For years, predictive maintenance was one of the easiest applications of AI to explain for industrial environments.
Take machine data, find patterns, predict a potential failure and schedule maintenance before the machine stops.
An understandable value proposition, but industrial AI has a much bigger opportunity.
The Larger Opportunity of Industrial AI
Industrial organizations do not simply want to predict if or when a machine is going to fail.
They want to understand where their equipment is located.
How their assets are moving.
What is going on around their manufacturing processes.
What events deserve their attention.
How their operations are changing.
What actions should be taken.
This transforms the opportunity of industrial AI from a narrow focus on predictive maintenance to operational intelligence.
Physical Data Provides Context
Your machine sensors may provide one type of data, but how would an AI system know to pay attention to this information or how it should react?
By tying together data about the physical world, such as the location of equipment, the movement of materials, the timing of production, the environment around machinery and other relevant factors, you increase the value of information from any one source.
This is why the combination of AI and IoT is proving to be so valuable in industrial environments.
IoT provides an understanding of the physical world, while AI helps to derive meaning from that information.
From Monitoring to Decision Making
Monitoring provides information, analytics can reveal insights and AI can decide what actions should be taken.
That distinction is critical, because if an organization is getting thousands of alerts from its industrial AI systems, more alerts are not necessarily better.
The most effective industrial AI applications help prioritize events and make it possible for humans to differentiate between what requires action and what does not.
The Importance of Human Oversight in Industrial Environments
Industrial environments are not neat laboratories.
Decisions can impact workers, equipment, production processes, inventory and much more.
That is why industrial AI applications must have the right checks and balances.
The best systems do not seek to entirely automate decision making and action, but rather aim to provide the most meaningful information and context at the right moment to humans.
The Future of Industrial AIoT Systems
AIoT combines connected physical systems with artificial intelligence.
An architecture can be seen as a progression from identification to sensing to deciding to acting.
Each is valuable, because they contribute something different.
Identification provides a foundation for what sensing is about.
Sensing adds observations.
AI enables insights and decisions.
Decisions are implemented through operations or human action.
A venture studio focused on industrial AIoT such as Aperture Venture Studio helps companies that want to create new approaches to solving industrial challenges using this connected and intelligent approach.
What's Next for Industrial AI?
The next frontier of industrial AI is likely to involve less isolated algorithms and more interconnected systems and operations.
Consider that manufacturing, logistics, mining, energy, transportation, warehousing and other industries represent some of the most complicated physical processes in existence.
That has significant value, because it means there are many opportunities for creating more visible, interpretable and responsive systems.
From a narrow focus on predictive maintenance, industrial AI has the potential to transform into an entirely new mode of interaction with the physical world.
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