The Next Revolution Will Be Data-Driven
The industrial sector is undergoing yet another revolutionary shift. Increasingly connected machines, equipment, warehouses, and facilities produce vast amounts of operational data every single day.
But gathering data is just the first step.
The true potential is unlocked when the data becomes intelligence enabling improved decision-making. And this is where Artificial Intelligence and the Internet of Things (AIoT) meet.
From Connectivity to Intelligence
The IoT allows connecting physical assets with digital systems via sensors, RFID technologies, gateways, and connected equipment, which could become a source of significant operational insight.
Organizations will be able to track assets, monitor their equipment, see how the inventories move, and register the surrounding environment in real-time.
But classic monitoring mostly offers one answer:
What is going on?
AI is capable of answering more relevant questions:
- Why does it happen?
- What will happen next?
- What are the unusual patterns?
- Where do the inefficiencies form?
- What steps need to be taken?
By means of machine learning algorithms, organizations are able to process vast volumes of data and derive the insights from them.
Reasons Why Industrial AI Needs More Than Just a Model
Industrial AI is not the same thing as a digital application.
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Physical systems create dirty data. There are possibilities for sensor failures, network disruptions, and industrial equipment having to operate side-by-side with new technologies.
Thus, data quality, connectivity, security, integration, and workflow design are as critical as the model itself.
Even the most accurate prediction is of no use if no action can be taken because of it.
The whole point is to integrate intelligence into the operation process.
From Data to Action
The development of AIoT systems could go through the following stages:
Visibility → Monitoring → Prediction → Recommendation → Action
At first, the company can monitor the asset, then the state of that asset, predict any problems with it, make recommendations on how to solve them, and in the end link them with the process itself.
Practical Application Areas
A combination of artificial intelligence and IoT can enable practical applications including asset tracking, inventory optimization, workforce safety, access control, predictive insights, and industrial operations.
Aperture Venture Studio creates AI + IoT startups that are dedicated to addressing real-world problems with AIoT technology. Discover more at apertureventurestudio.com.
The key takeaway is that industrial innovation must start from problems and not technologies only.
Artificial intelligence delivers the insight. IoT delivers connectivity. Data delivers context. Industrial know-how delivers action.
This way, the next industrial revolution will not be about gathering any more information. It will be about understanding what is going on in the physical world and helping people to make better decisions.
