Artificial intelligence can recognize patterns, whereas IoT can establish connections between physical assets and digital systems. When combined together, these two technologies form AIoT – the systems which combine observations in the physical world with analytical capabilities of AI.
However, building useful AIoT systems is not about establishing connections between the physical sensors and an AI model.
Start With the Challenge
Every practical AIoT project starts from an operational challenge.
For instance:
- Where there is no visibility for the equipment?
- What makes locating assets difficult?
- What is causing repeated production delays?
- What maintenance challenges can be detected before the failure happens?
- Where manual workflow produces redundant work?
Starting with the challenge helps to figure out what data, connectivity and intelligence will be needed.
Connect the Physical World
IoT is the technology used to observe physical environments.
Depending on the application, the connected systems collect information about equipment, assets, people, inventory, production activities, operating conditions, etc.
This establishes the basis for AI-based analytics.
An oversimplified architecture would look like the following:
Physical Environment → Sensors → IoT Infrastructure → Data → AI → Insight → Action
The goal is not just to get more information. The goal is to get a good visibility into what is going on.
Where AI Helps
AI may help to transform the connected data into operational intelligence.
For instance, AI can analyze historical and real-time data to discover the patterns associated with equipment degradation, production bottlenecks, abnormal behavior, resource utilization, etc.
In manufacturing, this can help with predictive maintenance and quality control. In logistics, this can help to increase the visibility of the assets and inventory. In industrial environments, this can help with optimization and safety monitoring.
The application depends on the problem and available data.
Real-World Data Changes Everything
The AIoT systems operate in the physical environments where everything is never as good as in a controlled lab environment.
The sensors can give you noisy data. The equipment may behave differently under different load. The connectivity may be imperfect. And people can interact with the processes in the way that cannot be replicated by the software.
This is why real-world deployment becomes critical.
An accurate and efficient model in the controlled environment must also prove itself capable of delivering useful insights in real conditions.
From Insight to Action
Another pitfall in working with an AI + IoT model is stopping at analytics.
A dashboard can show an anomaly, but the real value is when this information enables making an informed decision.
For example:
Detection → Alert → Investigation → Decision → Action
Here we get a closed loop between technology and operations.
People are still key in the decision-making process because operators, engineers, and maintenance personnel are familiar with the context behind the numbers and know whether it's worth paying attention to the insight generated by the AI.
Build, Validate, and Then Scale
A reasonable way to deal with the challenges is to start with a particular use case instead of trying to turn an entire process into AI.
Teams can identify a problem, develop the corresponding AIoT system, deploy it, gather feedback, and evaluate the effect it produces.
If the AIoT solution proves itself useful, the developed technology can become a framework for other applications.
That is what we call a system-first, venture-second strategy in our work in the Aperture Venture Studio: identify the most valuable industrial problems, develop the AIoT systems based on real data and deployments, validate them via customer feedback and then build the ventures based on successful concepts.
Designing for Measurable Outcome
Every AIoT project should have its goals.
Depending on the application, teams can measure, for instance:
- Downtime reduction,
- Asset utilization optimization,
- Response time improvement,
- Enhanced production visibility,
- Decrease in operational delays,
- Improved maintenance planning, etc.
The exact metric is less important; the key is to have a measurable connection between the technology and the problem.
Conclusion
AIoT is not just AI + sensors.
It's an end-to-end system connecting:
Problem → Data → Infrastructure → Intelligence → Decision → Action
When AI and IoT technologies are used in the right context and with the right goals in mind, they stop being just technology demos and become real systems that can help monitor, analyze, and optimize the physical environment.
Aperture Venture Studio develops AI + IoT ventures for the physical world.
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