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.
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