For years, the Internet of Things (IoT) has been connecting up the physical world
Machines can report their status, cars their positions, warehouses their inventory, and sensors report data such as temperature, vibrations, pressure and movements.
But connectivity is one thing; understanding what it means is another.
The next step is about getting value from all this connectivity and data, which is where AIoT (Artificial Intelligence of Things) comes in.
AIoT combines connected physical systems and artificial intelligence to enable organizations to evolve from merely collecting information to deriving insights and generating actions.
From Collecting Information to Gaining Insights
Imagine a system that monitors a machine using sensors.
A typical IoT setup would be able to report if a machine started to vibrate more than usual.
That's useful information, but an AI-enabled system could take that data and other factors into account and potentially determine that the machine is operating outside of its normal parameters.
That results in a simple process:
Connect → Observe → Understand → Predict → Act
IoT can provide the first two components in this equation, but by adding AI, the next three steps become possible (or at least much more viable).
The final step, taking action, would require this system to be connected to an actual process.
Industrial Environments are Natural Fit for AIoT
Industrial environments are filled with physical systems that generate data.
Factories have machinery, warehouses have inventory, logistics have vehicles, and energy has systems.
This data is connected in complex ways.
A machine can affect the manufacturing process, a warehouse can impact logistics, and an anomaly in equipment can present maintenance and safety challenges.
AIoT can provide insights into this complex web of cause and effect, enabling a more contextual view of data.
Some applications for this type of system could include:
Predictive maintenance
Asset tracking and visibility
Equipment monitoring
Inventory optimization
Industrial safety
Energy management
Automated inspections
Operational analytics
The goal is not to apply AI just for the sake of using it, but to actually solve a physical-world problem.
AIoT and Physical AI
Another way to think about AIoT is that it is part of a larger trend towards Physical AI.
Most traditional AI systems work with text, images, data or other digital constructs.
Physical AI works with systems that involve machinery, robotics, sensors, vehicles and more.
An intelligent system that involves computer vision (cameras and sensors) that are connected, monitored and maintained by people using AI to spot patterns and anomalies would be an example of this.
It's similar to the concept of augmented intelligence, wherein technology is used to enhance, rather than replace, humans.
The process becomes one of:
Physical world → Data → AI → Decision → Physical action
As these connections and processes grow more sophisticated, systems blur the lines between software and hardware.
Creating Opportunities Around Physical Challenges
One of the interesting aspects of AIoT is that it can lead to new opportunities.
Instead of looking at where you can use AI, you can look at problems in the physical world that can benefit from AI.
Questions such as the following could be asked:
How can equipment malfunctions be predicted?
How can organizations gain better visibility into their assets?
How can industrial workers and processes be made safer?
How can warehouses track their physical inventory in real time?
How can operations data be used to make decisions?
The technology can then be applied to this challenge.
This problem-first approach is also relevant to venture creation; Aperture Venture Studio describes a system-first, venture-second approach, wherein the focus is on building AI and IoT platforms for the physical world and industrial environments.
Five Questions for Successful AIoT Projects
An AIoT project is not simply about connecting up a system with sensors, and throwing in some form of AI.
Organizations should consider a number of factors before launching an AIoT initiative, including the following five questions.
- What problem are we trying to solve?
Having a specific operational goal will be far more helpful than a vague desire to use AI.
- What data do we need?
More data is not always better.
The important consideration is identifying what data is relevant and which it is not.
- Can we trust our data?
The quality and reliability of sensors, connections, assets, labels and more affect the value and accuracy of the insights derived from them.
- What decision will this information enable?
A predictive model is only valuable if it supports a decision, action or other tangible business outcome.
- What comes next?
The process after a decision or prediction is made may be just as important as the decision itself.
The next steps in a workflow can be just as important as any model or prediction.
Looking Toward the Larger Opportunity
AIoT is not just about intelligent devices.
The larger opportunity is about building intelligent systems.
A connected system can be valuable, but an intelligent system of interconnected machinery, assets, processes, people and more could be much more valuable.
While a connected machine can provide status, an AI-enabled machine can provide insight.
A connected network of systems can offer something far more powerful: the ability to continually learn and improve.
That's the next frontier of industrial intelligence.
The biggest challenge is not that every physical machine and component will get AI.
It is finding specific physical problems where greater data, analysis and insights will lead to better outcomes.
Once that opportunity is defined, the technology can be applied to it.
That's the real value of AIoT.
For years, the Internet of Things (IoT) has been connecting up the physical world
Machines can report their status, cars their positions, warehouses their inventory, and sensors report data such as temperature, vibrations, pressure and movements.
But connectivity is one thing; understanding what it means is another.
The next step is about getting value from all this connectivity and data, which is where AIoT (Artificial Intelligence of Things) comes in.
AIoT combines connected physical systems and artificial intelligence to enable organizations to evolve from merely collecting information to deriving insights and generating actions.
From Collecting Information to Gaining Insights
Imagine a system that monitors a machine using sensors.
A typical IoT setup would be able to report if a machine started to vibrate more than usual.
That's useful information, but an AI-enabled system could take that data and other factors into account and potentially determine that the machine is operating outside of its normal parameters.
That results in a simple process:
Connect → Observe → Understand → Predict → Act
IoT can provide the first two components in this equation, but by adding AI, the next three steps become possible (or at least much more viable).
The final step, taking action, would require this system to be connected to an actual process.
Industrial Environments are Natural Fit for AIoT
Industrial environments are filled with physical systems that generate data.
Factories have machinery, warehouses have inventory, logistics have vehicles, and energy has systems.
This data is connected in complex ways.
A machine can affect the manufacturing process, a warehouse can impact logistics, and an anomaly in equipment can present maintenance and safety challenges.
AIoT can provide insights into this complex web of cause and effect, enabling a more contextual view of data.
Some applications for this type of system could include:
Predictive maintenance
Asset tracking and visibility
Equipment monitoring
Inventory optimization
Industrial safety
Energy management
Automated inspections
Operational analytics
The goal is not to apply AI just for the sake of using it, but to actually solve a physical-world problem.
AIoT and Physical AI
Another way to think about AIoT is that it is part of a larger trend towards Physical AI.
Most traditional AI systems work with text, images, data or other digital constructs.
Physical AI works with systems that involve machinery, robotics, sensors, vehicles and more.
An intelligent system that involves computer vision (cameras and sensors) that are connected, monitored and maintained by people using AI to spot patterns and anomalies would be an example of this.
It's similar to the concept of augmented intelligence, wherein technology is used to enhance, rather than replace, humans.
The process becomes one of:
Physical world → Data → AI → Decision → Physical action
As these connections and processes grow more sophisticated, systems blur the lines between software and hardware.
Creating Opportunities Around Physical Challenges
One of the interesting aspects of AIoT is that it can lead to new opportunities.
Instead of looking at where you can use AI, you can look at problems in the physical world that can benefit from AI.
Questions such as the following could be asked:
How can equipment malfunctions be predicted?
How can organizations gain better visibility into their assets?
How can industrial workers and processes be made safer?
How can warehouses track their physical inventory in real time?
How can operations data be used to make decisions?
The technology can then be applied to this challenge.
This problem-first approach is also relevant to venture creation; Aperture Venture Studio describes a system-first, venture-second approach, wherein the focus is on building AI and IoT platforms for the physical world and industrial environments.
Five Questions for Successful AIoT Projects
An AIoT project is not simply about connecting up a system with sensors, and throwing in some form of AI.
Organizations should consider a number of factors before launching an AIoT initiative, including the following five questions.
- What problem are we trying to solve?
Having a specific operational goal will be far more helpful than a vague desire to use AI.
- What data do we need?
More data is not always better.
The important consideration is identifying what data is relevant and which it is not.
- Can we trust our data?
The quality and reliability of sensors, connections, assets, labels and more affect the value and accuracy of the insights derived from them.
- What decision will this information enable?
A predictive model is only valuable if it supports a decision, action or other tangible business outcome.
- What comes next?
The process after a decision or prediction is made may be just as important as the decision itself.
The next steps in a workflow can be just as important as any model or prediction.
Looking Toward the Larger Opportunity
AIoT is not just about intelligent devices.
The larger opportunity is about building intelligent systems.
A connected system can be valuable, but an intelligent system of interconnected machinery, assets, processes, people and more could be much more valuable.
While a connected machine can provide status, an AI-enabled machine can provide insight.
A connected network of systems can offer something far more powerful: the ability to continually learn and improve.
That's the next frontier of industrial intelligence.
The biggest challenge is not that every physical machine and component will get AI.
It is finding specific physical problems where greater data, analysis and insights will lead to better outcomes.
Once that opportunity is defined, the technology can be applied to it.
That's the real value of AIoT.
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