Artificial Intelligence (AI) is all about algorithms, models, and computational power. However, when moving AI into the industry, there is another crucial factor – real-world data.
Industries such as factories, warehouses, logistic systems, infrastructure, and many others have massive amounts of information generated by machines, sensors, operators, and systems. The problem is in making it intelligent enough to drive actual decisions.
The difference between information and valuable intelligence
A sensor can tell you that temperature of a certain machine has changed.
This is information.
The ability to assess whether this change is normal or maybe even dangerous or somehow linked to other operational conditions – this is where intelligence comes into play.
Industrial AI works with:
- Sensor readings
- Conditions of the equipment
- Location information
- Manufacturing information
- Information about energy consumption
- Quality information
- Operating patterns in the past
The closer this information corresponds to real operating conditions, the more valuable it is to develop practical solutions.
Challenges of Industrial Data
Real-world data is never perfect.
Industrial systems can provide information that is missing some values, has noise, inconsistencies, changing operating conditions and information from other systems that doesn't easily fit into the picture.
This leads to a number of challenges that are not always apparent when developing AI on top of specially prepared data sets.
Therefore, any AI system intended for industrial use needs to take the environment, where the data was generated, into account.
AI Model that works great in testing might require an additional validation before being used in production environment.
AI needs context
Any information becomes valuable in its operational context.
Whether an increase in machine vibration means different things depending on:
- Machine speed
- Current load
- Operating mode
- Maintenance history
- Temperature
- Past behavior of equipment
This is why industrial AI is not only about putting more data into the model.
It is also about understanding which data is important, how signals relate to each other and what kind of decisions should be made based on this insight.
From sensors to decisions
Practical AIoT solution can be understood as:
Physical Environment → Sensors → Data → AI → Insight → Decision → Action
IoT is able to connect physical environment to digital world. Then AI is able to interpret the information provided by this connected systems.
combination can be used in various applications like predictive maintenance, asset visibility, quality monitoring, process optimization, and workforce safety.
But at the end of the day, the aim is still the same: making information actionable.
Start From the Operational Problem
One of the most important principles of designing Industrial AI solutions is to start from the problem rather than technology.
Not to ask:
"Where can we apply AI?"
But to ask:
"Which operational decision can be made better by having better information?"
This way of thinking can help to determine what information is needed, what technologies are best suited, and how to evaluate the result.
It can also avoid creating complicated systems that provide information but don't improve any workflows.
Where AIoT Helps
Combining AI and IoT gives us an opportunity to integrate digital intelligence and physical operation.
At Aperture Venture Studio, we are focused on developing AI + IoT ventures that solve industrial problems. Our approach involves identification of industrial problem, development of AIoT systems based on real data and deployment, validation through customer engagement, and scaling successful concepts to venture status.
From Proof of Concept to Production
Deploying an AI solution from PoC stage to the production is not just about achieving high model performance.
But also about:
- Data consistency
- Hardware limitations
- Connection issues
- Integration
- Security
- Human interaction
- Monitoring model work
- Changing operating conditions
All these aspects become even more critical when AI is applied to physical processes.
Conclusion
AI becomes valuable for industries when it takes into account realities of the environment it is used in.
Real world data → contextual intelligence → informed decisions → tangible results
It is not about collecting as much data as you can or applying the most complicated AI model.
It is about creating systems that understand real-world conditions and help people make decisions.
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