intelligent machines, assets, sensors, workers, and systems. Yet, capturing that data is only one thing — far more challenging tasks ahead involve interpreting the information that can be gleaned from sensors to make decisions in the physical world.

This is where the confluence of Industrial IoT, AI, and operational systems become especially compelling.

Let’s look at the foundational architecture of such systems.

Industrial AIoT Architecture – The Big Picture

A distilled view of an AIoT architecture can be roughly represented as the following pipeline:

Physical environment → Sensors/devices → Data layer → AI/analytics → Operational decision

Each of these stages represents broad classes of real-world implementations, which can be seen below:

1. Physical environment

The physical environment encompasses the working areas where work typically happens. This could be anything from manufacturing facilities, warehouses, worksites, or anywhere where logistics or production happen — the mines, power plants, and other industrial facilities.

The physical environment includes assets, equipment, workers, and materials that comprise the working environment and have actionable signals.

1. Sensors and connected devices

IoT devices serve as conduits of information from physical objects. Depending on the use case, these could be sensors, tracking devices, access control systems, and other connected devices.

This layer serves to capture and relay the information derived from the physical environment.

1. Data layer

Raw data from devices seldom serves immediate purpose — there is typically a need to contextualize the data in order to make it actionable. The data layer allows for structuring of information, typically incorporating parameters such as time, location, asset, and other relevant variables.

1. AI and analytics

Contextualized data now allows for analysis and modeling to uncover patterns, relationships, and other information of interest. The choice of analytical tools and approaches depends on the problem at hand — machine learning algorithms may be useful in modeling relationships, while simpler methods may suffice for pattern recognition.

Some problems do not even require analytical models — rather, the mere presence of patterns may be used to derive benefits.

1. Operational decision

Finally, the results of analysis need to be channelled into an appropriate end-use decision.

Depending on the system and the use case, this could enable operational improvements such as equipment condition monitoring, asset tracking and visibility, automating logistics and procedures, or even improving workplace safety.

Industrial AIoT is More Than Just Software

A software application typically operates within a digital environment. An industrial application, however, needs to interface with the physical environment, which adds several variables to consider:

Device reliability, connectivity and infrastructure, sensor accuracy and calibration, data integrity, existing systems, physical working conditions, human workflows and constraints, and other variables all come into play. In other words, a technically sophisticated model is only as good as the data it receives and the applicability of its results.

Another important thing to note is that industrial environments have long and established workflows that AIoT solutions must operate with or enhance upon. As such, AIoT systems must be built with deep understanding of the domain and its specific needs.

Problem-First Thinking in AIoT Ventures

One important engineering principle for developing AIoT applications is to think from the problem back. In other words, instead of asking “where can AI be used”, the more useful question is “which physical processes generate detectable and useful signals to inform a particular decision”.

The engineering priorities drastically change depending on the use case. Instead of starting with the characteristics of the AI model, the system design starts with what the model will do, and subsequently, how it has to be built.

This approach is particularly pertinent to AIoT systems.

Building Toward Reusability

Another interesting question concerns reusability — given that any particular AIoT system addresses only one domain-specific problem, how much can be reused from one venture to the next?

A particular AIoT system typically requires components addressing:

• Data ingestion

• Device connectivity

• Asset modeling

• Sensor integration

• Event processing

• AI analytics

• Operational UI

• Workflow integration

Each of these has the potential of being reused in other contexts. This allows for the generic technology components to be decoupled from specific domain knowledge, thus enabling broader applications.

That is one reason why venture-studio approaches to industrial AIoT ventures are particularly compelling — the studio can identify opportunities in various domains and, subsequently, leverage common technology enablers across disparate verticals.

Physical AI and AIoT

The convergence of AI and physical environments touches on the broader concept of Physical AI — a notion of AI systems that operate beyond the virtual realm and engage directly with the physical world.

AIoT solutions are one particular class of Physical AI, which utilize IoT infrastructure as a conduit to the real world.

The fundamental architecture of such systems goes beyond traditional “data → model → prediction” paradigm to incorporate sensing and subsequent actions and decisions in the physical realm.

Depending on the particular application, the actual implementation could take various forms and levels of complexity. Yet, the idea of “sense → understand → decide → act” is fundamentally compelling, and the ability of AI systems to be integrated into the decision making process is what makes them valuable.

Engineering Principles for Physical-AI Ventures

The principle of working backward from problems and decisions is essential for any Physical AI application, including Industrial AIoT systems. One possible approach to designing such systems is to think through the following:

1. What operational problem needs to be addressed?

2. What physical signals are useful in this context?

3. How can such signals be acquired and processed?

4. What data infrastructure is necessary?

5. What analytical approaches are needed?

6. How can the analytical results inform decision making?

7. Is the outcome useful and actionable?

8. What components can be reused?

This approach ensures that the engineering efforts are dedicated to solving real problems. The design process starts with the end in mind, rather than the technology first and the problem later.

For further reading on this topic, Aperture Venture Studio has more resources on Industrial AIoT and Physical AI applications across industry verticals.

The engineering challenges at the intersection of AI and industrial environments are fundamentally fascinating — while simple to describe, they are intimidating to implement. The ultimate promise of AIoT lies in marrying the infrastructure and data to make actual decisions in the physical environment.