DEV Community: akihsay

Industrial Emissions Monitoring: When Data Becomes Operational Insight

akihsay — Thu, 27 Aug 2026 17:27:55 +0000

An industrial stack is more than an exhaust point—it can provide valuable information about what is happening across a process.

Gases, particulate matter, flow, temperature, and other conditions can change based on how equipment and operations are performing. The right monitoring approach helps turn those changes into useful data.

Common technologies include:

Gas analyzers for measuring selected gases
Particulate and dust monitors for tracking particles in exhaust streams
Opacity monitors for measuring light transmission through emissions
Flow and temperature instruments for understanding stack conditions

The real value is not simply collecting measurements. It is making the data useful for environmental visibility and operational decision-making.

The first question should always be: What do we need to understand, and which measurements can help us understand it?

For additional information about industrial emissions and stack monitoring technologies, visit Emissions and Stack.

EmissionsMonitoring #StackMonitoring #IndustrialEmissions #EnvironmentalMonitoring #GasAnalysis #ParticulateMonitoring #CEMS #IndustrialTechnology #Engineering

When AI Meets the Physical World: Why AIoT Matters for Industrial Innovation

akihsay — Thu, 27 Aug 2026 15:30:32 +0000

AI is transforming how we work with data. IoT is connecting the devices, assets, and environments where real-world work happens.

But the bigger opportunity emerges when these technologies work together.

AIoT (Artificial Intelligence of Things) can help connect physical-world data with intelligent analysis, giving organizations better visibility into assets, equipment, inventory, operations, and workforce environments.

However, technology alone does not create a successful industrial solution.

The key questions are:

What real problem needs to be solved?
Is there a practical technology foundation?
Can the solution work in a real operational environment?
Is there genuine market demand?
Can the opportunity grow into a scalable business?

This is where a venture studio approach becomes interesting. Instead of starting with a technology trend and searching for a use case, innovation can begin with real industrial challenges and validated opportunities.

At Aperture Venture Studio, the focus is on exploring and building AIoT-native ventures for the physical world—connecting technology capabilities with real-world problems and market opportunities.

Learn more about the approach at Aperture Venture Studio.

What do you think is the biggest challenge in bringing AI and IoT solutions from concept to real-world deployment?

AIoT #ArtificialIntelligence #IoT #IndustrialAI #VentureStudio #Industry40 #IndustrialInnovation #DigitalTransformation #SmartManufacturing

A construction site is a constantly changing system. The challenge is not only collecting data it is connecting it.

akihsay — Wed, 26 Aug 2026 17:40:12 +0000

Workers move across the jobsite. Equipment change's locations. Materials are delivered and installed. Project conditions evolve every day.

Technologies such as RFID, BLE, UWB, GPS, IoT sensors, and AI can help connect these physical activities with digital information.

The potential value is greater visibility into workforce activity, equipment utilization, material movement, access, and project progress.

But the technology should start with a real problem.

The best construction AIoT solutions are designed around practical jobsite challenges—not simply around deploying more devices.

For a closer look at how AI and IoT can support connected construction operations, visit CommCon AI.

ConstructionTechnology #ConstructionAI #AIoT #IoT #RFID #SmartConstruction #DigitalTransformation #Innovation

AIoT is not just about connecting more devices it is about making physical-world data more useful.

akihsay — Wed, 26 Aug 2026 15:32:02 +0000

Industrial environments generate data through assets, equipment, materials, sensors, and day-to-day operations. The real challenge is turning that information into meaningful operational intelligence.

This is where Artificial Intelligence + the Internet of Things (AIoT) come together.

IoT can connect the physical environment, while AI can help analyze available information and identify useful patterns. Together, these technologies can support applications involving asset visibility, inventory optimization, workforce safety, access control, and industrial intelligence.

The most important part? Start with the problem—not the technology.

AIoT solutions become more valuable when they are built around real operational challenges and practical industry requirements.

For a closer look at the venture-building approach behind AIoT companies for the physical world: Aperture Venture Studio

AIoT #IoT #IndustrialAI #Industry40 #IndustrialTechnology #DigitalTransformation #SoftwareDevelopment #Innovation

How AIoT Is Helping Connect the Modern Construction Jobsite

akihsay — Tue, 25 Aug 2026 17:21:23 +0000

Commercial construction projects generate information everywhere.

A worker enters the site. Equipment is moved to another floor. Materials arrive at a loading area. A subcontractor begins work in a new zone. A delivery changes the schedule.

Each event creates useful information, but the information is often scattered across different systems, teams, and reports.

The challenge is not always collecting more data.

It is connecting the data that already exists.

This is where AIoT—Artificial Intelligence of Things—can help.

The jobsite is becoming a connected environment

Modern construction projects involve more than blueprints and schedules.

Project teams coordinate people, equipment, tools, materials, deliveries, safety requirements, and constantly changing site conditions.

Technologies such as RFID, BLE, UWB, GPS, IoT sensors, and edge devices can capture information from the physical environment.

AI and analytics can then help organize that information and identify patterns that may be useful for project teams.

The objective is simple:

Turn physical-world activity into meaningful operational awareness.

1. Understanding workforce activity

Construction teams need reliable visibility into site activity.

Depending on the project, connected systems can provide information about site access and workforce presence.

This can help answer questions such as:

Who is active on-site?
Which areas are busy?
How is activity changing throughout the project?
Are site conditions affecting planned work?

The goal is not to replace construction professionals with technology.

It is to provide better information for the people making important decisions.

2. Tracking equipment and tools

Equipment and tools are valuable project resources, but they often move between teams and work zones.

Searching for a missing item can interrupt work.

Connected tracking systems can help project teams improve awareness of equipment location and movement.

However, a location alone is only part of the story.

The greater value comes from connecting location information with operational context.

For example:

Is the equipment available when needed?
How frequently is it being moved?
Is it located near the active work area?
Is it being underused or unavailable?

Connected data can help teams investigate these questions more effectively.

3. Improving material visibility

Materials follow a long journey through a construction project.

They may be:

Ordered → Delivered → Received → Stored → Moved → Installed

Information can be lost between each stage.

AIoT systems can help connect physical-world tracking with material records and operational workflows.

The result is not necessarily a completely automated project.

Instead, teams can work toward a clearer understanding of where materials are and how they are moving through the project.

4. Supporting progress monitoring

Construction progress is traditionally tracked through inspections, reports, photographs, meetings, schedules, and professional experience.

These methods remain important.

Connected technologies can provide additional information to support this process.

When physical activity, location data, material status, and project information are connected, teams can develop a broader view of project execution.

AI can help identify patterns and relationships across these sources.

The technology is only useful when it solves a real problem

Construction technology can quickly become complicated.

A project may have sensors, trackers, dashboards, software platforms, and multiple data sources. If they do not solve a clear operational problem, they may create additional complexity instead of reducing it.

A better approach starts with practical questions:

What information are we missing?

What problem does that information create?

Who needs to use it?

What decision will it improve?

These questions help ensure that technology supports the jobsite rather than simply adding another system.

Building a connected intelligence layer

The biggest opportunity for AIoT may not come from individual devices.

It may come from connecting systems.

Workforce information can provide more context when viewed alongside site access.

Equipment information can become more useful when connected to work activities.

Material visibility can support planning when linked to project requirements.

Progress information can become stronger when combined with multiple sources of jobsite activity.

This is how disconnected signals can begin to form a more complete operational picture.

For a closer look at how these technologies are being brought together for commercial construction, CommCon AI provides information on an AIoT approach that combines AI, IoT, RFID, BLE, UWB, GPS, edge computing, and predictive analytics.

The connected jobsite is not about replacing people

Construction will always require experience, judgment, and coordination.

Technology cannot understand every site condition automatically.

What it can do is help reduce information gaps.

The best systems do not try to replace the people managing a project.

They help those people see more clearly.

Final thought

The modern jobsite already generates valuable information.

The next step is making that information work together.

As AI and connected technologies continue to develop, commercial construction has an opportunity to move from isolated data points toward more connected operational intelligence.

The future may not belong to the jobsite with the most technology.

It may belong to the jobsite where information is easier to understand and use.

CommercialConstruction #ConstructionTechnology #ConTech #AIoT #ArtificialIntelligence #IoT #RFID #UWB #BLE #SmartConstruction #DigitalConstruction #ConstructionInnovation

Building AIoT Systems for the Physical World: From Connected Data to Real Industrial Intelligence

akihsay — Tue, 25 Aug 2026 15:46:22 +0000

AI is becoming more capable, and the physical world is becoming more connected. But connecting devices and generating data are only the starting points.

The bigger challenge is turning that information into something useful.

Industrial environments already produce enormous amounts of data through sensors, equipment, assets, materials, workers, and software systems. The problem is that these sources often operate separately. Teams may have access to information, but not necessarily the context needed to understand the complete operational picture.

This is where AIoT—Artificial Intelligence of Things—becomes important.

AIoT brings together AI and the Internet of Things to help connect physical-world data with intelligent analysis and operational decision-making.

The challenge isn't a lack of data

A manufacturing plant can have connected equipment.

A warehouse can use asset-tracking technologies.

A construction site can collect information from multiple systems.

Yet teams may still struggle with basic questions:

Where is a critical asset right now?
What is happening across the operation?
Which process is creating a delay?
Is inventory information aligned with physical reality?
Which operational changes require attention?

The issue is often not the absence of data.

It is fragmentation.

One system records an asset location. Another contains maintenance records. Another tracks inventory. Another provides workflow information.

When people have to manually connect these data sources, valuable time and context can be lost.

How AIoT can improve operational visibility

IoT technologies can help capture signals from the physical environment. Depending on the use case, this may involve RFID, BLE, sensors, connected devices, cameras, or other technologies.

AI can add another layer by helping analyze the information and identify relevant patterns.

The objective is not to collect technology for its own sake.

It is to answer useful questions.

Asset visibility

Organizations can use connected systems to better understand the location and movement of important assets. AI-driven analysis can help add context to the data rather than presenting location information as an isolated signal.

Inventory and materials

Physical inventory and digital records can sometimes become disconnected. AIoT systems can help improve awareness of inventory activity by connecting data from the physical and digital sides of an operation.

Workforce and operational awareness

Industrial operations involve people, equipment, processes, and changing environments. Connected intelligence can help organizations develop a broader understanding of activity across these areas.

Industry-specific applications

The value of AIoT does not come from applying the same technology everywhere. Manufacturing, logistics, construction, energy, and other industries have different operational challenges.

A useful system must account for those differences.

Why a problem-first approach matter

A common mistake in technology development is beginning with the solution.

“We have this technology. Where can we use it?”

A stronger question is:

“What important problem exists, and what system would help solve it?”
Starting with the problem helps prevent technology from becoming disconnected from real operational needs.

This is especially relevant when building industrial ventures.

A solution can be technically impressive and still fail to create meaningful value. Real-world validation matters because physical operations involve existing infrastructure, established workflows, multiple stakeholders, and practical constraints.

The strongest opportunities are often found where a repeated problem, a practical technology system, and real customer demand intersect.

From system to venture

This is where the venture-studio approach can offer a different path.

Instead of treating every startup as an isolated idea, a venture studio can develop shared knowledge, infrastructure, and technology capabilities across multiple opportunities.

The process can look something like this:

Identify a meaningful industrial challenge.
Develop a system that addresses the challenge.
Test and validate the system in a relevant environment.
Refine the solution based on real-world feedback.
Build a focused venture when the opportunity shows clear potential.

This approach allows innovation to begin with the system and the problem rather than a standalone company concept.

Aperture Venture Studio explores this model by focusing on AI + IoT systems designed around real physical-world and industrial challenges, with the goal of developing validated systems into scalable ventures.

Building technology that works beyond the screen

One of the most difficult aspects of AIoT is that it operates in the real world.

A system may interact with different devices, environments, people, processes, and existing software platforms.

That means successful implementation depends on more than an algorithm or a connected device.

It requires:

Reliable data collection
Context-aware analysis
Integration with operational workflows
Practical deployment
User adoption
Continuous validation

The technology has to work where the work actually happens.

The future of industrial intelligence

The next phase of digital transformation may be less about adding more disconnected tools and more about creating better connections between existing systems.

As AI and IoT continue to converge, organizations will have opportunities to develop a clearer understanding of physical operations in real time.

The most valuable systems will not simply generate more dashboards or collect more information.

They will help answer meaningful operational questions.

What is happening?

Why is it happening?

What deserves attention?

And ultimately:

What can we do about it?

That is the opportunity behind AIoT.

The physical world is already producing data. The next challenge is building systems capable of turning that information into intelligence people can use.

AIoT #ArtificialIntelligence #InternetOfThings #IndustrialIoT #Industry40 #IndustrialAutomation #DigitalTransformation #SmartManufacturing #IndustrialTechnology #OperationalIntelligence #VentureStudio #TechnologyInnovation

Why AIoT Is Becoming Relevant to Commercial Construction

akihsay — Mon, 24 Aug 2026 17:40:29 +0000

Construction sites are physical, dynamic, and constantly changing.

Workers move between tasks. Equipment is shared. Materials arrive at different times. Tools change hands. Project teams need accurate information to coordinate all these moving parts.

The problem is not a lack of data.

The problem is often fragmented visibility.

That is where AIoT can help.

From Connected Data to Jobsite Intelligence

AIoT combines Artificial Intelligence with the Internet of Things.

On a construction site, connected technologies can provide information about real-world activity. RFID, BLE, UWB, GPS, and other systems can support visibility into assets, materials, equipment, and workforce activity.

AI can help interpret that information and connect it to operational workflows.

The result is not simply another dashboard filled with data.

The goal is to provide information that helps answer practical questions:

Where is critical equipment?
Are materials available when needed?
What is happening across the jobsite?
How does actual progress compare with the plan?

The Technology Is Only Part of the Solution

A connected system is valuable only when it solves a real operational problem.

That is why construction AIoT should begin with the jobsite itself: the workflows, bottlenecks, coordination challenges, and information gaps teams experience every day.

CommCon AI focuses on connecting AI and IoT capabilities to commercial construction use cases, including workforce intelligence, equipment visibility, material tracking, site access, and progress monitoring.

The future of construction technology is not just about connecting more devices.

It is about making information from the physical world more useful.

ConstructionAI #AIoT #IoT #ConTech #ConstructionTechnology

AIoT: Turning Industrial Data into Practical Intelligence

akihsay — Mon, 24 Aug 2026 15:41:58 +0000

Industrial operations generate data everywhere.

Equipment reports its status. Assets move between locations. Inventory changes throughout the day. Workers interact with systems, machines, and materials. The challenge is not always the lack of data—it is turning disconnected information into something useful.

This is where **AIoT (Artificial Intelligence of Things) can make a practical difference.

IoT technologies help connect the physical world, while AI helps organizations analyze and interpret the information generated by connected systems. Together, they can support better visibility across industrial operations.

Where can AIoT create value?

AIoT applications can support areas such as:

Asset tracking and visibility
Inventory and operations optimization
Workforce monitoring
Access control and security
Industrial intelligence

The goal should not simply be to add more connected devices. The stronger approach is to start with a real operational problem and determine how connected data can provide better context.

For example, knowing an asset's location is useful. Understanding its location alongside its status, utilization, workflow, and operational requirements can make that information far more valuable.

The same principle applies to equipment, inventory, workforce activity, and industrial processes.

Building for the physical world

Industrial technology must work beyond a presentation or prototype. It needs to fit real environments, existing infrastructure, and everyday workflows.

That is why successful AIoT development requires more than strong technology. It also requires an understanding of customer needs, operational challenges, and how systems work in practice.

Aperture Venture Studio focuses on building and scaling AIoT ventures around real-world industrial opportunities.

The future of industrial transformation is not just about connecting more devices.

It is about creating more useful connections between physical operations and digital intelligence.

AIoT #IndustrialAI #IoT #Industry40 #DigitalTransformation #IndustrialInnovation

Industrial Emissions Monitoring: Turning Stack Data into Useful Insight

akihsay — Fri, 21 Aug 2026 17:09:52 +0000

Industrial emissions monitoring is often viewed as a compliance requirement. But there is another side to it: visibility.

A stack can provide valuable information about what is happening within an industrial process. Depending on the application, monitoring systems can measure gases, particulate matter, flow, temperature, pressure, and other important parameters.

The challenge is making those measurements reliable and useful.

Why Monitoring Matters

Industrial processes constantly change. Production levels, fuel, raw materials, equipment conditions, and operating parameters can all influence emissions.

Without dependable monitoring, it can be difficult to recognize these changes.

With appropriate monitoring, teams can gain better awareness of:

Gas emissions
Particulate and dust levels
Stack flow
Temperature
Pressure
Process trends

This information can support environmental monitoring while also providing useful operational context.

The Technology Should Follow the Problem

There is no universal monitoring solution for every industrial facility.

The right approach depends on what needs to be measured, the operating environment, required measurement frequency, and how the resulting data will be used.

For example, a facility may need continuous emissions monitoring, gas analysis, particulate measurement, or a combination of different measurements.

Starting with the actual requirement helps prevent technology from becoming the goal instead of the solution.

Data Is Only the Beginning

Collecting measurements is important, but data alone does not automatically create insight.

The real value comes from understanding what the measurements mean.

A change in emissions could be associated with a change in process conditions. A developing trend could indicate that something deserves investigation. Connecting monitoring data with operational information can provide a broader view of what is happening.

That is where modern monitoring systems can become especially useful.

Organizations exploring industrial emissions and stack monitoring technologies can find additional information at Emissions and Stack.

Looking Ahead

Industrial monitoring is becoming increasingly connected, making it possible to collect and access information more efficiently.

But the foundation remains the same:

Measure accurately. Understand the data. Take informed action.

When emissions monitoring is approached this way, it becomes more than a compliance exercise. It becomes another source of operational intelligence.

EmissionsMonitoring #StackMonitoring #CEMS #IndustrialTechnology #EnvironmentalMonitoring #AirQuality #IndustrialIoT #Sustainability

AIoT: Where AI Meets the Physical World

akihsay — Fri, 21 Aug 2026 15:43:05 +0000

AI can analyze data. IoT can capture what is happening in the physical world. When combined, they create AIoT—a practical approach to turning real-time data into useful operational intelligence.

The potential becomes clear in industries where physical activity matters every second. Connected sensors and devices can provide visibility into assets, inventory, equipment, and work environments, while AI can help identify patterns and support better decision-making.

For example, AIoT can support:

Asset visibility by providing real-time information about the location and status of important resources.
Inventory optimization by improving visibility into materials and movement.
Workforce monitoring by supporting greater awareness of activities and operational conditions.
Operational intelligence by connecting data from multiple sources and making it easier to understand.

The key is to focus on the real problem before choosing the technology.

Collecting more data does not automatically create value. The real value comes from connecting the right data to the right insights and enabling meaningful action.

That is why a system-first approach matter. Organizations need to consider how data is collected, connected, analyzed, and ultimately used in the real world.

To explore how AI and IoT can come together to address physical-world challenges, visit Aperture Venture Studio.

The future of AIoT is not just about smarter technology.

It is about making technology more useful in the places where real work happens.

AIoT #AI #IoT #IndustrialIoT #Industry40 #SmartManufacturing #DigitalTransformation #IndustrialInnovation #Technology #Innovation

Your Stack Is Telling a Story: How Modern Emissions Monitoring Turns Measurements into Insight

akihsay — Thu, 20 Aug 2026 16:52:42 +0000

Industrial emissions aren't just something that leaves a stack.

They can provide valuable information about what is happening inside an industrial process.

Gas concentrations, particulate levels, flow, and temperature all tell different parts of the story. The challenge is making those measurements useful.

That's where modern emissions and stack monitoring comes in.

Start With the Right Questions

Before choosing monitoring equipment, it's worth asking a few simple questions:

What needs to be measured?
Why does it need to be measured?
How often should measurements be taken?
What conditions will the equipment operate in?
How will the resulting data be reviewed and used?

These questions sound basic, but they can make a major difference when selecting monitoring technology.

Gas Analysis: More Than a Number

Industrial processes can produce gases such as NOx, CO, SO₂, and O₂.

Gas emission analyzers can provide measurements of these parameters, giving industrial teams information about conditions in stack emissions.

The value comes from having dependable measurements that can be interpreted within the context of the process.

Particulates Tell Another Part of the Story

Gases aren't the only concern.

Industrial processes can also produce particulate matter and dust.

Technologies such as opacity monitors and triboelectric dust monitors can provide different approaches for particulate monitoring, depending on the application.

When particulate data is considered alongside gas measurements, teams can develop a broader understanding of emissions conditions.

Flow and Temperature Add Context

Imagine looking at emissions data without knowing anything about the movement or temperature of the stack gas.

That's where flow and temperature monitoring can add another layer of information.

Understanding these parameters can help provide context around stack conditions and the behavior of gases moving through the system.

The Rise of Connected Monitoring

Industrial monitoring is also becoming increasingly connected.

IoT-enabled technologies can help move monitoring data beyond individual instruments by supporting remote access, centralized visualization, and data analysis.

This doesn't mean that every monitoring application needs a complex digital ecosystem.

It means connectivity can be valuable when it solves a real problem—such as improving access to data or reducing the difficulty of monitoring multiple points.

Better Data, Better Visibility

The purpose of monitoring isn't to collect as many measurements as possible.

It's to collect the right measurements and make them useful.

That means thinking about instrumentation, connectivity, data access, operating conditions, and the people who ultimately need to use the information.

This is where modern emissions monitoring is heading: from isolated measurements toward more connected and informative monitoring environments.

Emissions and Stack provides technologies covering gas emission analyzers, particulate and dust monitoring, and stack flow and temperature measurement.

You can explore the available solutions here: Emissions and Stack.

The takeaway is simple:

Measure what matters. Connect what helps. Understand the bigger picture.

EmissionsMonitoring #IndustrialTechnology #StackMonitoring #EnvironmentalMonitoring #IndustrialIoT

AIoT Is Moving Beyond Connected Devices: The Real Opportunity Is Intelligent Physical Operations

akihsay — Thu, 20 Aug 2026 15:35:35 +0000

For a long time, IoT was mainly about one thing: **connecting physical assets to digital systems.

Sensors collected data. Devices transmitted information. Dashboards displayed it.

Useful? Absolutely.

But there's a bigger question now:

What happens when those connected systems can also understand the data they generate?

That's where AIoT—Artificial Intelligence of Things—comes into the picture.

From "What's Happening?" to "What Does It Mean?"

IoT can help answer questions like:

Where is an asset?
What is the equipment doing?
How much inventory is available?
What conditions are being measured?
When did an event occur?

AI can add another layer of understanding.

It can help identify patterns, anomalies, relationships, and operational signals within that information.

Together, AI and IoT can turn connected physical environments into sources of actionable intelligence.

And that's particularly important for industries where the work doesn't happen entirely inside software.

The Physical World Is Complicated

Consider a manufacturing environment.

There may be machines, workers, raw materials, finished products, vehicles, tools, safety systems, and multiple software platforms operating simultaneously.

All of these create information.

The challenge isn't necessarily collecting more data.

The challenge is connecting the right information and making it useful.

That's where AIoT has an interesting role to play.

For example, better asset visibility can help organizations understand where equipment is and how it moves.

Operational data can reveal patterns in workflows.

Connected technologies can provide greater visibility into workforce and workplace conditions.

Security systems can become more connected and context aware.

The value comes from bringing these pieces together.

AIoT Needs More Than Sensors

A common misconception is that AIoT simply means putting AI on top of IoT.

Real-world deployments are rarely that simple.

Industrial environments often contain:

Legacy equipment
Multiple connectivity technologies
Different data formats
Existing enterprise systems
Physical constraints
Complex workflows
People making decisions in real time

So, building a useful AIoT solution requires more than choosing sensors and an AI model.

You have to understand the business problem, physical environment, data, users, and desired outcome together.

That's why problem-first thinking matters.

The Venture Opportunity

There's another side to this conversation that doesn't get enough attention.

Some recurring industrial problems can become the foundation for entirely new technology businesses.

Instead of asking:

"Where can we use AI?"

A more useful question may be:

"Which physical-world problem is important enough to deserve a new solution?"

That change in perspective can lead to more practical innovation.

A venture-building approach can bring together customer discovery, technology, IoT infrastructure, AI capabilities, industry knowledge, and commercialization around a specific opportunity.

This is the space where Aperture Venture Studio operates—building AIoT-native companies designed around real physical-world and industrial challenges.

Its focus includes areas such as asset tracking and visibility, operations optimization, workforce safety and monitoring, access control and security, and industrial intelligence.

You can explore the approach here: Aperture Venture Studio

The Bigger Shift

The interesting future of IoT isn't simply about having more connected devices.

It's about making those connections more meaningful.

IoT connects the physical world.
AI helps interpret the information.
AIoT brings the two together.

For industrial organizations, that can open new possibilities for visibility, decision-making, optimization, safety, and intelligent operations.

The real opportunity isn't technology for technology's sake.

It's using technology to solve problems that actually exist.

And that's where AIoT gets interesting.

AIoT #IoT #Industry40 #IndustrialTechnology #ArtificialIntelligence #DigitalTransformation