What are robots and RPA: types, how they work, implementation examples, and the benefits of automation for business in CIS

Robots are devices or programs that automatically perform actions according to a defined algorithm. Their task is replace or complement a person in routine, dangerous, complex, or repetitive operations. There are two main types of robots.

Physical, or hardware, robots This is mechanical deviceswhich can move, manipulate objects, travel through space, and perform physical work: - industrial robots in factories: they handle welding, painting, and parts assembly; - warehouse robots (for example, at Ozon and Wildberries): move goods; - medical robots: assist during operations; - delivery robots: deliver parcels; - robots in housing and utilities: clean streets and collect trash. How they work: 1. Sensors- camera, LIDAR, touch, temperature, and other sensors - gather information about the environment.

2. Controller / processor - the robot's "brain". It makes decisions based on data. 3. Actuators and motors - move limbs, wheels, and tools. 4. Software - defines action algorithms, routes, and work scenarios. Software robots (RPA) are virtual robotsthat work inside computers and servers.

They automate routine actions that used to be done by a person with a mouse and keyboard: - automatic processing of requests from email; - collecting data from websites and entering it into CRM; - working with accounting systems: exporting reports and reconciling data; - processing resumes or bulk applications; - filling out forms and generating documents. How they work:

Level 2: Rules and branching scenarios

The robot follows decision treecomes in, analyzing multiple conditions.

For example: - an RPA robot that checks which client sent the invoice and chooses one of the processing scenarios; - a warehouse robot that changes route depending on obstacles. Advantages: - flexibility in scenarios - the robot handles more complex logic with multiple conditions; - making simple decisions: "if the amount > 100 thousand, send for review"; - exception handling - the robot responds to common errors or missing data; - the solution is suitable for 80% of business tasks: finance, document flow, and logistics. Drawbacks: - logic design is required - you cannot just "turn on" the program - the scenarios must be carefully designed; - working strictly by template - cases not covered by the logic are not handled; - the solution may become more complex over time - when there are more than 100 conditions, the logic becomes hard to maintain.

Level 3: Machine learning

The robot learns to make decisions based on big data and examplesrather than only by rigid rules. This is where artificial intelligence: - Email classification: the robot "reads" an email and identifies its type using NLP - natural language processing. - Image recognition: the robot "sees" a defect on the line and rejects the part. - Forecasting: AI predicts which product the customer is most likely to need, and the robot places an automatic order.

An AI-powered robot "makes decisions" based on of probability.It does not explainwhy it did things one way or another.

The quality of decisions directly depends on data qualitytraining and testing. Advantages: - works with unstructured data - text, images, and voice; - adapts to new information - learns from examples and improves over time; - processes large volumes of datathat cannot be handled manually; - supports context-aware processing - takes into account not just one rule, but the meaning of the whole situation; - creates a competitive advantage - handles requests faster than competitors. Drawbacks: - expensive development - requires data scientists, infrastructure, and data; - "black box" - AI does not explain why it made a particular decision; - large training datasets are neededso the model is accurate; - the risk of errors in unpredictable situations - AI cannot be allowed into critical business processes without verification; - difficulty of validation and certification,especially in the public sector and healthcare.

Level 4: Hybrid systems

1. It is a combination of scenario logic and AI: - RPA for executing actions -> click, enter data, launch a system. - AI for analysis -> recognize, understand, choose an option. Example:
2. The robot receives a document.
3. Extracts data using OCR + NLP.
4. AI classifies the document type. 4.

Based on the result, the RPA robot launches the required process. Advantages: - a balance of flexibility and controllability - AI handles complex tasks, while RPA executes predictable ones; - scalability - can solve an increasingly wide range of tasks; - deploy faster than pure AI - fewer infrastructure requirements; - smart but controlled solutions - RPA executes actions consistently, while AI evaluates the situation. Drawbacks: - architectural complexity - AI and RPA systems must be properly connected; - support for two technologies - both RPA and AI require a team; - is neededDevOpsand CI/CDespecially when scaling; - process and IT environment maturity is needed - the solution will not work in "chaos".

Difficulty of preparing processes for automation

Most processes in companies are - unformalized or poorly documented. They must first be "cleaned up," standardized, and documented. Without this, the robot will "break" on every exception. In HR, 42% of CIS companies have processescompletelymanual - not ready for out-of-the-box automation.

Limited decision-making

Software robots follow the algorithm strictly: if the situation deviates from the template, they cannot adapt. This is critical in unstable conditionslegal changes, and nonstandard tasks. Exceptions require human intervention, which reduces the effect of automation.

Risks of losing control and vendor dependence

With extensive automation, a business can become depends on IT companies or a specific vendor's solutions. If the supplier changes, sanctions are imposed, or the platform becomes outdated, expensive reimplementation is required.

Staff resistance

Employees often perceive robots as a threat: fear losing their jobs, do not trust IT, and have had negative implementation experiences. Companies need manage changetrain and adapt employees. In 33% of CIS companies, staffresiststhe adoption of AI technologies.

Scaling difficulties

The pilot may succeed, but scale robots to other departments or processes is difficult because of: - heterogeneous IT environments; - mismatched business logic; - security and access restrictions.

Low robot density in CIS

In CIS, per 10,000 manufacturing workers account for only 10-11 industrial robotswhereas in: - South Korea - more than 1,000; - Germany - 397; - the US - 285. This means CIS enterprises: - have low automation maturity- are not ready for mass robot deployment without infrastructure changes.

There are several reasons for this: - Skills shortage: a shortage of specialists in RPA, machine learning, and industrial automation. - Regulatory barriers: data protection, security, and licensing requirements, especially in the public sector. - Dependence on imported components: despite import substitution, 75% industrial robots and components - still foreign.