DEV Community: Memona

Why Do AI Agents Fail Company Policies Although All Tool Calls Are Successful?

Memona — Wed, 14 Oct 2026 17:55:20 +0000

AI agents can violate company policies despite successfully performing all tool calls. Such an agent might update a database or send an email as requested but engage in some other action that should not have been performed.

The root cause of such behavior is that a successful tool call does not imply that an action conforms to the company policies.

Therefore, building a reliable AI system and avoiding similar issues requires understanding how an AI agent performs its actions.

AI Agents Can Cite Success in All Tool Calls as a Precondition for Policy Violations

Typically, an AI agent takes action by interpreting the user’s instruction, selecting an appropriate tool, and calling it. However, a successful response from the tool only verifies that the tool performed the requested action.

It does not necessarily mean that the action was legitimate according to the company’s rules.

For instance, a customer service AI agent may update a client’s details in a database. If the details were incorrect or sensitive, the AI violated the company’s regulations. However, the database now has updated information, and the tool call was successful. Thus, it becomes essential for the system to prevent such an action and ensure that no policy is breached, even if it means refusing a seemingly valid request.

Why AI Agents Violate Company Policies

The Following Are Some Common Causes of Policy Violation

The instructions do not explicitly state business rules.
The AI agent has too many permissions.
The system checks authorization after an action.
The monitoring procedure focuses too much on failures.

The Instructions Lack Specific Business Rules

An AI agent may receive generalized instructions, for instance, “to help customers.” However, the agent should also know which actions should not be performed, such as issuing a refund without managerial approval. Therefore, a reliable system should include explicitly stated and testable rules concerning the necessary aspects of the business.

The AI Agent Has Too Many Permissions

If the instructions allow the AI agent free access to a database, it can update crucial information that affects the business’s smooth functioning. However, providing excessive permissions to an autonomous AI agent can cause more harm than good, so using the principle of least privilege is essential.

When designing such a system, it is helpful to consider who, what, and where the AI agent can access, edit, or view.

The System Checks Authorization After an Action

Sometimes, a system may check whether a request was legitimate after processing it. Thus, an agent can go through with a request that should have been denied and cause some adverse effects. Sensitive actions should be checked for validity before the AI agent processes them. In general, it is advisable to authorize every request and determine the relevant tools’ responses beforehand.

The Monitoring Procedure Only Tracks Failures

Failure monitoring procedures include recording denied requests, unavailable tools, and other unacceptable causes of failure. However, an AI agent can violate company regulations and successfully complete a request that should have been denied. Companies can benefit from designing procedures that track policy violations and abnormal behaviors.

How to Stop AI Agents From Violating Company Policies?

Preventing an AI agent from violating company regulations goes beyond providing it with detailed instructions. The following recommendations can help design a reliable system:

Apply policies that an agent cannot bypass when developing a product.
Ensure the reliability of all sensitive actions performed by an AI agent.
The authorization process should approve acceptable actions.
Whenever possible, a reliable system should require manual confirmation for any sensitive action.
Design logging procedures that record all the necessary information about the action that the AI agent has performed.

A sensitive action performed by an AI agent should be denied whenever it lacks the necessary approvals, tools, or permissions. Although a monitoring procedure is essential to detect any policy violations, it is better to build the system in such a way that such failures rarely occur. This approach will reduce debugging costs and make the AI agent safer.

In addition, the procedures should test the system’s behavior when instructed to violate the company’s regulations. Ideally, an AI agent should refuse to perform a sensitive action when the regulation is not explicitly stated. Developers should also consider cases when a request’s failure mode is not covered by the existing tools and deny all unapproved actions by default.

A real world example of an AI agent that violates company regulations is a customer support agent that updates client information and processes refunds. Typically, a customer would contact the support desk, give their details, and request a refund.

Depending on the size of the refund, the system should either allow or deny the request. Even though such a task seems relatively straightforward, the agent can violate company regulations in several ways. If the instructions only state that it should be able to update customer information and process refunds, the agent may violate data protection laws and process the refund unconditionally. A company policy may stipulate that a financial officer needs to approve all refund requests, but the AI agent lacks this information and follows the instructions correctly by executing the refund.

A more reliable procedure would be to make sure that the system denies requests that exceed the specified size, denies requests lacking an approving signature, or ensures that such requests rerouted to the financial officer for further instructions. Another way to avoid such a violation is to ensure that authorization defaults to an appropriate human operator whenever a request specifies monetary transfer.

The main lesson is that an AI agent’s successfully performed action does not necessarily mean that no company policy has been violated, particularly when designing the procedures concerning the tools the AI agent will use. Therefore, it is essential to ensure that an AI agent cannot freely access and update information that should require a manager’s approval.

AI agents can violate a company’s rules despite effectively executing all tool calls.

How AI and IoT Work in Automotive Manufacturing

Memona — Wed, 14 Oct 2026 15:35:32 +0000

A motor is shaking more than before. Its temperature is rising slowly, but the production process was already running earlier. Will the engineering crew investigate earlier, or will this wait until they are due for a regular equipment check-up?

In a connected, modern auto factory, sensor insights and artificial intelligence can help.

IoT sensors gather physical device data, and AI can help explore the numbers for patterns and insights.

When paired together, the connected car manufacturing ecosystem can help producers make more educated decisions about maintenance, quality inspection, and production process changes.

1. Gathering Necessary Information With IoT

Before making any inferences about a process, an AI engine would require a substantial amount of relevant information.

Industrial IoT platforms can help monitor equipment and the production process by gathering and analyzing relevant insights. Sensors and monitoring services can capture a wide variety of relevant data for automotive manufacturers, including:

• Temperatures and vibrations

• Rotational speed and run-time of a motor

• Cycle durations of manufacturing equipment

• Operational and power status of various devices

• Position and inventory of parts

• Environmental conditions of an area in proximity of manufacturing and testing equipment

For example, a monitoring service could capture temperature and vibrational data of a motor in an automotive assembly plant. These two factors will provide the engineering crew and AI systems with some information about a motor but, typically, not enough. Engineers would have to evaluate how the motor behaves under various conditions before they can predict how the motor will respond at a given moment. For example, engineers would have to build a reference from other similar devices to establish what a “normal” set of vibrations for a specific motor should be.

2. Generating Insights With AI Processing

IoT can enable manufacturers to observe their process. But having raw data about equipment conditions is not enough. To get to meaningful insights, automotive producers would be better off pairing IoT data with artificial intelligence for analysis. There are several ways AI and machine learning can become helpful for manufacturers. A few key use cases include:

Predictive maintenance

By capturing equipment data, a learning model can find behavioral patterns that may signal deterioration in quality. This kind of information can help producers find and address any developing issues.

Automated quality inspection systems

Computer vision enables an ML model to look at images of products and find any problems or irregularities of interest for the manufacturing process. This use case can reduce human effort during the quality inspection phase of production.

Production optimization

By analyzing data from cycle time durations, production equipment status, and interruption frequencies and causes, AI can help optimize a manufacturing process. This data can provide valuable insight into what manufacturing stages and equipment should be prioritized.

Anomaly detection in IoT data

Detecting unusual patterns of behavior can also be achieved by creating a reference of what a “normal” observation should be and flagging any outliers. These irregular patterns can include any information that deviates from established behavioral reference.

The choice of ML technique that should be leveraged for a specific use case will depend on the particular needs, volumes of available information, and acceptable prediction risks.

3. An Illustrative Predictive Maintenance Workflow

Let’s imagine a motor that runs continuously in an automotive manufacturing plant. One of the initial solutions that come to mind in this situation is a predictive maintenance approach.

A monitoring workflow that incorporates AI inference may have the following steps:

Step 1: Sensors

collect vibrational and temperature data at regular intervals

Step 2: Data pipelines

validate this information, verifying that all equipment has proper timestamps and that no values are empty or malformed.

Step 3: Engineers

establish the behavior of this motor at particular conditions, such as higher or lower loads.

**Step 4: **Either a model or an anomaly detection solution will compare new data with the information from step three or the established ranges for this motor.

Based on this data, an engineer would choose either a statistical or a machine learning model for this AI application or another approach (e.g., time series), depending on the volume of available data, the frequency and predictability of anomalous patterns, and other factors.

**Step 5: **Once the engine detects an outlier, it could issue an alert that will prompt human judgment

about the technical condition of the motor. This alert would usually include contextual information about the event, such as timestamps, equipment identifiers, and, possibly, other relevant variables.

In step 6, once there is a human evaluation of the equipment’s current state, engineers will make a conclusion and decide whether it is necessary to halt the production process and schedule maintenance for this equipment.

As you can see, the AI process is not a deterministic one an alert will usually provide valuable information, but not one of a guaranteed equipment or process failure. An anomaly detection model flags values that fall outside of expected ranges.

A predictive maintenance model that flags the likelihood that an equipment may fail at a later point in time involves a few additional steps because it needs to analyze what anomalous patterns may represent equipment wear that could lead to an imminent failure.

However, if the alert system is configured incorrectly, the model could raise false alarms frequently. This makes evaluation of the model’s performance against false positives an equally valuable metric during the solution evaluation phase.

4. Assessing Real-World Performance of an AI Solution

The actual evaluation of any applied model in the physical world is also far from complete. Engineers and analysts will want to measure the following performance indicators specific to a predictive maintenance use case:

1. False alarm rate: How often the maintenance alert system falsely marks equipment as faulty.

2. Missed fault rate: How often such an AI system fails to detect an equipment or process issue.

3. Detection lead time: A crucial time indicator that suggests how much time there will most be be available ahead of a failure for maintenance crews to investigate.

4. Alert response: How soon can the engineering team react upon receiving a model alert.

5. Process interruptions: Will this maintenance platform prevent unplanned system downtimes?

6. Sufficiency of data: Is the volume of incoming data adequate for this model?

Additionally, it will be important to balance how impactful false positives and false negatives will be for the production process. Failing to detect serious equipment issues will likely have negative consequences similar to unplanned downtimes during production, but these consequences will also vary from one setup to another.

Although a model will initially show promising results during training, it will also be crucial to continually monitor the platform and reevaluate its performance after changes, such as new equipment being added or different workload patterns.

5. Expanding Manufacturing Applications to Include Car Safety Features

The same kinds of broad principles that connect IoT devices and AI models apply to modern car safety technology. Automotive manufacturers can install onboard cameras, radar systems, LiDAR, and other sensors to assess their surrounding environment.

AI powered perception could help evaluate the camera feed from these sensors, giving insight into the car’s surroundings and enabling features such as lane-keeping and automatic brake mechanisms. Of course, actual vehicle safety extends beyond model inference to the design and installation of appropriate onboard sensors, systems validation, cyber physical security, and a variety of other factors. Actual automotive safety evaluations will usually require testing the product in relevant, varied, and realistic conditions.

6. Merging AI Components With Existing Factory Infrastructure

Modern auto factories are known to operate multiple systems that control, monitor, and optimize various processes during production. Different solutions serve various purposes for manufacturers:

• Manufacturing execution systems manage and control the production process.

• Enterprise resource planning tools organize and strategize business operations.

• Quality management systems collect and organize quality data and information about manufacturing anomalies.

• Equipment monitoring platforms gather and store data about individual equipment.

When these tools are standalone solutions, it may be difficult for engineers to investigate any production issues. For example, combining quality inspection data about an issue with the relevant machine data and production timeline is essential when looking for the root cause of any manufacturing discrepancy. To optimize the manufacturing process, engineers may want to analyze equipment, part, production scheduling, and maintenance data.

In an AI connected system, these pieces of information will need to be integrated into one unified solution, and engineers may need to consider the following:

• Communication standards: Most factory devices will communicate using standard sets of protocols, but manufacturers may need to build or choose additional communication infrastructure when these standards cannot be met.

• Application programming interfaces (APIs): Where these are available and compatible between systems.

• System availability: Will the necessary systems be up and running at all times to supply the necessary data?

**• Edge computing: **Edge services usually deliver low-latency performance and processing to individual manufacturing devices when necessary. On the other hand, a central cloud platform can be helpful to unite disparate systems, ensure sufficient data storage capacity, and provide the overall flexibility necessary for an AI solution. The particular choice of a design philosophy will depend on specific infrastructure requirements.

7. Additional Manufacturing Challenges Beyond the Core AI Engine

Even the most carefully designed and well implemented AI system will often under-perform if the supporting platform has issues.

Poor data collection and storage: When essential information is missing or inconsistent.

Legacy equipment: Older devices or machinery at a manufacturing site that will require additional infrastructure to support IoT solutions.

Variations in workload: An AI model can become less reliable and accurate when workload requirements change significantly.

**Cybersecurity: **Connecting a factory monitoring platform requires cybersecurity and network segmentation infrastructure to protect devices, data transmission, and processing services from threats.

Human validation: An alert about maintenance may need to be reviewed and corroborated by a human worker or team, depending on the risk level and technical requirements of a given scenario.

System design: Network downtime or unavailable hardware and software can severely impede the ability of an AI-driven factory monitoring solution to deliver on its promised performance. These weaknesses in a broader system should be identified and addressed prior to launch rather than at a later date.

Conclusion

AI and IoT can help manufacturing producers turn equipment and production process data into more valuable and actionable insights. IoT will enable visibility, and AI will help find meaningful patterns that can aid the maintenance process, quality inspection, and production cycle optimization. The best approaches will begin with a well defined objective, collect relevant data, and consider how AI alerts will be routed towards maintenance, inspection, and other operational teams. For readers interested in exploring how connected technology is applied in automotive manufacturing,(https://oemnexai.com/) is a good resource. A reliable AI solution in manufacturing does not always begin with a deep learning model, but rather with a focus on helping the appropriate people get reliable, timely insights to help them take timely and accurate action.

How AIoT Is Enhancing Commercial Construction Operations

Memona — Tue, 13 Oct 2026 16:38:39 +0000

Picture teams struggling to find the needed equipment, and managers attempting to rely on out of date information to manage a project.

Disconnected physical processes and digital information management may create conditions for these situations to occur.

Artificial Intelligence of Things (AIoT) addresses the problem by connecting Internet of Things (IoT) devices with AI to acquire and analyze information, which can improve operational decision making in the commercial construction industry.

Let's examine how this concept can be applied and what considerations should be made by developers and construction technology specialists when implementing it.

What Is AIoT in Construction?

IoT utilizes connected devices to collect information through sensors, tags, and other means.

AI analyzes this data to identify patterns and supports situational awareness.

In commercial construction applications, connected devices and technologies can be used to identify available equipment, track material locations and inventory, observe environmental conditions, or monitor machinery performance.

Depending on specific circumstances, a construction site may utilize a combination of the following:

RFID: Location tracking of tagged items

GPS: Long range tracking of tagged items

Bluetooth: Low Range tracking of tagged items

**Ultra-Wideband: **Precision tracking of tagged items

Environmental sensors: Temperature, humidity, and other conditions

Machine Telemetry: Operational information from assets

There is value in having an understanding of tagged asset locations, material inventory levels, and equipment operating conditions.

The specific technologies selected will depend on size and layout of an area, necessary precision of location information, available infrastructure, and budget.

The overall goal is to identify information helpful to making decisions and have an appropriate method to access it.

Improving Equipment and Material Visibility

Equipment and materials may require multiple transfers between storage, work site, and other areas of a construction job site.

If teams lack awareness of the location of assets, equipment operators may attempt to repurchase or rent an item that is already available at another worksite.

Material inventory issues can lead to ordering errors or confusion regarding the expected and received amounts.

Technology supported location tagging and tracking helps teams identify and address these situations.

Using a connected tracking system, a manager may verify if another worksite has the needed equipment before considering a rental option.

Similarly, tagged supplies may help clarify inventory problems.

Operators should be aware of the limitations of a tracking method before implementation.

For example, RFID requires a reader to be in proximity to a tag before it can be read.

GPS functions may be inconsistent in some areas.

Indoor positioning accuracy depends on specific infrastructure.

Predictive Maintenance Using AI

Construction equipment generates operational data that can be analyzed to support maintenance decisions.

Based on the type of equipment and sensors that have been installed, this information includes:

Equipment usage: Hours, patterns, etc.

Temperature: Operational and environmental

Vibration: Abnormal patterns, etc.

Status: Operational condition and error information

Using this data, AI can detect patterns that suggest a developing equipment issue.

As an example, an AI-powered maintenance support system might identify abnormal vibration patterns and alert personnel that an item may require inspection.

It is important to note that abnormal vibrations can be caused by a variety of factors, including poor maintenance, changes in environmental conditions, or sensor errors.

Before implementing predictive maintenance procedures, it is critical that AI has been trained on operational data and that maintenance personnel have been trained and are prepared to respond appropriately to alerts.

Job Site and Operational Safety with AI

A worksite involves the movement of equipment, materials, and personnel in a changing environment.

Connected sensors can help gather information on relevant conditions, and an AI system can be used to detect abnormal patterns.

As an example, an AI powered safety application may receive environmental data from a sensor, determine that the temperature is above a project's acceptable limit, and notify the appropriate team.

An appropriate response would involve identifying what action or inspection needs to be performed.

A properly functioning AIoT safety application would define these parameters:

What conditions will raise an alert
How to verify that an alert is valid
Who needs to be notified
What inspection or action should be taken
How to document the results of an inspection

With this information, a team can take appropriate action.

An AIoT application cannot assure job-site safety, but it can augment existing safety procedures.

Connecting Construction Data Systems

A complication of implementing AIoT is that location tags, inventory systems, equipment telemetry, and other data sources may exist in independent systems.

To unify this information, and bring it to an operator in a coherent manner, an AIoT system can be implemented using the following architecture:

Devices
Connectivity
Edge Processing
Data Platform
Analytics
Applications

Within this architecture, devices represent sensors, tags, equipment, or other sources of information that will be utilized by an application.

Connectivity refers to the system that allows devices to communicate with the remainder of the architecture.

Edge processing involves servers or applications that perform some calculations on device to reduce the amount of data that needs to be transferred, or to enable operations when a connection is unavailable.

A data platform is a database where data from devices and other sources is stored for use by applications.

Analytics software defines rules and algorithms to identify patterns in data.

Applications receive data from the data platform and present it to users, generate recommendations, or perform other functions.

Not all commercial construction projects will require all components.

A simple asset tracking application, for example, will likely involve a relatively straightforward architecture, compared to a project that will simultaneously track, analyze, and report on multiple data sources.

The choice of an appropriate architecture will be based on the needs of the application.

Sensors, Tags, and How to Use Their Information

It is one matter to collect information, and another to appropriately apply it.

As an example, consider an application that aims to improve equipment availability.

1. Collect: A tracking tag provides an update regarding the location and time of an item

2. Validate: The application confirms that it has received the update, validates that the update is recent, and may flag it as an issue if the tag indicates abnormal movement

3. Information: Based on this information, the application may calculate the likelihood that the needed item will be available

4. Recommendation: If the item is unavailable, the application can suggest an alternative based on current inventory and availability

5. Implementation: An operator reviews this recommendation and updates the inventory tracking information

As this example illustrates, an application utilizing AI should receive reliable information and generate reliable recommendations. Using AI as an intermediary between disparate systems and applications can help bring clarity and understanding to operators.

AI agents that assist with operational tasks need to be given responsibility that they can handle, and it is important that operators continue to review generated recommendations before taking action, especially in regards to physical equipment.

(https://commconai.com/)is an appropriate choice for organizations seeking an experienced team to assist with AIoT implementation in the commercial construction industry.

Challenges Developers Should Consider

Before pursuing an AIoT implementation initiative, it is important to understand potential obstacles.

Reliable Data Collection: Inconsistent, repeating, or irrelevant data may hinder an implementation.

Proper validation procedures should be established for each data source.

Connectivity: Large scale job sites may involve areas where a stable connection is not available.

Systems should be designed to buffer data until a connection can be re-established.

Integration: Older equipment or applications may not be compatible with modern systems.

Developers should identify integration points and work with available systems to determine compatibility and requirements before implementation.

Security: Connected devices and applications require secure protocols, including authorization and authentication procedures to prevent data breaches or sabotage.

*Alert Reliability: Excessive false alerts may diminish the importance of actual issues.
*
Thresholds and parameters should be established using appropriate rules, conditions, and AI models that have been tested using realistic data.

Information Accessibility: Data is only valuable if it can be interpreted and utilized.

Developers and operators should determine what information is needed and how it can be incorporated into existing procedures.

As a practical starting point, choose an obvious problem and verify that an identified solution actually helps.

A Practical Checklist Before Implementation

Before continuing an AIoT initiative, ask yourself the following questions:

What problem are you trying to solve? Consider a specific issue
What data do you need to solve it? Can your available sensors provide this information reliably?
What happens if a connection is lost? Can your operators continue to operate and verify the accuracy of alerts without this data?
What alerts and suggestions will you need to verify? Who has the authority and capability to perform these verifications?
What metrics will you use to evaluate the success of your implementation?

Final Thoughts

AIoT can help teams of commercial construction professionals identify and organize available equipment, track materials, and monitor environmental and machinery conditions.

By selecting appropriate sensors, tagging systems, and analyzing the resulting data, construction professionals can improve decision making and help avoid operational delays.

This process begins with understanding how to implement connected technology in a systematic, consistent manner.

For developers and construction technology professionals, the most appropriate point to begin is by selecting an issue or process that you would like to improve.

From here, you may design an appropriate architecture, incorporate existing systems or technologies, and generate data sources helpful to your operations.

Most importantly, people must be involved in information processing tasks that require context or discretion.

While technology can help monitor a job site, it cannot replace the value of individuals who make decisions based on knowledge, experience, and judgment.

Companies Building the Future with AI and IoT

Memona — Tue, 13 Oct 2026 15:16:23 +0000

How are companies using connected devices and real-world data to engineer intelligent systems?

Think about a manufacturing plant with hundreds of pieces of machinery, each producing a steady stream of temperature and performance data. Getting that information is only part of the equation, the greater challenge lies in understanding it and knowing how to respond.

This is where the Internet of Things (IoT) and Artificial Intelligence (AI) come into the picture. These two technologies work in concert IoT gathers and shares data; AI analyzes and understands it. Artificial Intelligence of Things (AIoT) is the emerging space where AI and IoT converge.

What are companies building with AI and IoT? How do these technologies complement one another? What are typical AIoT implementations?

These are the questions we will address by building the foundation of understanding.

1. Companies Shaping the Space

There are a variety of established technology companies in the AI, IoT space, many of which are building computing infrastructure, robotics, or intelligent systems.

NVIDIA: Compute and Graphics Architecture

NVIDIA makes GPUs, AI computational software, and tools for robotics and computer vision.

As an application developer, one would consider NVIDIA-based AIoT applications for robotics and computer vision use cases around warehouses, fulfillment centers, or even autonomous vehicles.

These applications range from performing inferencing based on imagery and sensor data to using vision to find a defect on a manufacturing line what those applications can do are limited only by the training data, context, and environment in which this AIoT application would be deployed.

Microsoft: Software, Enterprise Intelligence

Microsoft builds some of the world’s largest and most influential cloud and enterprise systems in the world, and they power many of the tools we use on a daily basis.

IoT architectures often feature a cloud component that allows the connected products and sensors to get updates, report statistics, and connect business intelligence tools together. Microsoft components could enable that IoT architecture one would consider them when designing a system that could analyze data arriving from thousands of connected products and bring it together into a meaningful whole.

The choice of architecture would be informed by the data, size, scalability expectations of the system, and business needs.

Siemens: Industrial Automation

Siemens makes industrial automation products, tools, and systems. Industrial applications usually involve the convergence of IT and OT — operational technology, or connected devices and machinery.

By analyzing this data, teams could monitor performance, identify issues and bottlenecks, or improve their understanding of what is happening on the factory floor. That is important to developers because an industrial application typically runs in a more complex environment which requires deeper domain expertise about how systems, processes, and devices function.

IBM: Enterprise Systems and Tools

IBM’s offerings span across enterprise platforms, big data, automation, and industrial applications.

By combining those, IBM enables organizations to analyze their data in novel ways, find anomalies and patterns that suggest something unusual is happening in their business, but the results of that investigation need to be interpretable by people within the system.

Google: AI and Language Models

Google’s end to end systems power much of the modern world, from cloud infrastructure hosting to AI and machine learning architectures.

These would likely serve as the backbone of an AIoT application that requires data science, advanced analytics, or perhaps another external application that would connect to this system. One would choose this architecture based on the particular AIoT use case and data requirements.

The choice of architecture would be informed by the use case, current infrastructure, cost expectations, security needs, team expertise, and not brand preference.

This is a highly simplified architecture, but the general idea follows one designs the system around the data and what they want to achieve.

2. AI and IoT in Concert

A typical AIoT architecture has elements of physical machinery, and actions, and the software and AI that analyze the data and suggest or execute actions in the real world.

Physical World –> Sensors –> Connectivity –> Data processing –> AI / ML –> Decision –> Action

Within this architecture one can see six stages:

Sensors –> these measure physical quantities like temperature or vibration

Connectivity –> some form of transmission, either physical or wireless, that moves this data to a device or cloud

Data processing –> where this data is validated, filtered, organized, and prepared for analysis

AI and ML –> models analyze and detect patterns in data, identifying known events or forecasting likelihood of an undetermined event

Decisioning –> decision logic analyzes model output and operational constraints and identifies recommended or required actions

Action –> recommendation, alert, or an automated action executed by this system

Not every AIoT system includes AI. Often, decision trees and basic logic can accomplish what an AIoT application would attempt but a simple threshold value could suffice when a particular physical condition is met.

One builds an AIoT application the same way as any other: identifies a situation where current tools, actions, and technologies are insufficient an unmet need and then designs a system that delivers value.

The application might use AI or it might use simple logic what is more appropriate to this setting?

3. A Working Example

Let’s consider a working example: predictive maintenance within a manufacturing plant.

There are a number of large motors that turn during the manufacturing process, and attached to them are vibration and temperature sensors. Every so often, these sensors transfer their information to an edge device or cloud, where a machine learning model analyzes if these measurements are normal or not then it notifies the maintenance team if there is a concern.

Such applications need to take context into account, be it physical location or operational constraints.

Other concerns also become relevant data integrity and quality, latency or responsiveness if the connection is slow, false positives reducing credibility of these alerts,

security of this connection and the data itself, and possibly human review before an issue is escalated. All these are practical concerns when implementing the application. It is not enough for an AIoT application for it to simply work it should function reliably and consistently under a variety of environmental conditions.

There is also the question of edge AI versus cloud AI one can implement the system so that sensor data is processed near the edge, where it receives near real time updates based on physical conditions, limited bandwidth, and other operational constraints. Alternatively, one could implement a system that uses the cloud, where the processing power and memory is less constrained, though there might be latency. Many applications use a combination of both, with edge processing being used for immediate concerns and the cloud providing longer-term insights, analysis, and reporting.

The choice between cloud AI, edge AI, or a combination would depend on the needs of the application, its response and latency requirements, budget, privacy, and reliability expectations.

4. Looking Ahead at AI and IoT

Artificial Intelligence in Industrial Applications: Intelligent, Connected, and Adaptive AI and IoT are making that possible across a new range of applications and are enabling the rise of Physical AI: systems that are aware of and adaptive to the physical world. This spans sensing and perception of equipment and surroundings to advanced logistics or process intelligence. These are the technologies that can make systems around asset availability, safety protocols, and even the very nature of industrial processes evolve. This kind of transformation is what companies such as Aperture Venture Studio are pursuing as an area of strategic interest by building AIoT companies in physical industrial markets.

That being said, there is a long road ahead before such an application sees production. There is a great deal more than choosing a technology stack, but it is always best to start at the beginning: by identifying the problem the technology will solve.

Conclusion

Companies such as NVIDIA, Microsoft, Siemens, IBM, and Google, each offer distinct capabilities that power intelligent applications, enabling modern developers and organizations to accomplish a great variety of objectives. There is no single application or purpose that these technologies serve but rather, they complement a growing array of use cases and domains. Their selection becomes informed by what the application requires to accomplish its stated purpose. These are the concepts we explored in this series.

When designing AIoT system, what considerations would you give to the choice between edge AI versus cloud AI? Why?

EV Repairability Considerations

Memona — Mon, 12 Oct 2026 17:20:08 +0000

Battery powered vehicles can evolve to have complex requirements related to packaging, software management, and advanced automated features. This can impact the lifecycle and serviceability attributes of an electric vehicle (EV).

An element that is often given less consideration than battery performance or range capabilities is the serviceability of the vehicle, particularly several years after purchase and initial use.

Key aspects of EV engineering can impact component accessibility, serviceability, and the repair process for battery technology, and electronics-based control functions.

Factors that influence maintainability and repair include:

• Battery packaging considerations

• Software and electronics diagnostics and control

• Availability of replaceable and serviceable components

There are also specific considerations related to lifecycle availability of technical documentation and repair parts for EV platforms in general and certain classes of battery designs.

Battery Packaging Considerations

An essential aspect of vehicle lifecycle and repair considerations is the ability to access individual battery modules or cells and related components.

Some degree of integration of battery technology with other vehicle subsystems is generally unavoidable, but the extent and the resulting implications for repair processes will vary depending on the architecture.

The ability to replace or access components can depend on whether there is access to the battery compartment or space, and the degree of integration with other systems such as power electronics.

For example, if a fault develops in a battery system, there may be a need to identify if the root cause is in the battery modules, sensors, battery management system, or thermal management components and access this set of systems. There may also be the need to access individual sets of cells or modules.

Impacts on repair processes include: restricted access to specific elements of the battery system, high voltage safety and diagnostic considerations, proprietary diagnostics and technical support requirements, and the possibility of having to replace larger sections of the battery system.

This can also impose additional lifecycle constraints on the ability to make repairs if specific technical documentation, diagnostic tools, or technical training are not available. The implications of these architectural decisions can vary significantly depending on the nature of the fault and the extent to which the system can be serviced.

Electronics Diagnostic Considerations

As part of modern vehicle architectures, there is increasing use of embedded and connected software technologies. There are implications for lifecycle and serviceability, particularly around diagnosing the root cause of a fault and ensuring required technical skills and tools remain available.

A control module fault, for example, may not always require only a simple module replacement but may also involve the need to perform software programming and system diagnostics. Similar considerations apply to sensor related faults and interactions between sensor systems, power modules, and vehicle-level software.

Some of the key serviceability considerations for electronics-related systems include: diagnostic access and availability of relevant software tools, software support and programming requirements, component replacement considerations, calibration and programming requirements for sensors and other components, and training requirements.

There are additional considerations around lifecycle implications of connected and industrial AI enabled technologies.

These considerations also cut across OEM manufacturing and production operations, particularly related to using operational data to optimize manufacturing processes. For more information on AI and industrial IoT technologies in manufacturing, please visit OEM Nex AI.

Finally, related to the availability of spares and serviceability documentation is the question of replacement part availability.

Particularly with respect to control modules, battery electronics, and other system-specific electronics, there may be considerations around the availability of replacement components for an extended period.

For example, it can be a critical concern for fleet managers to ensure availability of spare parts, diagnostics, and repair capacity at the local level. This is further impacted by overall vehicle availability and utilization considerations. For a private owner, the primary concerns may be related to warranty coverage and availability of local repair service support.

Some of these issues can be addressed through appropriate lifecycle planning and logistics support at the manufacturer level. The engineering evaluation during the product development cycle can also ensure that there is appropriate diagnostic and repair support at the component level.

Other considerations around diagnostic and repair capabilities apply to training needs for appropriate personnel. As part of the lifecycle management and repair strategy, the manufacturer should evaluate training and qualification requirements for working with high-voltage battery systems.

It is possible in some cases to provide modular repair approaches or component-level replaceable options to address some of these requirements. It is not always necessary to engineer a repair process that will optimize every aspect of lifecycle cost.

Rather during the design review process, engineering staff should evaluate a number of lifecycle, serviceability, and maintenance considerations in the context of overall performance and packaging constraints and arrive at an appropriate design.

Repair Consideration Checklist for Manufacturers and Managers

The following list provides a brief overview of key considerations for evaluating long-term serviceability attributes:

Which battery components are replaceable or repairable at the component level?
What are the tools and equipment needs for diagnostics?
Are electronics replaceable and programmable at the component level?
Will essential replacement parts and technical documentation continue to be available?
Will qualified local repair facilities be available?
Which repairs typically require the replacement of an entire assembly?

7.What battery and electronics repairs are covered under warranty?

Depending on the manufacturer, model, and the region of operation, there may be a number of additional questions to consider.

With these considerations in place, it will also be possible for a fleet or private owner to determine if the purchase of a specific make and model will be supportive of long-term vehicle availability and minimize unplanned repair costs and downtime.

Summary and Further Consideration

EV lifecycle and repair considerations can add an additional dimension to design reviews for extended-range vehicles. Battery technology, packaging considerations, and software-related issues can impact the ability to access and repair systems over the course of the vehicle’s useful life. Some of these considerations are more relevant to certain classes of faults and certain design architectures.

These repair considerations should not be a reason to avoid purchasing an extended-range or battery-powered vehicle but rather a means to evaluate the need for appropriate lifecycle planning during the design process, and to ensure that lifecycle and repair support is available at the component level across a wide range of fault scenarios.

For fleet managers and manufacturers, the evaluation should be extended to overall lifecycle cost and availability considerations. These factors can also impact vehicle acquisition decisions for a private owner, depending on the overall projected utilization and local availability of repair resources.

Discussion: Should repairability considerations constitute a formal criterion in the design of an electric vehicle, alongside other standard requirements and considerations? What engineering compromise trade-offs would you consider most critical in this context?

How AIoT Can Transform Industrial Operations Essay

Memona — Mon, 12 Oct 2026 16:14:03 +0000

Picture a factory with a malfunctioning machine. The machine generates different temperatures. Production is slowing, and technicians have to determine what is happening: Is there a need for repair?

Without the right systems in place, the process involves going through numerous reports before deciding. With the right mix of Internet of Things, or IoT, and artificial intelligence, or AI, we can observe processes, detect changes, and make recommendations.

This is where the concept of artificial intelligence of things, or AIoT, comes into play.

Instead of looking at how IoT and AI can exist separately, let’s connect them to observe and identify factors affecting our operations.

What Is AIoT?

IoT technology enables us to connect objects, such as machinery, sensors, vehicles, and robots, into an information generating machine.

Using them, we can collect data about location, condition, movement, or activity of physical assets.

AI algorithms, in turn, can analyze this information to detect patterns, identify outliers, draw conclusions, and make recommendations.

Combined, they create a powerful AIoT concept, which allows us to see beyond the data and understand the situation.

Using AIoT, we can, for example, collect, organize, and evaluate data to transform a current process into a sequence of informed decisions. We might proceed as follows:

IoT sensors generate information about temperature, pressure, and vibration.
Connectivity layer, perhaps utilizing edge computing, allows us to receive these readings
An AIoT application interprets data and detects irregularities.
As a next step, it provides actionable recommendations.
A human supervisor checks what has occurred and decides what to do.

The point is not that we use AI to analyze every connected device. Instead, we want to tackle a single issue that can benefit from both data and an informed recommendation.

Let’s review how practical AIoT can potentially be by looking at its architecture. We’ll examine how to best implement such a system.

Physical World To AIoT Architecture, How It Works

A functional AIoT application usually covers several steps, beginning with our physical assets

As we connect our physical assets with sensors and identification tools, the system is ready for processing.

1. Physical Assets And Data Collection

Every system needs physical assets to observe. These may include vehicles, robots, machinery, tools, and materials.

Sensors collect quantitative data, while identifiers help track inventory, materials, or goods. They can include thermal sensing, vibration analysis, RFID tags, and barcode labels.

Camera imagery or video surveillance can add more insight, depending on our purposes. It is crucial to recognize that the quality of information affects our results.

Incorrect sensor data, missing information, and poor recording conditions degrade our outputs. We must ensure consistent data quality, sensor accuracy, and proper recording settings across locations and equipment.

2. Connectivity And Edge Processing

The next step focuses on how we collect and process this information. Our data transmission and processing capabilities affect how we design the rest of the application.

Industrial applications often use a mix of connectivity solutions, including cabling, Wi-Fi, cellular, gateway connections, and more.

Likewise, cloud and edge processing methods will differ. Some operations are better suited for a system running locally in proximity to the data source, while others benefit from centralization.

A reliable connection is required for transmitting readings and recommendations over these systems. Finally, decisions can be made and actions executed.

3. AI And Machine Learning

Artificial intelligence can help us recognize patterns, detect deviations, and find valuable information. Using the information, we can, for example, create an electronic version of a production flow diagram.

It becomes easier to see which processes generate the most bottlenecks, how to improve robotic movements, or how to adjust supply chains. We can detect irregularities and recommend actions:

Detecting when equipment needs maintenance
Assess if products meet the required standard
Forecast how much raw material we will need
Determine the cause-effect relationship in observed production

We must evaluate our application, data, and potential results realistically. No matter how much training our AI model has, we should not consider its output as a fact.

4. The Final Process, Generating Insights

The application needs to transform ambiguous data into specific recommendations. A human operator receives it and takes action, perhaps observing the machine and adjusting the settings.

We might automate decisions and execution of certain commands, depending on our needs. It is critical to consider what the appropriate permissions, restrictions, tests, and operators are before proceeding with physical AI.

Now, let’s look at how we could use AIoT in practice.

Predictive Maintenance In A Factory Using AIoT Use Case

An application for predictive maintenance in a factory could look like this:

A machine runs for a few hours a day. We want to ensure there are no failures and disruptions to the production line.

Following the AIoT architecture above, we could, for example:

Step 1: collect data.

Sensors observe machine vibrations, temperatures, and activity. The application collects additional information about the machine’s condition.

We could include previous maintenance records for better context.

*Step 2: we establish a baseline by comparing our averages to the current values.
*
Engineers could review what values are acceptable during specific production processes.

Step 3: An AIoT algorithm detects outliers after establishing a baseline.

A maintenance worker reviews the data and determines if there is a need for maintenance.

Step 4: Our final step generates recommendations for the worker. They could include recent readings and observations.

Based on their evaluation, the worker can perform a full maintenance procedure.

It is the application’s ability to collect data and recommend the most probable causes of deterioration that makes the system valuable.

A single temperature reading would not be enough to suggest that the machine needs maintenance.

How Can Edge And Cloud Computing Combine Into AIoT?

The application has several capabilities. It can process and analyze the data as described above.

Cloud computing can play a role at this stage by providing resources for storing information, running analytics and recommendation algorithms, and analyzing patterns with machine learning models.

This is where cloud and edge computing come together.

Edge computing in AIoT can help if:

The application requires fast processing speeds (low latency).
The operation environment is highly distributed.
The process is critical and requires continuous performance.
Applications need to operate independently of the cloud.

Both edge and cloud computing have their place in AIoT. Usually, there is no reason to choose one exclusively over the other. Rather, we design the application so that its overall architecture best suits our needs.

It is one thing to build an impressive application. But how can we ensure that our approach will work when we implement it?

What Are The Challenges Of Implementing AIoT Systems?

Creating an application involves more than simply connecting a few sensors. Let’s examine practical challenges of implementing AIoT applications, such as the following:

1. Data Quality

Sensors have limitations. Their data may be incomplete, erroneous, misleading, or irrelevant to our needs.

Before continuing, we must assess quality ratings, potential errors, and variances.

2. System Integration

Modern, sophisticated manufacturing applications require multiple interconnected software systems. These can range from production control and robotics to inventory and enterprise resource planning tools.

Integration requires careful coordination and consideration of data, processes, and authorization.

3. Security and Access

An IoT-enabled system adds additional software and hardware to the current infrastructure. It introduces additional opportunities for infiltration and abuse.

Correct device permissions, network segmentation, data encryption, and protection are essential.

4. Model Performance

If the model does not deliver consistently, it can slow our processes. A functioning AI model requires training, evaluation, and ongoing maintenance.

5. Interpretation and Execution

A working system is great. But unless our application translates insights into clear recommendations and execution instructions, the model will not improve our processes.

A dashboard containing dozens of alerts means little unless someone knows what to do with this information.

A system application can notify, recommend, and execute commands. However, it does not know what to do in a specific case without human review.

Now that we can envision the necessary architecture, let’s turn our attention to practical application.

How Should We Approach Development Of AIoT Architectures?

Organizations considering investing in this space should start the process by examining a single system.

For example, we might want to:

Increase efficiency by reducing downtime during production.
Improve inventory management by reducing losses and inaccuracies.

We should then determine which physical assets we need to monitor and identify.

This can involve sensors, GPS trackers, identifiers, and even video surveillance. Now is the time to ensure we receive high-quality data.

We must perform a verification procedure and evaluate potential weaknesses before progressing. With these components, we can then design a narrow, focused application. We are not trying to build a fully automated, end-to-end application at this stage.

We then perform a test run and compare our results to the baseline: our current system or a similar process executed manually.

We can determine what actions to take at this stage. Perhaps AIoT helps us schedule and prioritize maintenance tasks.

We must establish a procedure, designate personnel, and provide permissions and requirements for execution. In the long term, we want to ensure that our application continues to generate value and that adjustments are made as needed.

A working AIoT solution can transform an end into an informed start.

What Is The Difference Between AIoT And Physical AI?

While AIoT deals primarily with data analysis and information processing, physical AI is about linking AI technologies with physical objects.

This connection enables systems to perform a wider range of tasks, from controlling robotics to regulating heating and air conditioning systems.

In essence, AIoT describes how we process data about our physical world. In contrast, Physical AI represents how we can bring AI into the physical world by applying it to objects and processes.

We can see the difference in information processing and decision-making: AIoT systems are designed to generate useful information by detecting what has happened.

Physical AI systems make decisions based on information about the physical world.

Physical AI systems, unlike AIoT systems, extend into the physical world and have an effect on it. Decisions and instructions require consideration of authorization, personnel, and processes.

For companies considering how such capabilities can apply to their operations, Aperture Venture Studio works to identify companies at the intersection of AI and IoT, with a specific emphasis on physical objects and industrial operations.

Final Notes About AIoT And Applications

AIoT is best applied when we combine high-quality information about the physical world with specific recommendations that lead to decision-making.

We can leverage sensors for data gathering, connect them, and apply artificial intelligence.

Finally, application software can transform this information into recommendations. By doing so for a particular system, we can fine-tune processes, make decisions, and continuously improve.

The aim is to create a system that best meets our needs with regard to the process, existing technologies, and the operating environment.

Discussion: If we were to develop such a solution within a factory or warehouse, what would we begin with – predictive maintenance, asset tracking, quality control, or inventory?

Do you think AIoT is valuable, and can it be implemented successfully? Does a factory need to connect everything available, or are there systems that should be left alone?

How Technology Improves Inventory Control

Memona — Sat, 10 Oct 2026 17:05:42 +0000

Consider that your inventory system shows 120 of some product on-hand, but a worker can only identify 95. Your customers may have to wait later for an order to be delivered, buying decisions may be wrong, and time is spent digging why there aren't enough of a particular product.

You understand that you may have too much inventory or not enough in your inventory control to reflect your actual level of stock.

Inventory control allows a company to track what product is going in and out and is a critical aspect of many businesses. Utilizing technology can assist in the operations performed, but the actual contents and reliability need to be correct as well to help maintain control.

What Is Inventory Control

Inventory control is a process where businesses monitor the goods coming into the firm, going out, and physically on-hand in order to meet demand. It includes such operations as inventory management and the recording of inventory sales, purchases and returns, shipments to other locations, inventory counting practices, and reorders. Whereas inventory control deals with the management and monitoring of items on-hand, inventory management can include estimating the demand of a product and purchasing arrangements.

At a retail level, maintaining inventory ensures that items listed as in stock are indeed present and can be readily sold on demand. As a warehouse, the proper oversight can allow the workers knowing where and what items are available for picking and shipping. For a manufacturing facility, the control over their raw materials and supply resources may be a major aspect of keeping its operations running. The ultimate objective is inventory information that is reliable and maintains a balance between being able to fulfill orders and not spend extra unnecessarily on storage and purchases.

Why Can Inventory Records Be Inaccurate?

Having inventory records not reflect the actual figures can happen in variety of ways:

Good may be received before they are recorded
Incorrect amounts manually entered
Stock shifted in the warehouse between locations but not designated in inventory
Returned goods or items disposed of wrongly marked
Goods inaccurately logged, whether similar products recorded incorrectly with its details or simple typos

inventory counted at infrequent times allowing mistakes to go unnoticed or undiscovered until later.

Inventory counting software may add complications for a business if the recorded amount in files are incorrect or employees enter the numbers incorrectly and inconsistently throughout a process.

A good inventory control system begins by having procedures and policies on documenting all processes related to the goods on-hand.

How Can Technology Assist in Keeping Inventory Control

1. A centralized inventory record

Using inventory systems can give different departments and employees one source representing product availability, locations, shipment, and related activities. Such a centralized database helps to have a uniform source of inventory information instead of multiple people having spreadsheets or a mix of manual entries and computer data sources. When working out of multiple facilities, the information can also help to see if a stock item is available elsewhere and if it's more practical to transfer some goods rather than buy new stock at a retail location.

The accuracy of this data is tied to the correct and timely recording of all transactions.

2. Barcode scanning and cell phone use for inventory

Bar codes allow the identification of items during receiving, processing, stock counting, shipping, and other operations. Cell phone scanning can make the recording of items faster and easier and prevent issues such as incorrect transcription found by using a hand written or manual entry into spreadsheets.

As an example, in receiving, a employee receives a pallet of stock. After scanning each item or case on the shipment, the quantity scanned will be compared with the purchase order for accuracy in the receipt of goods. Using an integrated scanning procedure in an inventory system helps streamline the receiving process and makes mistakes less frequent.

3. RFID for applicable operations

Radio-frequency identification takes advantage of tag-reader technology to recognize any tag that is compatible with the reader. Whereas barcoding and scanning requires reading of each item, RFID can be used under various settings depending on the type of reader and tags utilized. The technology can be used for identifying items and movement in inventory, counting, and other activities.

The decision on RFID comes down to whether the benefit outweighs the implementation and use cost. As with other technology, it's important to assess current procedures and how they're helped by inventory solutions.

4. Point of sale and e-commerce integration for proper sales records

When an item is sold, its' record must be updated in inventory to indicate fewer items. Failure to integrate POS or other sales channels and inventory software can cause problems where an item is displayed as available when in fact it's sold out. Making sure that any sales channel is linked into inventory is a critical first step in keeping proper records. There are exceptions and workarounds for refunds, cancellations, exchanges, or overrides, but having sales figures be integrated will be critical to having accurate data in inventory.

5. Reorder advice reports and inventory analysis for purchases

Inventory systems can be programmed to advise when an item should be re-ordered, usually when it reaches a reorder point. A simple reorder calculation is as follows:

Average daily demand x supplier lead time + safety stock = reorder point

This means that with a daily usage of 12 units, and a supplier lead time of 4 days, expected usage would be 48 units. If a company holds 10 units of the product for safety, their reorder point would be 58. The assumption would be that the daily usage and days in transit can be anticipated. The calculation may not account for fluctuations in sales volume as well as delays in supply and the quantity needed to meet minimums for ordering.

Reports from inventory systems can also help with analyzing which items are stagnant in stock, consistently low and requiring attention, and items with inventory issues that tend to be incorrect.

A Practical Example: Investigating a Stock Discrepancy

Say your system shows 120 units, but upon inventory count, your employee only finds 95. The missing 25 units have to be investigated to see what happened to them.

Step 1: Check transactions recently made and see if you find evidence of an entry error, an omission in a sale or transfer between locations.

**Step 2: **Look at any movement of goods to ensure that the goods were properly recorded as shipped or transferred.

Step 3: Check any receivings performed and ensure that the quantity entered was properly received.

Step 4: Do a recount of the on-hand stock, and check for any quantity that was misplaced in other storage areas.

Step 5: Adjust figures and document the correction accordingly.

Step 6: Make sure there isn't a systematic reason for missing stock that would require more permanent procedures change rather than just fixing a particular issue.

Maintaining a documentation trail to support the audit and finding any patterns of issues help to ensure that inventory records are correct.

Choosing the Perfect Inventory Technology for Your Business Requirements

There are many varying possibilities of systems that can be put in place for recording inventory and ensuring product availability is known reliably. This can range from a small retail store that wants to have a system where the products are well-organized and maintain basic warnings for reordering through a warehouse business that needs to have tracking at hand for multiple locations and integration. Some factors to consider include:

What business requirements need to be supported and is there an existing inventory method should be taken full advantage of or enhanced.

Scalability: Will it grow with the number of products or facilities added?

Integration: Can it interoperate with other systems such as POS and accounting software, and support such sales aspects as web stores?

**User-friendliness: **Can most employees can perform core transaction tasks without advanced training?

Reporting: Can it help in reporting and maintaining data that assists companies in decisionmaking, purchasing decisions, and other procedures?

Cost: The combined software or hardware costs shouldn't outweigh the potential benefits and other options.

As businesses consider methods of recording transactions and staying on top of inventory, they should research the options for their business needs. With a solid understanding of how they operate and what they desire, companies are advised to visit (https://theinventorymaster.com/) for an initial examination.

How Can Technology Assist Companies Improve Their Control Before Adopting Inventory Automation

Technology can become more effective when there is knowledge as to how the process works, and automation doesn't hide or overlook issues. Before companies start putting in place new inventory methods and procedures, they should take the time to:

Standardize product documentation, such as names and other attributes so they follow consistent guidelines.

Document all transactions for proper recording and have clear guidelines of how things are done.

Establish a cycle counting program and review quantities on a rotating basis versus annually.

Identify the causes of discrepancies in order to make sure the problems aren't consistently overlooked.

Assign responsibilities for maintaining records as well as reviewing any corrections made.

Examine errors occurring and work on process improvement rather than continually trying to fix them.

Start with an area of concern such as discrepancy of products or shortages in order to test and adjust procedures. Measure and analyze if changes help maintain consistent, accurate records.

Final Thoughts

Having accurate inventory requires companies to have reliable records of transactions, consistent procedures, and a method that can be easily utilized to confirm quantity on hand versus what is recorded. Maintaining these three objectives, along with implementing such tools for centralized records, scanning items, and RFID or POS interfacing, are the best ways to ensure inventory records. Their true value is realized when they can assist businesses to ensure inventory figures are up-to-date and trustworthy.

Discussion: What are the main causes of inventory discrepancies discovered by you or your peers: entry inaccuracies, lack of recording or transfer of goods, system inconsistency of multiple applications, or something else?

From Sensors to Decisions: How AI and IoT Work Together in Action

Memona — Sat, 10 Oct 2026 15:42:48 +0000

We live in an age where connected devices and systems can absorb staggering amounts of information. Information is valuable, but data in and of itself is not an actionable solution to optimizing systems. We need solutions that allow us to act on the data we receive.

Artificial Intelligence and the IoT are two fields that support and supplement each other well, and can be combined in practical applications. The AIoT is one name given to this space of technological development.

There’s a common workflow to an AIoT system that looks something like this:

Physical Environment
Sensors
Connectivity
Data Processing
AI Analysis
Decision
Action

The data from the sensors feeds the AI and provides it with information about the physical environment it’s observing. The AI produces information that can be synthesized into a decision and finally, an action.

Depending on the environment, each subunit has specific considerations to make. The AIoT’s implementation depends on factors such as latency, bandwidth, device density, connectivity, and a whole host of others.

Sensors collect information such as temperature, vibration, position, moisture, and more. Then the connectivity layer takes this data (usually numeric values from different sensors) and converts it into a usable form. The data processing stage can refine this information further, searching for patterns or outliers. At the same time, the AI uses the data it gets from the sensors or the data processing layer and identifies patterns, irregularities, or behaviors of interest. It will form conclusions about the state of the physical environment and the systems in it. These conclusions are refined into a decision that can be executed by a human or a dedicated machine.

An example could be a vibration sensor attached to a rotating piece of machinery in a factory. As the motor turns, the sensor will read values and the system will analyze them. If the values suggest an anomaly or a deviation from the norm, it can be flagged as a point of interest for a human operator to examine more closely. Once the human observes the anomaly and any surrounding contextual information, a determination can be made about whether maintenance is necessary.

Another example might be a temperature sensor in a home or commercial building that controls a thermostat. Abnormal readings can prompt the system to adjust the temperature or recommend that a technician visit to inspect the HVAC system for issues.

At the core of any AI implementation is pattern recognition and determining significance based on available information. Information becomes data when it can be interpreted, and data can be evaluated for relevance and usefulness in any given situation. Information is data that has been organized and can be further used to make a decision. AIoT is information from the physical world that has been processed and organized to determine if something abnormal or noteworthy is happening and why.

Practical Ways AIoT Can Be Used As Examples

There are many applications for AIoT depending on the industry and use case. Here are some examples of particular interest.

Predictive Maintenance Can Be Added to Industrial Equipment Management.

Machines produce information about their current state and behavior while in operation. Vibration, temperature, pressure, and power consumption are only some of the characteristics that can be helpful in determining how well a machine works and whether it requires any service.

These behaviors and properties can be evaluated by an AI system, which can flag equipment that exhibits patterns suggesting abnormal operation. These alerts can be examined by technicians and double-checked against the known condition of the machines in the field to assess the accuracy of the AI.

Asset Tracking Can Be Used Across Many Industries and Scales of Operation

Keeping track of inventory and materials can be a challenge, particularly for businesses with distributed operations, warehouses, and supply chains. Devices for tracking can be implemented on a large scale, and the data collected can be helpful both in resolving specific issues and noticing overall trends.

Data from these devices can be analyzed alongside logistical and inventory software to observe and optimize material flow. The results and suggestions can be implemented on a smaller scale or expanded depending on the results.

IoT Devices and Analytics Can Aid in Environmental Monitoring at A Construction Site

There are many aspects of a construction site that change constantly and are difficult to monitor, equipment inventory, environmental conditions, weather states, and overall coordination of people and resources.

Devices and the information they collect in situ can be helpful in understanding these conditions and behavior patterns. Analytics can streamline these into recognizable trends so that managers and supervisors on the ground can determine how to address and resolve issues.

This streamlines many processes, but only to the extent that physical conditions on the ground and the needs of personnel are understood.

Cloud or Edge Processing: The Best Choice Depends on the Situation

Edge and cloud computing are two options that can be used depending on the application. Edge processing refers broadly to computation that takes place closer to the data source. Using this method, some or all of the processing and analysis done in the cloud can be shifted to an edge node. This has the advantage of reducing latency and data transmission, required computation resources and power. It also improves reliability and security by reducing points of failure and attack vectors.

The cloud provides virtually limitless storage capacity, allowing us to collect and store data from many sources. It’s also well suited for computation – intensive processing, training, and modeling of neural networks, and overall coordination of applications and users.

In practice, both methods are often used together, with some signal processing taking place at the edge and higher-level processing and long-term planning taking place in the cloud. Each system has advantages and disadvantages that must be considered in relation to the application. Bandwidth, processing power, and costs must be taken into account alongside reliability, security, and data integrity.

Some of the other practical challenges of implementing an AIoT solution are:

Data quality and availability
Integration with existing systems
Connectivity
Security and data governance
AI reliability and evaluation
Operational auditing

Having realistic expectations about the capabilities of an AIoT system is important before designing and implementing it.

Putting It All Together, Iteratively

Rather than jumping in blindly and connecting everything possible to an AI system, it can be helpful begin with a question.

Perhaps there is an issue that needs resolving. Perhaps there is an area of operations that needs improvement, expansion, or optimization. In any case, a question worded in terms of something we can measure will help us decide what data to collect.

We can also consider where the data might come from and what it might tell us. After that, we examine if and how we can refine this information to get usable results.

Once we’ve got an initial approach, we can implement it as a test. We can measure how well it performs, use benchmarking to compare it to other systems, and modify or expand it as needed. Afterwards, we can plan for long-term maintenance and further refinement based on ongoing feedback and new information. The most useful type of AIoT implementation is one that we understand and control, rather than something that is too broad and complex to maintain.

In practice, these initial observations and tests are essential before moving to a production-level environment.

Talking About Something Bigger Than Connected Devices

We already mentioned that AI complements physical machinery in a new field that goes beyond the IoT: Physical AI.

Physical AI is intelligence embedded in and interacting with machinery, robotics, and control systems, enabling it to operate independently and dynamically in the physical world.

While AIoT can help bridge the gap between connected devices and processes, physical AI enables new technologies requiring higher levels of perception and action in the physical environment. Real physical AI applications would still require extensive engineering, testing, safety measures, and human expertise to optimize and safely implement.

For more opinions on AI and connected devices and technology for practical fields, visit Aperture Venture Studio.

The Bottom Line

The value of any AIoT implementation is defined by how useful the information it generates is in making decisions about real physical objects and systems.

A practical application always begins with a question and data that is relevant and applicable to it. Information becomes data when we can understand and interpret it, and data can be used to make decisions and take action. The AIoT can provide solutions in many fields and spans many industries, but most implementations are narrow in scope but impactful nonetheless.

If you had to pick just one, which of the three potential pitfalls for implementing AI do you think is easiest to recognize at the onset of an AIoT project?

From Sensors to Decisions: How AI and IoT Work Together in Real-World Systems

Memona — Thu, 08 Oct 2026 14:55:06 +0000

An IOT system isn't just a sensor connected to the cloud, the true challenge lies in how they communicate a reliable chain of information through to a use-case within a secure environment.

IoT systems gather information from sensors, machines, devices and the physical world. AI and machine learning can then analyze the information to spot irregularities, patterns, predictions and even make decisions.

A simple architectural design could go something like this.

Physical World → Sensors → Connectivity/Data → AI/ML → Decision → Action

Each step in the chain creates additional considerations for developer's looking to build an IoT system.

1. The Device Layer

We'll begin our exploration at the start of the pipeline - the device layer. Depending upon the application, this layer could feature:

Temperature/Humidity Sensing
RFID Scanners
GPS Tracking Devices
Cameras
Machine Telemetry Sensors
Environmental Sensors
Industrial Controllers
Smart Meters

These devices observe the physical world.

For instance, a piece of industrial machinery could be capturing information about it's vibration, temperature, speed and operating state.

It's also worth considering how valuable of a signal these devices provide.

Do we have missing, repeating, delayed or faulty information being received?

2. Connectivity and Data Transmission

We'll now move through the connectivity layer.

Depending on the application, an IoT system could utilize WiFi, Cellular, Ethernet, Bluetooth, LoRaWAN or various industrial communication protocols.

As with any IoT architecture, a lot of thought must go into connectivity, including: reliability, latency, bandwidth, device connectivity, security and intermittent connections.

Not all applications need to directly send events to the cloud. Some applications benefit greatly from processing closer to the source.

3. Edge Processing

A lot of solutions are now adopting a strategy best described by Edge Processing, where computations occur closer to the edge of an architecture in order to reduce latency or improve reliability.

For instance, A camera could potentially offload some processing to reduce the amount of information sent back to an on-premise security system.

A possible architectural design is as follows.

Device → Edge → Cloud/Data Platform → AI → Application

As discussed, the proper topology is usually determined by latency, computing power, security, connectivity and scalability requirements.

4. Data Ingestion and Storage

Now that we've transmitted the information, we will now turn our attention to data. It should come of no surprise that storage considerations become much more important when building an IoT application.

An IoT data platform will typically see a high volume of events arriving from devices in an application.

Some important considerations for developers should include: schemas, device identifiers, timestamps, validation, storage requirements, data retention, duplicates, and missing events.

A structured data schema would make data easier to consume for subsequent downstream processes.

Imagine we have an equipment event which conceptually features the following design.

device_id → timestamp → measurement → unit → location → status

Creating traceable and structured data makes the life of subsequent analytics and machine learning systems much easier to deal with.

5. Applying AI and Machine Learning

With a reliable source of information, an organization can begin to apply AI techniques to extract patterns in their data that can in turn make predictions or decisions.

Depending on the problem domain, an organization could benefit from utilizing anomaly detection, predictive maintenance, demand forecasting, computer vision, asset optimization, pattern recognition, classification or operational forecasting techniques.

For instance, imagine we have vibration measurements from a machine which we've used to train a machine learning model which will then recognize abnormal patterns.

The most important thing to remember is that AI is applied to information which in turn was captured from the physical world.

6. From Prediction to Decision

An AI model becomes more interesting when it's output can be integrated into a workflow which will allow it to contribute to a tangible decision.

For instance:

Sensor observes abnormal vibration pattern → Data Pipeline sees event → ML model identifies anomaly → application raises alert → maintenance crew investigates.

This represents a very common connection of an event in the physical world to an analytical workflow being executed in the digital world.

In highly automated plant-facilities, an authorized control system in an operational environment could also automatically execute a predefined action. This type of automation is useful but should also be carefully guarded by appropriate safeguards, permissions, validation and human-governance.

7. Reliability and Observability

An AI model could perform exceptionally well under certain conditions however begin to produce unreliable inferences in production due to issues with the data pipeline.

Some important considerations for an IoT developer should include: sensor calibration, quality of data, model drift, network failures, clock synchronization, duplicate or missing events, faulty devices, logging, model performance, and recovery strategies.

A good observability strategy will typically span all tiers within an application which makes observing just the application tier insufficient.

One effective monitoring strategy could potentially detect whether a problem originated from a device, network, ingestion layer, data store, model or the application itself.

8. Security Across the Stack

An enterprise will also have to consider security considerations across the entire architecture. An AI + IoT application represents a complex security surface which will also require operations and monitoring investments.

At various points within an architecture, security should be enforced between: devices, networks, APIs, cloud infrastructure, databases, machine learning models, and user access to applications.

Key security considerations should include: device and application authentication, encryption, secure communication channels, access control and credential rotation across APIs, data encryption at rest and in motion, and regular firmware and software updates.

A company cannot secure just the application if the underlying communication channels between a device and the cloud remain insecure.

9. Where could this apply?

The same architectural design could be utilized across industries with differing implementation considerations.

Manufacturing

Machine data can support predictive maintenance, operational analytics, anomaly detection and quality analysis.

Logistics

Vehicle and fleet information can support location tracking, route optimization and fleet management systems.

Agriculture

Environmental sensors can provide insights into field conditions which could be leveraged with analytics or AI to optimize input spending.

Construction

Connected equipment and sensors can provide insights into assets, environment and more.

Energy

Infrastructure monitoring can support anomaly, maintenance and safety predictions across oil rigs, windmills, power grids and more.

A common theme of these application domains is sense → connect → process → analyze → decide → act.

10. The Right Problem to Solve

One of the largest mistakes a team could make is choosing the wrong application for their technology.

A better approach would be something like this:

Identify an operational problem
Identify what information is required to solve a problem
Determine what sensors or devices can capture the information
Design the connectivity or data pipeline
Decide what should be processed at the edge or in the cloud
Determine what analytics or ML should be applied
Connect model outputs to a useful application or workflow
Monitor or observe how well your system is working after deployment.

By thinking in terms of a problem first, we avoid building yet-another-sensor-pipeline-without-reason.

The Big Picture

Why do we care about AI + IoT together? Essentially, we are connecting software intelligence with the physical world. We can now begin to use sensors to learn about the real-world. The data infrastructure now allows us to turn these observations into reliable information. AI can extract patterns from that information to predict future values or behaviors. Then, applications can integrate these insights to make informed decisions or authorize actions, when authorized.

This is why concepts similar to AIoT or Physical AI make so much sense for Industrial Technology.

For a glimpse into how these concepts are explored within an industrial context, Aperture Venture Studio makes an excellent reference.

The engineering challenge doesn't lie in connecting more things or deploying more AI models, it's about building secure and reliable observation pipelines which can convert physical-world data into a valuable asset for any organization.

How Modern POS Systems Help Liquor Merchants Maintain Inventory

Memona — Thu, 08 Oct 2026 13:12:32 +0000

A POS system should integrate a sale. At the most basic level, a POS system is more than an inventory logger. This is especially important in a liquor business, which often involves bulk quantities, multiple sizes, multiple stores, and rapidly fluctuating demand. Most importantly, this means establishing the connection between the POS and inventory, so that the two keep each other up to date.

POS Transaction to Inventory Event

Let’s begin with the simplest application.

Customer buy → POS transaction → Inventory event → Restocking report

A purchase results in a transaction being made in the POS system.

This transaction contains relevant information about the sale: the item, the quantity, the price, and the time and place of sale.

This information can be used to update the inventory in turn.

Here’s an example:

Item ID: 1042, quantity: -2, location: Store A, at 14:32

This is simple enough for the inventory system to understand.

Once this event is processed, the inventory quantity for that item will be updated.

In this sense, the POS and inventory systems essentially communicate directly: the POS reports sales, and the inventory tracks the sales in turn.

This, in turn, allows the inventory to build a restocking report based on the sales.

This approach entirely removes the need for double-entry bookkeeping: a sale is registered once, in the POS system, and the inventory is updated automatically.

The Importance of Data Consistency Between Systems

However, the primary issue with this approach is data consistency.

If the POS and inventory disagree on the quantity, then it becomes impossible to know which one to go with.

The two systems must communicate to keep the data consistent.

This communication can take many forms, depending on the architecture of the two systems.

Either way, it is necessary to ensure that the data is kept as consistent as possible.

In any case, the inventory system should have up-to-date information on its items, in order to make correct restocking recommendations.

Automating Manual Inventory Updates

This brings us back to the problem of manual inventory updates. The primary issue with manual updates is the opportunity for human error. It becomes a burden on the staff to keep track of sales, and update them in turn.

Physical inventory counts serve to correct any errors that have occurred since the last count.

However, these discrepancies can be a sign of other issues, such as misreceived items. If the staff has to update the inventory twice once for a sale, and once for a purchase, it becomes a serious burden.

The integration of POS and inventory systems would help to reduce the number of manual updates necessary for bookkeeping. This is especially important in liquor stores, where there is often a need to keep track of large inventories.

While physical inventory counts still serve a useful purpose, the number of manual updates in between can be greatly reduced.

Using POS Data for Inventory Insights

As a POS accumulates more sales data, it can begin to make predictions about the inventory based on that data.

This allows answering questions such as: Which items sell the fastest? Which ones tend to stay on shelves? When do sales increase? Which items tend to reach their reorder point the quickest? How do sales vary between locations?

This, in turn, can be turned into a forecast: a request for more stock, based on historical sales.

This is an example of a sale being used for inventory insights:

Sales data → Analysis → Forecasting → Replenishment → Inventory

Here, the inventory isn’t just a list of items in stock: it is a dynamic entity that reacts to sales in real time. This allows a more data-driven approach to restocking and inventory management.

Adding More Technologies to the Mix

POS systems are often part of a larger inventory tech stack.

These technologies can serve to enrich the data from the POS or provide additional functionality.

Barcodes

Barcode scanning is a convenient addition to any POS, as it allows for quick and accurate identification of items during a sale.

RFID

RFID can help to identify and track RFID-enabled items: this is useful in larger-scale operations, where there is significant inventory.

IoT

IoT devices can be used to provide additional data or react to events from the inventory system.

Analytics

Analytics platforms can analyze the data from the POS and inventory systems in order to find patterns, predict future demand, or flag outliers.

Automation

Automation can be used to accelerate the process, and reduce the number of manual steps in the workflow.

These technologies don’t have to exist in isolation: they can work together with the POS and inventory systems to create a more comprehensive overview of the warehouse or retail space.

The important thing to keep in mind is that the data from these technologies can enrich the data from the POS, and in turn make the inventory more accurate.

Tracking Inventory Across Multiple Locations

Multi location retailing has different requirements for inventory tracking.

As a multi-location POS system, you have another problem to solve, not only do you need to track inventory, but you also need to know where it is.

A central inventory overview can be useful in this case, as it allows seeing the aggregate value of inventory.

Having one central POS with multiple locations allows building an inventory overview that covers all locations.

This, in turn, can be leveraged to make more informed restocking decisions.

That is, if one location has plenty of stock, and another one is running low, it may be worthwhile to redistribute some of the excess stock between locations, rather than purchase new items from the supplier.

This is especially important at larger scales, where the opportunity cost begins to matter.

Design Notes for Developers Considering POS and Inventory Integration

When building or buying your inventory system, it is important to consider your options carefully.

There are several factors that have to be considered when connecting a POS system and inventory system.

Data consistency: what happens when the two disagree on something?

Identifiers: are the identifiers consistent?

APIs: what API does the POS provide? What does the inventory use?

Error handling: how are failed inventory updates handled?

Concurrency: how are overlapping transactions handled?

Security: who can modify inventory values?

Scalability: will the system be able to handle larger operations?

Auditability: can the changes be tracked?

There are more considerations to be made, depending on the larger application.

For instance, if the POS has to interface with other applications, such as ERP, warehouses, or accounting software, those considerations have to be made as well.

It is important to ensure that sufficient measures are taken to ensure that the data stays consistent between the two systems.

Summary

Modern POS systems enable liquor merchants to take everyday sales and turn them into valuable inventory data.

A single customer purchase can generate an inventory event, which in turn can be used to update the inventory.

With enough data, the inventory in turn can generate restocking events: a form of forecasting, based on historical sales.

This turns a list of items into a dynamic, living entity, which helps to ensure that the shelves are always stocked, without buying excess. It is this combination that creates the most value for the developer: the ability to make decisions based on enriched data.

For liquor merchants, this means fewer stockouts, fewer missed sales opportunities, and lower inventory costs.

This is just the beginning of a larger discussion around the inventory tech stack.

When it comes to inventory tracking, management, and enrichment technologies,(https://theinventorymaster.com/) has more information on the topic.

How AI, ML, IoT, Blockchain, and RPA Can Work Together

Memona — Wed, 07 Oct 2026 16:58:17 +0000

What happens when a physical machine emits data, an ML model finds an anomaly, automated workflow reacts to it, and a trusted record needs to be shared between enterprises?

How can IoT, AI, Machine Learning, RPA, and Blockchain can complement to each other?

Let me try to imagine it as a flow:

Physical Devices → IoT → Data → AI/ML → Decision → Automation → Action

Blockchain can be utilized in such a system if trustworthy and verifiable records are required by one or more parties, The main engineering question at this point is not “How can we use all technologies together?”
but rather “Which technology addresses which problem?”

1. IoT: Collecting Data from the Physical World

Internet of Things connects physical devices and machinery to the software systems. For example, a manufacturing setting can generate a number of relevant signals:

Temperature
Vibration
Device state
Power consumption
Location
Production data

A typical technical stack for IoT can look like:
Sensors
↓
IoT Gateway
↓
Data Platform
↓
Analytics / AI / ML

IoT provides information about the physical world.
That’s why AIoT (AI + IoT) is sometimes a synonym for this stage.
AI/ML can be used for analyzing this data and, therefore, addressing use cases such as predictive maintenance, quality inspection, demand planning, equipment monitoring, etc.

However, it’s important to note that ML is only as good as the data and the model.

2. AI and ML: Interpreting and Finding Value in Data

Once data is ingested into the system, AI/ML can be leveraged to extract value from it. For example, a machine learning model can learn the patterns of a physical device’s behavior and spot anomalies in real time.
This is how an ML powered predictive maintenance can be built:

Sensor Data
↓
Data Processing
↓
ML Model
↓
Pattern / Anomaly Detection
↓
Decision Support

Such a model can be utilized in use cases like predictive maintenance, quality inspection, etc. However, a machine learning model is only one part of a solution: it requires data preparation, feature engineering, training, validation, and testing.

It’s also important to remember that poor quality of input data and/or an incorrectly chosen model will result in mediocre performance.

3. RPA: Automating the Digital Workflow

Robotic Process Automation is a set of tools that allows you to automate rule-based digital processes. For example, an RPA robot can be utilized to perform certain actions in an enterprise application when instructed to do so.

In this scenario, the robot is essentially an automation engine that executes a series of defined steps.
A typical RPA-driven digital workflow looks like:

AI/ML Insight
↓
Business Rule
↓
RPA Workflow
↓
System Update
↓
Notification / Report

In other words, RPA is about executing defined digital tasks.
It is not the same as AI/ML: the latter is usually used for data analysis and decision support, while the former takes these decisions and executes them in digital systems.

For physical processes, other automation tools may have to be used.

4. Blockchain: Making Important Events Immutable

Blockchain is different in that it serves a different purpose: it allows you to build a shared source of truth about important events. For example, a supply chain involves many manufacturers, suppliers, and logistics companies.

Sometimes, certain events should be captured and verified by multiple stakeholders.

A blockchain layer would look like:

Business Event
↓
Validation
↓
Blockchain Record
↓
Shared Verification

However, you should always remember that a traditional database is often a better solution than a blockchain. Whenever performance, flexibility, and fine-grained access control are important, a standard database management system will be a better fit. A similar consideration applies to using blockchain: use it when you need it.

There are cases when a shared source of truth and verification is required by multiple independent parties.

In such situations, a blockchain can be worth considering.

Putting Everything Together

Here’s how all of the above can be combined in a connected manufacturing system:

Physical Equipment
↓
Iot Sensors
↓
Data Platform
↓
AI / ML
↓
Decision / Recommendation
↓
Automation / RPA
↓
Action

Here’s how a blockchain can be added to this system:
Physical → IoT → Data → AI/ML → Decision → Automation → Action
↑ ↓
Blockchain: Trusted Record
Keep in mind that this is a very simplified view: there are more steps and supporting systems such as data governance, data quality management, API gateways, edge processing, etc.

The above is just one possible way of building a connected system.

What Should You Implement First?

When you start working on a project, you should never focus on technologies.
Focus on your problem domain first.
Here’s an example:

Step 1: What Is the Use Case?
Let’s imagine that the use case is to avoid unplanned equipment downtime.

Step 2: What Data Do We Need?
We probably want to collect data about temperature, vibration, machine age, state, maintenance history, etc.

Step 3: Let’s Build a Pipeline
We need to make sure that the relevant equipment is connected and monitored.

This could be a very simple pipeline:

Sensors
↓
Gateway
↓
Message Broker
↓
Data Storage
↓
Processing

Depending on requirements and data volume, some processing can be done on the server or at the edge.

Step 4: Let’s Add Some Intelligence
Once you have enough data, you can think about how you can utilize it.
You can process it to gain some operational insights or apply machine learning to find patterns and anomalies.
Make sure that you have a meaningful use case: do not build an ML model just because you can.

Step 5: Turning Insights into Action
Whenever an interesting finding emerges, you will likely want to do something about it.
If the response is deterministic, you can utilize RPA to automate it.
For example:

Anomaly Detected
↓
Maintenance Threshold
↓
Create Work Order
↓
Notify Team

Such a process can be fully automated or involve manual steps whenever the decision has to be made or a human has to review the finding.

Step 6: Do We Need Blockchain?
If there is a need for several independent parties to have access to the same information and verify certain events, you can think about adding a blockchain layer. This may not always be the case: if no such requirement exists, you can skip this step.

Where Does AIoT Fit In?

AI + IoT is often referred to as AIoT.
This concept can be seen as yet another stage in the system:

IoT: What is happening in the physical world?
AI/ML: What does the data mean?
Automation: What should a digital system do?
**Physical Systems: **What should happen in the physical world?

This fits into the larger system as a “loop”:
Sense → Identify → Decide → Act
The decision can be automated (e.g., turn a machine off) or may require human intervention.
This approach fits into manufacturing settings with interconnected, smart equipment that seeks to sense, identify, decide, and act in order to fulfill a larger use case.(https://apertureventurestudio.com/)

Final Notes

When you look at the above, it becomes obvious that the single biggest mistake is to try to apply the technologies in isolation.

The worst approach is to think something along the lines of “We need IoT, AI, blockchain, and RPA because they are cool technologies.”

Instead, you should always design your system around the problem that you have to solve.

You should answer questions such as:

What are we trying to achieve?
What data do we need to acquire?
Where can we get this data?
Where will we process it?
What analysis do we have to perform?
What decisions can be automated?
Where do we need humans?
Do we need blockchain?
What about scalability and security?

The resulting architecture may involve all of the above or may not involve any of these technologies, it all depends on your use case.
Good engineering is not about adding technologies: it is about choosing the right technologies to address the problem at hand.

AI, Robotics, and the New Jobs Coming to Exist Around Automation

Memona — Wed, 07 Oct 2026 15:48:55 +0000

When software interacts with automation hardware, robots, cars, and other physical objects, what does that say about the role of developers?

When discussing automation, the focus is usually on removing repetitive human effort.

But to implement every form of automation requires specialized technical and analytical professionals to design, maintain, protect, and improve those systems.

This is where AI, robotics, automated transportation, Industrial IoT, and 3D printing come in.

What are the top technical specialties that could help drive the demand for automation?

1. Robotics and Automation Technicians

Industrial robots and other forms of mechanical automation can handle a lot of physically demanding or repetitive tasks, but that doesn't mean that they are completely self-sustaining.

Someone has to set them up, watch them, and maintain them.

A robotics and automation technician could find themselves working with:

Industrial robots
Sensors and actuators
PLCs and controls
Machine vision
Inspections/automated quality controls
Production line software

In many ways, developers could find interesting work around understanding how computer-code controlled processes interact with the physical world.

2. Manufacturing Data Analysts

Modern factories can produce an enormous amount of data, through process monitoring and control, visual inspections, and automated machine diagnostics.

The hard part is always making that data relevant, actionable, and interpretable for humans.

Some form of manufacturing data value chain could be:

Sensors → Collection and processing → Storage and processing → Analytics and reporting

Some of the work of a developer could be identifying and implementing analytics and decision pipelines, finding patterns about machines that could suggest quality control issues or process improvements, or understanding relationships between production quality and other factors.

This is all very relevant to programming, data science, and manufacturing analytics.

3. AI and Machine Learning Specialists

There are many applications for AI and ML in manufacturing: detecting quality issues, detecting demand patterns, predicting machine failures, forecasting process improvements, etc.

The main issue is usually implementing an ML model behind a working manufacturing process, which can involve not just ML, but data processing, analytics, and logistics expertise.

Developers and other programming professionals should consider ML/AI for manufacturing work; but keep in mind that an ML model isn't the end of the conversation; you have to consider:

Data and input quality
Information delivery and presentation
Inference pipeline latency
System integration
Model monitoring and retraining
Model failure criteria
Human model review/editing capability

Whatever your ML model is, it has to actually work in production and be integrated into the wider manufacturing or automation process.

4. Industrial IoT Specialists

Industrial IoT covers a lot of ground, but essentially it is a catch all term for linking hardware sensors and software systems.

A general architecture could be:

Machines/robots → Physical sensors → Network connection → Server → Analytics and decision support → Network and/or Human

The kinds of skills relevant here can vary immensely, but generally could involve APIs, databases, and network engineering at the very least, edge computing could also be involved.

In some ways, it is similar to other data pipeline systems, but applied specifically to manufacturing or automation contexts.

Organizations such as(https://oemnexai.com/) work in the space of AI, IoT, and automotive manufacturing, representing another specialty that could link software and automation.

5. Predictive Maintenance Specialists

Traditional preventive maintenance keeps machines running by replacing worn parts and finding potential issues before they occur.

Predictive maintenance takes the data from those monitoring systems and implements it into a decision support model.

Many such systems work with:

Sensor data
Machine telemetry and statistics
Historical data
Machine learning
Anomaly detection
Predictive analytics
Human in the loop diagnostic and repair processes

The main technical issues often aren't the creation of a model, but also working with statistics, domain expertise, and edge cases to make sure that your system actually makes accurate predictions about equipment behavior without false positives.

6. Industrial Cybersecurity Professionals

IoT and automated machinery often involve a wide network of hardware and software, all of which may need protection against malicious interference.

Network intrusion, equipment sabotage, and data breaches would all be very expensive and disruptive for manufacturing and logistics automation systems.

Cybersecurity professionals understand network security, zero-trust access control, and threat analysis, making them a good fit for helping to protect critical infrastructure: servers, databases, connected systems, IoT devices, and more.

Industrial cybersecurity covers all of that and more; there are many sub-disciplines and specialties that could interest software security professionals.

As networking and automation increase, so will the need for security experts.

7. Autonomous Vehicle Support Positions

Automated and driverless vehicles require a lot of different systems to come together: sensors, perception, mapping, networking, security, and more.

As such, there are many potential roles for cybersecurity, software, and data analysts in supply chains, logistics, and even fleet maintenance and diagnostics.

Autonomous and connected vehicles are another example of the convergence of software with the physical world, and there could be many different kinds of specialties involved.

Existing Skills Remain Relevant

One skill that isn't mentioned much when talking about automation is upgrading your existing skills to better meet demand for automation.

A maintenance worker could gain data analyst or AI skills. A factory worker could learn about automation technologies. A networking professional could transition to a job in industrial or cybersecurity. A software developer could learn to integrate their applications into an automated machine or manufacturing process.

This is always something to keep in mind when discussing the impact of automation and AI: new specialties will arise, but it is also a chance to enhance old skills in order to make a bigger difference.

The Developer Role is Changing

For developers, the biggest change usually involves understanding systems that run in concert with automated manufacturing, logistics, and machine processes.

Code no longer exists exclusively in applications or even in servers; applications now communicate with and control robots, drones, manufacturing sensors, automated vehicles, and more.

In turn, that requires developers to not only understand business logic, but also the limitations and possibilities of peripheral components.

For example, developers may need to understand:

How information is gathered about the physical world (sensors, camera, machine diagnostics)
How that information is transmitted (APIs, network transfer)
How that model may involve unsteady or unreliable information (edge computing, data science)
How to handle physical system failures (network interruption, system delay)
How decision-making processes are evaluated, monitored, or edited (AI, cybersecurity)
How to protect automated and connected systems (cybersecurity)

AI, data analysis, cybersecurity, edge computing, networking, and automation aren't just separate specialties for developers; they are merging together in the realm of computer science and engineering.

Humans and Machines

Sometimes automation is pitched as a binary choice between the dominance of man or machine.

A more realistic outlook is a collaborative space between the two.

Machines could automate processes and free people to focus on higher level concerns; people could add nuance, interpretation, and understanding to data and systems that machines alone would miss.

The technical challenge always involves finding ways that people and machines can work together to solve problems. And as AI and robotics become more sophisticated, that will continue to be an issue.

In short, the future doesn't require more robots; it may very well require more people to understand the relationship between robots, data science, computers, and code.