Find out how the Robotic Process Automation will change the future!

You are probably wondering what RPA is. We could give you a definition right away, of course. But it’s easier to take it by the hand and give you a chance to visualize what it is and the incredible potential it holds for every company.

 

Let’s imagine an employee at his desk reviewing a massive mountain of customer requests. He must allocate his time in the best possible way. Filter the data from each application, sort through the applicants, and determine which service best suits their characteristics. You will undoubtedly do your best, but it will take you a long time, affecting the response time to the customer.

It may be the case of an insurance company or a company in the financial sector. The options are immense, but the solution to this common situation today lies in RPA or Robotic Process Automation.

What is Robotic Process Automation?

RPA is a technology that allows designing a software or software robot in a way that it can:
 
  • Control existing applications
  • Process transactions
  • Record data
  • Elaborate responses
  • Communicate in a coordinated manner with other systems.

It offers the possibility of automating any process carried out by a worker, which involves a high volume of data, is repetitive and routine.

 

It facilitates the management of large volumes of data and the integration of workflows efficiently in an organization.

Jack Moreh - Freerange

Advantages of implementing RPA in your company

Opting for robotic process automation in your company is not a decision to be taken lightly. It requires a thorough analysis and diagnosis of processes to define which areas are susceptible to RPA. Always with the basic premise: to automate routine, repetitive, and data-intensive tasks.

The main advantages offered by RPA are the following:

 

Favorable change in the destination of your workforce’s working time.

Automation will allow you to free your staff from repetitive and cumbersome tasks that consume their time and are not being performed as efficiently as possible. Handing those tasks over to a software robot that will work 24/7 and deliver results in less time will allow your staff to channel their time and effort into activities that are much more productive for the organization.

There are even studies that show that the employee’s commitment to the organization increases. Job and personal satisfaction increase as they feel involved in activities that contribute more significantly to the organization and fulfill its objectives.

Reduction of the time of specific tasks in a project.

By having a software robot to streamline repetitive tasks and large volumes of data, less time is spent, which will impact favorably on the development of projects that feed.

Improved customer service quality

Undoubtedly, all those tasks that aim to provide solutions to customers will improve in real-time. Either by reducing the response time to their requirements or by delivering better quality products and services.

Significant cost reduction

A software robot can work 24/7 and do the work of several employees easily and quickly. This translates into a better distribution of the existing workforce for activities and their contribution to the organization. Also, a reduction in the need to incorporate a new crew with the associated costs.

Zero errors

A robot can be programmed to work without making errors, which increases the reliability of the results it delivers and minimizes the rework they cause.

The success of RPA in different sectors

Process automation has proven its effectiveness in different sectors of the economy. In the financial industry, they are an excellent tool for transaction processing, administrative work, and certain initial levels of customer service.

 

A critical mention goes to the automation of the customer onboarding process. This has found in RPA a space to grow and offer customers a fast, efficient operation with all the necessary security guarantees.

In the service sector, robotic automation of processes has been incorporated to create, correct, and send service orders, inventory control, and customer service in standard cases.

And so, the experience is being repeated in many other organizational world areas that daily seeks solutions to optimize its processes and improve its efficiency. In the short term, the combination of Artificial Intelligence and Machine Learning will add one more factor of innovation and technology that will make RPA a necessity in the future.

The transformation of the human factor and its role in the organization

An organization’s human factor remains its most valuable asset. Even when the digital transformation has imposed new challenges at the work level, employees are still at the forefront of the most critical projects, providing the spark and strength that only people committed to their dreams have.

 

Therefore, faced with the RPA, the question arises about its effect on staff: will they feel displaced, will they lose importance in the future, what does this new stage hold for them? Recent studies offer fascinating results on its impact on human capital.

In terms of personal satisfaction, employees feel more at ease with their new job assignments. The necessary reassignment of tasks entails placing them in activities where intellectual reasoning is essential and increases their interaction with customers. This favorably influences their mood and the way they perceive themselves within the organization. In the same way, the use of creativity increases notably.

 

There is indeed fear of job insecurity and lack of legal protection. Still, a well-structured RPA process in an organization has considered this situation first and foremost. It seeks to avoid the most significant possible damage to human capital.

 

Those responsible for the transformation are engaged in assessing a new organizational structure into which employees fit, with new roles and responsibilities in line with the change. It is an opportunity to grow and integrate them into the digital transformation that is in the environment.

Adi Goldstein

Hypernova Labs is your ally when it comes to taking the first step.

Sometimes change brings uncertainty but, in the hands of reliable people, it becomes an exciting adventure. Facing the decision to robotic process automation (RPA) in your company is a big one. And it requires the support and advice you need to act in an informed and appropriate manner. Hypernova Labs has the experience and expertise to provide you with the solutions you need depending on the nature of your business.

If you want to optimize your processes and make RPA an element of change in your organization, have a coffee with us, and let’s talk about change. You dream, and we make it happen.

Find out about machine learning and what can we “learn” about them

Machine Learning is part of our daily lives, but we don’t see it. Instead, we feel its presence through the infinite solutions that make our lives easier. They seem to know what we need before we ask for it. We interact with chatbots that have the answer to our questions. We chat with Siri or Alexa and even listen to their jokes. It is even possible to consult a virtual assistant about a potential medical diagnosis based on our symptoms. Machines learn from our behavior and prepare to meet our requirements, as simple as that.

Machine learning has made great strides in recent years and continues to advance steadily. It has become the ally of multiple research areas and has refined its ways of learning. A giant derivative of Artificial Intelligence that is here to stay for good.

What is Machine Learning?

The ability of machines to learn without being previously trained to do so is what is known as Machine Learning. They are designed to change their behavior and responses based on the data they are exposed to.

This learning process is based on algorithms that allow analyzing and comparing data to make predictions and establish behavioral patterns. This leads to the autonomous improvement of the system without human intervention.

Its origins date back to the birth of the science of statistics. The establishment of patterns based on data behavior is the key. Artificial Intelligence has been the field that has opened the doors as a branch of this fantastic technology.

 

For this reason, the beginnings of Machine Learning date back, like AI, to 1950 when Alan Turing wondered about the possibility of machines thinking as humans do.

A milestone that marks the development of machine learning can be found in creating the first artificial neural network system by mathematician Marvin Minsky. Named SNARC (Stochastic Neural Analog Reinforcement Calculator) is considered the first artificial self-learning machine capable of finding its way out of a maze.

Like AI, machine learning faced long periods in which its development was limited due to the lack of available data and computing limitations. But during the 20th century, it managed to take off thanks to the advent of the internet and its immense potential to offer gigantic volumes of data. At the same time, the development of technology in terms of processing capacity served it on a silver platter that is needed to make a great leap forward.

 

In 1997, IBM marked the history of Machine Learning by presenting its most recent creation: Deep Blue. This system was trained from thousands of successful chess games and managed to beat the world champion of the moment, Garry Kasparov. This success was possible thanks to what is known as deep learning. This learning modality is based on the fact that machines learn from experience and are also capable of educating themselves to improve their performance based on data.

From that moment on, this field has continued to grow in technology, learning, and sophistication and does not seem to stop.

Freerange

Different types of Machine Learning

We can classify machine learning into three classes according to the treatment of the data it handles for its development:

 

Reinforcement learning

It is known as learning based on a behavioral model. It responds to trial and error. The system learns its experience from the punishments or rewards it receives for its behavior. In this way, it defines the patterns that will lead it to succeed in similar situations in the future.

 

Supervised learning

In this type of learning, the characteristics of the data that is introduced change. The information is pre-tagged to build patterns based on the tags and identify new data in the future.

A typical example is the introduction of photos of animals, for instance, with their corresponding tags (cats, dogs, etc.).

The system will recognize them and, in the future, will be able to catalog images of animals based on their similarity to those previously labeled.

 

Unsupervised learning

In this case, learning goes a step further and no longer requires previous labels. Instead, the systems are trained to look for similarities in the data and catalog them accordingly. An example of this learning can be found in facial recognition systems, which do not look for a specific face but for those faces that share the most significant number of common characteristics.

Deep learning, mentioned above as a particular type of machine learning, deserves special mention. This learning system incorporates the technique of artificial neural networks and aims to simulate the behavior of the human brain. As a result, it is possible to make the computer deal with abstract concepts just as a human being would.

The present and its applications

Machine learning has opened the door to change and improvement of customer-oriented solutions and business in general. The possibility of learning from day to day and improving each system is an invaluable opportunity at this time. For this reason, it has been incorporated into almost all everyday spaces.

Its main applications can be found in the following areas:

 

Market research and development of predictive models

It allows a more accurate segmentation of the market based on user behavior patterns. It also facilitates the design of demand prediction models based on customer needs and behavior.

 

Optimization of customer service

Thanks to the analysis of the data obtained from the interaction of the systems with the customer, improving the customer service provided continuously. Offering better recommendations in response to customer requests and personalized options are just some improvements made possible by machine learning.

 

Improved quality control and fraud detection systems

Continuous process monitoring and data analysis allow machine learning-based systems to respond with greater accuracy and success in quality control and fraud detection. They can detect faults and irregularities in time and launch the necessary alerts.

 

Automation of processes in companies

Hand in hand with RPA (robotic process automation), machine learning becomes the perfect tool to automate repetitive processes within organizations, minimize human errors, and optimize results. These advances greatly favor areas such as human capital management and access control.

 

Optimization of production lines

One of the concerns of any manufacturing company is maintaining its production lines, both in terms of time and quality. Machine learning offers the possibility of reducing production costs by continuously improving processes. Moreover, it makes it possible to reduce or prevent future failures, an aspect that favors the competitiveness of companies.

Jack Moreh - Freerange

Hypernova Labs for the future

Our development teams prepare daily to build solutions adapted to the future and meet our customers’ needs. We stay on the cutting edge of technology and know that you expect that and much more from us.

Just as machine learning-based systems do, we learn from our experiences and grow stronger as an organization. So come to our offices and discover that the future is no longer a dream but a possible reality. We are waiting for you.

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