SOUTH AFRICAN IOT NETWORK MOVES TO MAKE MILLIONS OF CONNECTED DEVICES READY FOR ARTIFICIAL INTELLIGENCE

JD GLOBAL MEDIA | SOUTH AFRICA

21 SEPTEMBER 2026

JOHANNESBURG — A South African Internet of Things network is exploring a new technology standard that could allow artificial intelligence systems to understand and interact with connected physical devices without requiring a completely different software integration for every sensor, machine or piece of equipment.

Sigfox South Africa has registered to engage with Anthropic’s Model Hardware Standard, an emerging specification designed to give AI agents a structured way of discovering, understanding and interacting with physical hardware.

The development represents a potentially significant shift in the relationship between artificial intelligence and the Internet of Things, commonly known as IoT.

For years, IoT technology has focused on connecting physical objects to digital networks.

Sensors can measure temperature.

Water meters can record consumption.

Tracking devices can monitor vehicles and equipment.

Agricultural sensors can measure environmental conditions.

Industrial equipment can transmit operational information.

Security systems can send alerts.

But connecting a device to a network is only the first step.

For an AI system to make useful decisions from that device, it also needs to understand what the device is, what its measurements mean, what actions it can perform and what limitations apply.

The emerging standard being explored by Sigfox South Africa is designed to address that problem by creating a common way for physical devices to describe themselves to AI systems.

FROM CONNECTED DEVICES TO AI-READABLE DEVICES

The Internet of Things has expanded rapidly as businesses and organisations deploy sensors across physical environments.

However, IoT systems have traditionally faced a fragmentation problem.

Different manufacturers can use different data structures.

Devices can communicate through different interfaces.

Commands can be formatted differently.

Sensors can report measurements using different models.

A software developer may therefore have to create a specific integration for each type of equipment.

That approach can work when only a small number of devices are involved.

It becomes considerably more complicated when thousands or millions of sensors are deployed.

An organisation managing water meters across multiple municipalities, for example, could have equipment from different manufacturers.

A logistics company may operate tracking devices from several suppliers.

A farmer may have sensors measuring soil moisture, temperature and weather conditions.

A factory may have hundreds of machines generating different types of operational information.

Each device may already be connected.

The challenge is making that information understandable to intelligent software.

WHAT THE NEW STANDARD IS DESIGNED TO DO

Anthropic introduced the Model Hardware Standard, or MHS, as a proposed common specification for AI agents interacting with physical equipment.

The concept is relatively straightforward.

Instead of forcing an AI system to guess what a connected device does, the device can provide structured information describing its capabilities.

That information could explain what the device measures.

It could identify the units involved.

It could describe actions the device can perform.

It could specify adjustable parameters.

It could identify operating limitations.

It could also provide safety-related information.

An AI agent can then use that information as part of a broader workflow.

This could reduce the need to develop an entirely bespoke software integration for every individual piece of hardware.

The technology remains in an early research stage, but the potential applications extend across several industries.

WHY SOUTH AFRICA IS INTERESTED

Sigfox South Africa operates within the IoT environment, connecting large numbers of low-power devices that transmit relatively small quantities of information.

The global Sigfox 0G ecosystem currently connects more than 14 million devices across more than 70 countries.

For a network operating at that scale, a standard that allows AI systems to understand connected equipment could potentially create new opportunities.

The technology could help transform IoT from a system that primarily collects information into one in which AI systems can interpret information and potentially coordinate actions.

This distinction is important.

A sensor that sends information is useful.

A system that understands what the sensor is measuring can be more useful.

An intelligent system capable of comparing that information with historical data, business rules and information from other devices could potentially become more useful still.

A WATER METER COULD BECOME AN AI-READY DEVICE

Consider a connected water meter.

Today, the meter can send a numerical reading.

But the number alone may not provide enough context to an AI system.

The system needs to know that the device is a water meter.

It needs to know what the number represents.

It needs to know whether the measurement is litres, cubic metres or another unit.

It may need to know the meter's normal operating range.

It may need information about how frequently readings are expected.

It could also benefit from information about what actions the device supports.

With a structured hardware description, an AI agent could potentially receive that information automatically.

The AI could then analyse consumption patterns.

It could compare current readings with historical usage.

It could identify unusual increases.

It could potentially flag a suspected leak.

It could alert a utility company.

It could help prioritise an inspection.

The AI would not necessarily need a completely custom integration designed specifically for that particular water meter.

That is one of the potential advantages of a common hardware standard.

AGRICULTURE COULD BE ANOTHER MAJOR APPLICATION

South Africa's agricultural sector provides another potential use case.

Farmers increasingly use sensors to monitor soil conditions, weather, irrigation systems and equipment.

A soil-moisture sensor can provide information about water levels.

A temperature sensor can report environmental conditions.

A tracking device can monitor the movement of livestock or equipment.

Weather stations can collect information about rainfall, wind and temperature.

Individually, these devices produce data.

An AI system could potentially combine the information.

For example, an AI agent could examine soil moisture alongside temperature, weather forecasts and irrigation history.

It could identify unusual conditions.

It could alert a farmer to a potential irrigation problem.

It could help identify equipment that appears to be operating outside normal conditions.

The AI would still require appropriate rules, data and human oversight.

But a standardised way of describing the devices could make it easier to connect different types of equipment to the same intelligent system.

INDUSTRIAL OPERATIONS COULD ALSO CHANGE

Factories and industrial facilities contain large numbers of sensors and machines.

Industrial equipment can generate information about temperature, vibration, pressure, speed and other operating conditions.

This information can be used for maintenance and operational planning.

One of the biggest potential applications of AI-connected IoT is predictive maintenance.

Instead of waiting for a machine to fail, an AI system could examine changes in its operating behaviour.

If vibration levels gradually increase, the system could identify the pattern.

If temperature rises beyond a normal range, it could flag the equipment.

If several sensors show unusual behaviour at the same time, an AI system could investigate the relationship.

This could potentially allow companies to identify problems earlier.

However, the AI needs to understand what each sensor measures and how its data should be interpreted.

A common hardware description standard could help make those integrations easier.

LOGISTICS AND ASSET TRACKING

The logistics industry is another potential beneficiary.

Companies use connected devices to track vehicles, containers, equipment and other assets.

Tracking systems can provide location information and other operational data.

An AI system could potentially combine tracking information with schedules, historical movement patterns and operational requirements.

It could identify unusual delays.

It could detect unexpected movement.

It could flag assets that remain stationary for unusually long periods.

It could help coordinate maintenance or deliveries.

Again, the key issue is interoperability.

If every device requires a separate integration, expanding the system becomes increasingly complicated.

A common standard could potentially make it easier for AI agents to understand different connected devices.

SECURITY SYSTEMS COULD BECOME MORE INTELLIGENT

Security is another area where connected sensors can produce large amounts of information.

Motion sensors, access systems, tracking devices, environmental sensors and other equipment can generate alerts.

A traditional system might simply forward those alerts to a control centre.

An AI-enabled system could potentially analyse several signals together.

For example, it could identify an unusual combination of movement, access activity and location information.

The system could then prioritise the event for human attention.

However, security applications also highlight why safety and human oversight are important.

AI systems can make mistakes.

A false alert could cause unnecessary intervention.

A missed alert could have serious consequences.

Any AI system interacting with physical security equipment would therefore require carefully designed permissions and safeguards.

THE STANDARD IS STILL AT AN EARLY STAGE

The technology being explored by Sigfox South Africa is not yet a mature, widely deployed global standard.

The Model Hardware Standard is currently being developed through an early research preview.

Organisations participating in the programme are expected to help test the specification, evaluate safety considerations and develop practical approaches before the technology eventually becomes more broadly available.

This means businesses should not interpret the announcement as an indication that millions of existing IoT devices will immediately become AI-controlled.

The technology is still being developed.

There are technical questions to resolve.

There are security questions.

There are interoperability questions.

There are governance questions.

There are also questions about how manufacturers will adopt the standard.

EXISTING SIGFOX DEVICES WOULD NOT AUTOMATICALLY BECOME AI-ENABLED

An important distinction is that simply connecting a device to the Sigfox network does not automatically make it compatible with an AI agent.

The existing Sigfox technology provides the connectivity layer.

The proposed hardware standard would provide an additional layer that could describe the device in a structured, machine-readable way.

A compatible driver or interface would be required.

This means businesses would not necessarily need to replace existing devices.

Instead, future software layers could potentially provide AI systems with additional context about hardware already connected to the network.

That could be important for companies that have already invested heavily in IoT infrastructure.

Replacing thousands of sensors simply because a new AI system has been introduced would be expensive.

A technology layer that allows existing equipment to become more understandable to AI could therefore reduce some of that complexity.

THE DIFFERENCE BETWEEN DATA AND UNDERSTANDING

The development highlights a fundamental distinction in modern technology.

Having access to data does not necessarily mean understanding the data.

A temperature sensor might report 38 degrees.

An AI system needs to know what the number means.

Is it Celsius or Fahrenheit?

Is the sensor measuring air temperature, water temperature or machine temperature?

Is 38 degrees normal for the equipment?

Is the sensor functioning correctly?

What should happen if the temperature rises to 45 degrees?

A human engineer may already know the answers.

A software system needs those relationships represented in a way it can process.

The hardware standard is intended to provide more of that context.

This could allow AI agents to move beyond simply receiving raw information.

AI COULD COORDINATE MULTIPLE DEVICES

The longer-term possibility is that AI systems could coordinate information from multiple connected devices.

Imagine a warehouse containing temperature sensors, security sensors, tracking devices and equipment monitors.

Each system could operate independently.

An AI agent could potentially understand all of them through standardised descriptions.

It could then combine the information.

A temperature increase might be connected to a refrigeration problem.

A security alert might be linked to movement in a restricted area.

A tracking device might indicate that a vehicle has not arrived as expected.

The AI could identify relationships that would otherwise require several separate systems.

Human employees could then investigate the most important events.

This would shift IoT towards a more intelligent operational model.

SAFETY BECOMES MORE IMPORTANT WHEN AI CAN TAKE ACTION

There is an important difference between an AI system reading a sensor and an AI system controlling physical equipment.

Reading data is relatively low risk compared with taking an action.

If an AI system misunderstands a temperature reading, it could produce an incorrect report.

If the same system is authorised to shut down industrial equipment, open a gate or change a machine setting, the consequences could be much greater.

This is why safety information is an important part of the proposed hardware standard.

AI agents need to know not only what a device can do, but also what it should not do.

They may need limits on operating parameters.

They may need confirmation before performing certain actions.

They may need human approval.

They may need emergency shutdown mechanisms.

These safeguards will become increasingly important as AI moves from digital environments into the physical world.

THE RISE OF AI AGENTS

The development is closely connected to the rise of AI agents.

Traditional generative AI systems primarily respond to instructions.

AI agents are designed to perform more complex tasks, potentially involving multiple steps and interactions with external systems.

An AI agent could potentially analyse information, make a recommendation and interact with a connected device.

For this to work reliably, the agent needs a structured understanding of the systems it is interacting with.

It needs to know what actions are available.

It needs to know the required parameters.

It needs to understand restrictions.

And it needs a safe mechanism for executing commands.

A common hardware standard could provide part of that foundation.

WHAT THIS COULD MEAN FOR SOUTH AFRICAN BUSINESSES

South African businesses could potentially benefit from easier integration between existing IoT systems and AI platforms.

Companies operating large sensor networks often face high integration costs.

Every new device can require software development.

Every manufacturer can use different formats.

Every application can require different configuration.

A standardised approach could reduce some of that complexity.

Businesses could potentially deploy new devices more quickly.

AI systems could potentially discover equipment automatically.

Developers could spend less time writing custom integrations.

Operations teams could receive more contextual information.

The full benefits, however, will depend on whether manufacturers and software developers adopt the standard widely.

A POTENTIAL BOOST FOR RURAL CONNECTIVITY

The implications could be particularly relevant for rural environments.

Low-power IoT networks are useful in locations where traditional broadband infrastructure may be limited or where devices need to operate for long periods using limited power.

Agriculture, environmental monitoring and infrastructure management can involve devices spread across large geographical areas.

Connecting these devices efficiently can be challenging.

If AI systems can eventually interact with such equipment through standardised interfaces, organisations could potentially manage large distributed networks more intelligently.

This could support applications ranging from agriculture to water management and environmental monitoring.

ENVIRONMENTAL MONITORING

Environmental monitoring is another potential application.

Sensors can measure air quality, water levels, temperature, rainfall and other conditions.

An AI system could combine readings from different locations.

It could identify unusual patterns.

It could compare current measurements with historical data.

It could help identify potential environmental events that require investigation.

The system could also potentially prioritise alerts.

Instead of sending every sensor reading to a human operator, the AI could identify which patterns appear unusual and require attention.

This would not eliminate the need for human experts.

Instead, it could help experts focus on the most important information.

THE CHALLENGE OF INTEROPERABILITY

For the technology to achieve its potential, interoperability will be critical.

A standard only becomes useful if enough manufacturers and developers adopt it.

If one company uses the standard while thousands of other devices continue operating through incompatible systems, the integration problem remains.

Industry participation will therefore matter.

Hardware manufacturers need to consider the standard.

Software developers need to build support.

Network operators need to evaluate compatibility.

Businesses need to determine whether adoption makes economic sense.

Researchers need to identify security weaknesses.

The standard will need to evolve based on real-world experience.

SECURITY CANNOT BE AN AFTERTHOUGHT

Connecting physical devices to AI creates a new cybersecurity challenge.

An attacker who compromises an ordinary sensor might manipulate data.

An attacker who compromises an AI-connected device could potentially influence an automated workflow.

This makes authentication and access control extremely important.

AI agents need to know which devices they are authorised to interact with.

Devices need to know which AI systems are allowed to send commands.

Communication needs to be protected.

Actions need to be logged.

High-risk commands may require human approval.

These controls will be essential if AI-connected IoT systems move into critical infrastructure.

THE POSSIBILITY OF MACHINE-TO-MACHINE COORDINATION

One of the most significant long-term possibilities is increased machine-to-machine coordination.

A sensor could identify an abnormal condition.

An AI system could interpret the information.

The AI could communicate with another device.

That device could potentially adjust its operation.

A second sensor could then confirm whether the change produced the desired result.

Such systems could operate much faster than human teams responding manually to every event.

However, automation at this level requires extremely strong safeguards.

The system must know when it is allowed to act and when it must stop and ask a human.

SOUTH AFRICA AT THE INTERSECTION OF AI AND IOT

South Africa already has an expanding ecosystem of telecommunications, cloud computing, data centres, IoT networks and artificial intelligence development.

The integration of these technologies could create new opportunities for local businesses and technology developers.

IoT provides the connection between physical environments and digital systems.

Cloud platforms provide computing resources.

AI provides analysis and decision-making capabilities.

New hardware standards could potentially provide the common language connecting the systems.

Together, these technologies could create a more intelligent digital infrastructure.

WHAT HAPPENS NEXT

Sigfox South Africa's engagement with the Model Hardware Standard is currently exploratory.

The company is participating in discussions around the emerging specification and examining how it could apply to large-scale IoT deployments.

The next stages are likely to involve testing, technical evaluation and consideration of practical use cases.

Businesses will need to determine whether the standard can operate reliably with existing equipment.

Developers will need to test interoperability.

Security specialists will need to examine potential vulnerabilities.

Manufacturers will need to determine whether supporting the standard should become part of future product development.

A NEW WAY FOR AI TO SEE THE PHYSICAL WORLD

For much of the recent AI boom, artificial intelligence has primarily interacted with digital information.

It reads documents.

It analyses databases.

It processes images.

It generates text.

It writes software.

The next stage could involve much more direct interaction with the physical world.

AI systems could increasingly receive information from sensors and potentially interact with machines.

For that transition to happen safely and efficiently, AI needs a reliable way to understand physical equipment.

That is the problem the Model Hardware Standard is attempting to address.

THE BIGGER TECHNOLOGY SHIFT

The significance of the development goes beyond one South African IoT company or one emerging technical specification.

It points towards a future in which devices are designed not only to communicate with networks but also to explain themselves to intelligent software.

A machine could describe its capabilities.

A sensor could describe its measurements.

A meter could describe its operating limits.

A tracking device could describe its location capabilities.

An AI agent could then use that information as part of a larger workflow.

This could reduce the amount of custom software required to connect the physical and digital worlds.

FROM INTERNET OF THINGS TO INTELLIGENT THINGS

The Internet of Things originally focused on connecting physical objects.

The next phase could be about making those connected objects understandable to AI.

That distinction could eventually change how businesses design equipment, software and networks.

Instead of asking only whether a device can connect to the internet, engineers may increasingly ask whether an AI system can understand the device.

Instead of building separate software integrations for every sensor, developers could increasingly rely on common standards.

Instead of simply collecting millions of data points, businesses could use AI to interpret those data points and identify what requires attention.

The transition will not happen overnight.

The Model Hardware Standard is still at an early stage.

But the direction of development is significant.

A POTENTIAL NEW CHAPTER FOR SOUTH AFRICAN IOT

For South Africa, the opportunity lies in combining existing connectivity infrastructure with emerging AI capabilities.

The country already has IoT deployments across sectors such as agriculture, logistics, utilities, security and industrial operations.

Making those devices easier for AI systems to understand could open new possibilities for automation and intelligent decision-making.

The biggest question will be whether the technology can move from an experimental specification into a broadly adopted industry standard.

If that happens, the impact could extend far beyond individual devices.

It could influence how sensors are designed, how networks are operated and how businesses build AI applications.

For now, Sigfox South Africa's participation places the country's IoT sector inside an international conversation about how artificial intelligence will interact with physical infrastructure.

The technology is still being tested.

The standards are still developing.

But the direction is increasingly clear: AI is moving beyond screens and databases, and the physical world is becoming part of the next major frontier of artificial intelligence.

JD GLOBAL MEDIA will continue to follow South Africa’s developments in artificial intelligence, Internet of Things technology, digital infrastructure and emerging technologies.