Emerging Technologies in Automation: From AI and RPA to Blockchain and IoT

Emerging technologies in automation have ceased to be just a topic of speeches at conferences; they have already become an everyday reality in companies in Romania. Invoices are processed by themselves, sensors announce a malfunction before the machine stops, and a contract is automatically executed when the goods arrive at the warehouse. Behind these scenarios are various families of technologies that combine with each other. Next, we will discuss four of them in more detail: artificial intelligence (AI), robotic process automation (RPA), blockchain and the Internet of Things (IoT).

In this glossary-type article, we will show what each technology means, how they differ, how they complement each other, and what skills you need to work with them. The article is written for both those evaluating an investment in automation and those preparing their transition to a job in tech.

What Are Emerging Technologies in Automation?

Emerging technologies in automation are those digital solutions in a rapid phase of maturation and adoption, which allow the execution of repetitive, decision-making or physical processes with minimal human intervention.

Unlike classic automation, based on rigid scripts and fixed rules, these technologies bring three new elements:

• the ability to learn from the data obtained
• the ability to perceive the physical environment in which it is located
• the ability to execute actions without an intermediary.

In practice, these technologies have certain common denominators:

• They are interoperable. They are rarely used in isolation; the real value appears at the intersection between them.
• They are based on data. Without a clean and accessible data flow, none of them produces results.
• They are scalable in the cloud. Their adoption no longer necessarily requires large investments in its own infrastructure.
• They change the role of the human, not eliminating it, but directing it from execution to supervision, design and exception control.

Below you will find the definition of each technology, followed by concrete applications.

Artificial Intelligence (AI): Definition and Role in Automation

Most of us use it; but how many of us really know what it means?

Artificial intelligence is the branch of computer science that creates systems capable of performing tasks normally associated with human intelligence: pattern recognition, natural language understanding, reasoning, decision making, and content generation.

In the context of automation, AI is the component that brings judgment to a process: it decides, classifies, prioritizes, and interprets unstructured information.

Artificial intelligence is a broad concept; several of its subdomains are particularly relevant to business automation:

• Machine Learning (ML) – the system learns patterns directly from historical data, without being explicitly programmed for each rule. It is the basis of demand prediction, credit scoring, or fraud detection models.
• Deep Learning – an ML technique based on multi-layer neural networks, used for complex data: images, sound, text.
• Natural Language Processing (NLP) – natural language processing; it is the basis of chatbots, sentiment analysis, vibe coding and document information extraction.
• Computer Vision – automatic interpretation of images and video streams: quality control on the production line, reading license plates, checking protective equipment.
• Generative AI and AI agents – models that produce text, code or images and which, integrated as agents, can plan and execute sequences of actions in other applications.

What Can Be Automated with AI

In practice, AI is not implemented at the company level, but department by department, on well-defined use cases. This is how the professional approach to the role of an AI Automation Specialist is structured: starting from a library of use cases by functions, real bottlenecks are identified through root cause analysis and only then the technology is chosen.

Three solution patterns can cover most of these cases:

• Knowledge Assistants (RAG) – Retrieval Augmented Generation means that the model responds based on the company’s internal documents, not just what it “learned” during training. It is the solution for internal procedures, knowledge bases and customer support.
• Visual automation flows – built in platforms like Make, n8n or Zapier, connect existing applications and run business logic without classical development.
• AI agents – systems that plan and execute multi-step sequences of actions, using external tools. They are justified only when the process is truly variable; for stable flows, simple automation is cheaper and more reliable.

Regardless of the pattern, well-designed flows maintain a human-in-the-loop escalation point: high-impact decisions or cases where the model has low confidence go to a human, not directly to the customer.

Rule of thumb: AI is used where the process requires interpretation, not just execution. And the selection criterion is not technical feasibility, but estimated ROI — implementation cost, generated value and breakeven point. As Tiberiu Radu, trainer of the AI ​​Automation Specialist course at NewTech Academy, summarizes, the right question is not what AI can do, but which process is worth automating.

RPA (Robotic Process Automation): Definition and Mode of Operation

RPA (robotic process automation) is the technology through which “software robots” imitate the actions of a human user at the level of the graphical interface of applications: they open files, copy data, fill out forms, send emails and navigate between systems, based on predefined rules.

The essential difference from AI: RPA executes, AI decides. An RPA robot does not learn or improvise — it follows a set flow, which makes it extremely reliable for stable, repetitive and high-volume processes.

The Defining Characteristics of RPA

Types of RPA Robots

• Attended – start at the command of an employee and assist them in real time (e.g. in a call center).
• Unattended – run autonomously, scheduled, without supervision (e.g. nightly accounting reconciliations).
• Hybrid – combine both modes in a single flow.

Intelligent Automation and Hyperautomation

When RPA is combined with AI, the result is called Intelligent Process Automation (IPA): the robot no longer needs perfectly structured data, because the AI ​​”reads” and interprets the input before execution.

Hyperautomation is the umbrella concept that describes the coordinated use of several technologies — RPA, AI, process mining, low-code, API integrations — to automate as much of an organization’s process chain as possible, not just isolated tasks.

Blockchain: Definition and Use in Automation

Blockchain is a distributed digital ledger, in which transactions are grouped into cryptographically linked blocks and replicated across multiple nodes in a network, making records virtually impossible to modify retroactively without the consensus of the participants.

In automation, blockchain solves a specific problem: trust between parties who do not know or supervise each other. Where a process gets stuck because someone has to manually verify and confirm, blockchain can eliminate the middleman.

Key Elements of Blockchain Technology

• Distributed ledger (DLT) – there is no single “official” copy held by a single entity.
• Immutability – once validated, a record cannot be discreetly deleted or altered.
• Smart contracts – programs stored on the blockchain that execute automatically when pre-established conditions are met. Example: payment is automatically released when delivery is confirmed by an IoT sensor.
• Traceability – the entire history of an asset can be reconstructed end-to-end.
• Permissioned networks – private blockchains with controlled access are frequently used in the corporate environment, not public networks.

Real-World Applications in Automation

• Supply chain: tracking the origin of food or pharmaceutical products.
• Logistics: digital transport documents, automatically validated.
• Financial: settlements and reconciliations without manual verification processes.
• Energy: automatic trading of surplus energy between small producers.

Important to remember: blockchain is not useful in every process. If all data is controlled by a single organization, a classic database is faster and cheaper.

IoT (Internet of Things): Definition and Role in Physical Automation

Internet of Things is the network of physical objects (equipment, vehicles, buildings, devices) equipped with sensors, software and connectivity, which collect and exchange data with other systems via the internet, allowing remote monitoring and control.

If AI and RPA automate the digital world, IoT is the bridge to the physical world: without sensors, an algorithm has nothing to observe in the production hall, in the warehouse or in the field.

Components of an IoT System

1. Sensors and actuators – collect data (temperature, vibration, position, humidity) and execute commands.
2. Connectivity – Wi-Fi, 5G, LoRaWAN, NB-IoT, Bluetooth Low Energy.
3. Aggregation platform – collects, normalizes and stores data streams.
4. Analytical layer – transforms raw data into decisions, often with the help of AI models.
5. Control interface – dashboards, alerts, control systems.

Related Concepts Worth Knowing

• IIoT (Industrial IoT) – the application of IoT in manufacturing and critical infrastructure.
• Edge computing – processing data as close to its source as possible, directly on the device or on a local gateway, instead of sending it to the cloud, for reduced latency and lower bandwidth consumption.
• Digital twin – a virtual replica of a physical equipment, process or system, fed in real time with sensor data, used for simulation, optimization and predictive maintenance.
• Predictive maintenance – intervention on equipment before failure, based on patterns detected in sensor data.

The Four Technologies, Compared

How Emerging Technologies Combine in Automation: an End-to-End Scenario

The real value comes when these technologies are integrated. Here is a fully automated logistics flow for a refrigerated freight carrier:

1. IoT – sensors in the refrigerated container continuously transmit temperature and GPS position.
2. Edge computing – data is filtered locally; only relevant deviations reach the cloud.
3. AI – a model predicts the risk of delay and recalculates the optimal route.
4. RPA – the software robot automatically updates the ERP, notifies the customer and generates receiving documents.
5. Blockchain – a smart contract verifies compliance with the cold chain and automatically releases payment to the carrier.

The result: a process that previously involved five people and two days of checks is now executed in real time, with human intervention only in exceptions.

Industry Applications

Benefits and Challenges of Automation with Emerging Technologies

Main Benefits

• Reducing operational costs on high-volume, low-value-added processes.
• Speed ​​up from hours to seconds on standardized streams.
• Reducing human errors in repetitive activities.
• Real-time visibility into operations.
• Scalability without proportional team growth.
• Easier compliance through full traceability of actions.

Real Challenges, to Plan For

• Data quality. An AI model trained on incomplete data produces bad but fast decisions.
• Cyber ​​security. Every IoT device is a potential gateway to the network.
• The regulations. The AI ​​Act, adopted at the level of the European Union, introduces differentiated obligations according to the level of risk of the AI ​​system, and data processing remains governed by the GDPR.
• Technical debt. Automating a poorly designed process doesn’t fix it—it just makes it fail faster.
• Skills deficit. The most common barrier is not the technology, but the lack of people who know how to implement and oversee it.
• Resistance to change. Without clear internal communication, adoption fails even with good technology.

What Skills Do You Need in Order to Work with Emerging Technologies in Automation?

The fastest growing roles — AI Automation Specialist, RPA Developer, Automation Analyst, IoT Solutions Engineer — require a mix of technical and business skills:

• Understanding of business processes and the ability to document and optimize them before automation.
• Basic programming, most commonly in Python, for integrations and scripting.
• Working with APIs and visual automation platforms.
• Data analysis: SQL, interpretation of results, building dashboards.
• Prompt engineering and integration of AI models into existing workflows.
• Concepts of cyber security and data governance.
• Critical thinking: knowing when a process is not worth automating.

If you want to enter this field with a clear structure, NewTech Academy’s AI Automation Specialist course covers exactly this combination of skills, with hands-on projects. For the data analysis side, which supports any automation initiative, you can also explore the Data Analyst course, and for programming fundamentals, the AI Fullstack course.

Conclusion

Emerging technologies in automation are not four competing directions, but four pieces of the same system: IoT brings data from the physical world, AI turns it into decisions, RPA executes, and blockchain guarantees the integrity of the result between independent parties.

Organizations that achieve real results are not the ones that buy the newest technology, but the ones that first understand their processes, clean their data and train their people. And for a professional, this is probably the best time in recent years to enter the field: the demand for specialists is growing faster than the supply of trained people.

Do you want to turn this understanding into practical skills? Explore the NewTech Academy courses and choose the right direction for you.

Frequently Asked Questions