Despite being considered controversial by many, robotization continues to advance in the world. With it, several businesses have increased efficiency and reduced costs, making them more profitable.

Much is said about Digital Transformation thinking about creating websites and systems, in addition to virtual service (as in social networks).

Do you know what Digital Transformation is? Do you understand how robotics can be helpful in this scenario? In this post, you will have the answers to these questions and understand how companies have been modernizing.

Do you want to be on top of the news, to achieve better results? So, read on to find out all about the topic. Good reading!

Digital Transformation In Companies

It is common for the general public to associate Digital Transformation with companies’ acquisition of new technologies. However, this is far from being a complete truth.

As its name suggests, it is linked to a significant and profound change in how the company relates to consumers and their new demands. With this, the operation of institutions becomes utterly different from what is common today.

To transform itself, the company needs to change the entire organizational structure, relying on technology in each process. Consequently, it will become more digital, helping to democratize the use of technology — as this only happens when there is a membership.

Therefore, it is common for companies that consider adopting technologies in business processes to be seen as more modern and attractive institutions. The image that the customer has is of someone who is always thinking about the future, which is essential for brands that want to attract the consumer’s attention and show confidence.

Although Digital Transformation has been happening in the market for many years, it can no longer be avoided. The longer a company takes to carry out its changes, the greater the chances of losing space and seeing the changes happen from the box without being able to participate.

In this sense, barriers must be broken, especially cultural ones that link digital to the dangerous, the unknown, and even the difficult. Many entrepreneurs believe that they would not be able to manage the company if it had different processes, for example — and it is, in this sense it is necessary to work.

Robotization For Companies

Anyone who thinks that robots are only used in science-fiction movies is wrong. Currently, several are used to increase companies’ production and serve customers more efficiently.

Sectors such as the automotive, food, and chemical industries, as well as beverages and electronics, are the ones that most use robotization. However, any company can use it to increase the productivity of sectors.

Digital businesses, for example, can let artificial intelligence take care of part of customer service. As a result, customers are responded to more quickly, leaving the support to specialists for more complex cases that require human attention.

In a competitive market, there is a need to reduce costs in all possible processes. With this, it is possible to lower the price to the final consumer and attract even more people to consume their products and services.

Characteristics Of Robotization Applied To Business

In business, robots that can make processes more intelligent and learn from use are on the rise. Understand a little more about it.

Smart Processes

It’s hard to work with information in text, emails, messages on social networks, etc. This happens because this data needs to be structured. With this, the robot must understand which information is helpful and valuable for the institution.

That is, this machine imitates a little of the human cognitive capacity, being able to understand the language itself, which is essential to make decisions. This happens mainly through the use of the data cross-checking technique.

Cognition

Companies that want to take Digital Transformation to a higher level can go to the last stage of corporate robotization. In this case, it is possible that robots learn from their own experience and can change behavior according to the new information received, adapting to increasingly complex tasks.

With this, it is possible to extrapolate the initial movement of learning, allowing the ability to integrate systems that they did not yet know, make complex reports in Big Data, interpret the linguistic content, and deal with past information.

Most of the systems used in robotics are limited to rules. This means that the robot will only do what it has been taught. No response will be given if a different environment or an unforeseen situation appears.

To understand better, you can imagine a call center that gives options from 0 to 5. If the user types 9, no action will be performed, as the systems will not understand the request.

The great advantage of this type of system is the simplicity of programming and the low cost required for the operation to be in effect, also requiring less hardware.

With cognitive systems, it is possible to leave part of the administration and execution of tasks to robots, reducing costs and increasing productivity, efficiency, and quality of actions since production errors tend to be smaller.

For now, values ​​are still expensive. But, with popularization, the tendency is for it to become cheaper and be widely used in corporate environments.

Internet Of Things (IoT)

In addition to robotization, significant trends pass through the Internet of Things. With it, Digital Transformation can be seen even more clearly by smaller companies and even at home.

From it, equipment and parts with sensors connected to the Internet can transmit information to each other, forming a large chain. This way, management becomes more efficient since the main business variables will be interconnected and communicated.

As part of robotization, systems can make decisions about business processes without human intervention. It is possible to imagine the concept linked to a fleet control system.

Also Read: Difference Between RPA And Artificial Intelligence

Machine Learning: How would it be if we could transfer repetitive and unproductive tasks to an intelligent computer system? That way, we could focus on more challenging work and add value to the business.

The problem is that such a solution does not exist. Correct? Not! Machine learning and the various artificial intelligence and data analysis tools can hand over most of the work to the machines themselves.

Thinking about talking more about the concept, its applications, and its many benefits, I prepared a private post on the subject. The subject has been much discussed lately, even due to the General Data Protection Law. Good reading!

After All, What Is Machine Learning?

Also known as machine learning, technology can be described as the ability to teach computers to think and act independently. In this way, the software can learn, through prescriptive data analysis, to present precise answers to some computational issues.

The concept seems quite far from the everyday application. Nothing further from the truth. This technology has already become standard in people’s routines — not just those who work with digital solutions.

So, when you do a Google search or accept a new movie suggestion on Netflix, you use a machine learning application directly. This is because technology makes it easier for the search engine to identify better results and even points out television attractions that may interest you based on your browsing history.

The main objective of this technology is to make machines able to make decisions without human interference. This becomes possible due to the algorithms (logical sequences of instructions passed on to the computer), which enable the computer system to identify patterns and point out appropriate solutions.

Unlike what happens with a joint program, a machine learning system can learn while performing the functions for which it was programmed. This feature makes it much more efficient compared to traditional software.

Connection With Artificial Intelligence

When reading about technology, people often come across concepts like machine learning being used interchangeably. This conception is partially correct, but it is simplistic: machine learning is one of the manifestations of AI.

In this sense, it joins other widely discussed technologies, such as Big Data, to enable practical applications of artificial intelligence.

Difference From Deep Learning

Similarly, deep learning is a way to put artificial intelligence to work. While machine learning works exclusively with structured data and requires the organization of this information, sophisticated deep-learning technology, and self-learning capabilities result in greater accuracy and faster processing.

Thus, machine learning has laid the foundations for deep learning to evolve, inheriting the main features of the traditional machine learning model and developing new features.

An exciting and recurring application of deep learning algorithms is optimized image recognition.

Why Invest In Machine Learning?

Now that we know the concept and its relationship with artificial intelligence and deep learning, it is time to learn about the advantages of investing in technology.

Background Knowledge For Decision Making

With an increasingly fierce market and the ease of customers finding new options, the most efficient managers must find differentiated strategies to identify new business opportunities.

Without this care, retaining consumers and winning over new customers becomes challenging to work. However, to develop strategies that allow more profitability, it is necessary to have accurate information. That’s where machine learning comes in.

That’s because every company generates a high load of data, especially with the internet guiding most of the business. With machine learning and data analysis tools, it will be possible to map the organization’s digital history, establish consumption patterns and generate new practical loyalty actions.

Machine learning studies information related to the business to trace consumption habits. It is possible to determine which products sell the most in a given period and anticipate this demand for the following year. The tool goes through the purchase history and offers valuable insights for the company.

Optimized Use Of Resources

A meaningful way to measure the success of an enterprise is to study how it directly allocates its physical and financial resources. A company that fails in this management tends to suffer continuous losses and show low overall productivity.

However, manually mapping all processes to identify failures and point out bottlenecks is exhausting for employees. It is necessary to appoint leaders and displace them from their usual functions, for example.

As if that needed to be more, every employee, no matter how talented they may be, can get complicated when dealing with a large amount of information. The company benefits from delivering purely manual jobs to machines by using machine learning solutions and automating some processes.

In addition to encouraging employees to dedicate themselves more to the company’s core activities, this digital solution also reduces rework, eliminating the risks associated with human errors.

Increased Profitability

Let’s assume that your company manages to commercialize a good amount of products annually. However, despite the numbers covering the losses, it has not grown significantly. That’s because daily activities make it difficult to identify new opportunities.

In this context, machine learning emerges as a tool capable of pointing out new paths for the organization. This is because technology contributes to the company being part of a data-driven culture — that is, one that uses Big Data and machine learning to find new ways to innovate.

The technology can analyze public databases such as government studies, social media, and information found on the internet to identify trends. This is especially important for finding gaps in the market and taking advantage of them to launch products accordingly, generating more profitability for the business.

Also Read: Machine Learning: The Technology Has So Much Potential In Production