With the arrival of technology, companies and businesses from all commercial sectors have evolved their internal and external processes because it has constantly changed market scenarios.

In the latest years, digitization has played a fundamental role in this evolution. Due to this companies have begun their digital transformation processes after understanding the benefits that the internet and virtuality have brought with them.

Among the trends that have marked and will continue to mark this era, there are technological advances such as Cloud Computing, Robotics, Immersive Virtual Reality, besides Artificial Intelligence and Machine Learning; the latter catapulted by data analysis and today are vital for business foresight.

According to MIT, today 50% of United States organizations use these technologies to make their customer's data analysis easier and obtain more insights.
46% are interested in the competitive advantages that Machine Learning provides, 45% say they want to boost the speed of obtaining data thanks to these tools and, 44% want to promote R&D to offer innovative products and services.

Today we teach you about these two technological trends that revolutionize the corporate present and, we show you how you can implement their solutions in your company:

Artificial Intelligence

What is artificial intelligence?

It is about technological innovation through which systems or machines try to imitate human intelligence to perform tasks mechanically. In addition, they can iteratively improve based on the information they collect in each process.

How does it work, and what is it used for?

This technology works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to automatically learning patterns or characteristics in the data through devices and systems for optimization processes.

Innovation on trend!

AI is a booming technology, and it is increasingly present in company budgets. The investment in its solutions ranges between USD 500.000 and USD 5 million annually, according to the note from the technology media Muy Canal, which analyzed the report published by the company Appen Limited on the state of AI and machine learning in 2021.

This study found that companies with AI budgets of $ 1 million or more had a return on investment (ROI) of 100%. The report also found that those companies that spend less than USD 500.000 on AI projects are less likely to be considered market leaders compared to those companies that spend more.

How and where to apply Artificial Intelligence in a company?

There are many areas of a company that can benefit from AI. Especially those of marketing, sales, logistics, finance, and human resources:

• Custom experience designing: Create more optimized advertising campaigns and save costs by doing so. In addition, consumers will have experiences increasingly tailored to their needs and profiles through solutions such as chatbots.
  
• CRM Platforms (Customer Relationship Management): In sales, the solutions are immersed in these essential tools for businesses. Because through them, companies manage the communication with their customers. Emails, calls, chats, social networks, all integrated into a single service.
  These CRM platforms incorporate Artificial Intelligence when they store contact information and all customer interactions. Also, through them, people in charge of the commercial area can create automatic and personalized responses to interact effectively with more potential customers.

• Administrative automation: In the financial field, AI has contributed through the analysis and projections of a company's financial statements. Besides the calculation and allocation of costs, sales prediction, and risk analysis.

• Applicant tracking systems: In the human resources area, AI technology has helped the teams in charge of hiring, through systems that search for CVs online and contact applicants to schedule their interviews. A positive point of these solutions is that they help reduce bias in recruitment processes.

• Monetary fraud detection: It can easily detect which transactions are legitimate and which are not by assigning a pattern to money movements on digital platforms.

• Preventive actions: It rules out on its own the riskiest actions and those that may affect the development of business services, preventing them from being carried out.

Machine Learning

What is Machine Learning?

This discipline allows technological devices to carry out tasks autonomously without must be programmed, through predictions based on patterns stored in their memory in a process that resembles “learning”.

How does it work, and what is made for?

It works through algorithms in computers that are endowed with the ability to identify patterns from massive data analysis to make their predictions.

The term was used for the first time in 1959. However, it has gained relevance in recent years due to technological development and the Big Data boom, which refers to the voluminous amount of structured, semi-structured, and unstructured data that has the potential to be extracted for information.

But, what is the difference between Artificial Intelligence and Machine Learning?

You may be confused at this point about the resemblance between AI and Machine Learning, so it's time to mention the difference between the two concepts:

The difference between Artificial Intelligence and Machine Learning is that AI is a device capable of imitating human reasoning, while Machine Learning is a subset of Artificial Intelligence, through which people 'train' these devices to recognize patterns based on data and make predictions.

Matter of algorithms!

Machine Learning algorithms are divided into three categories, the first two being the most common:

• Supervised learning: These algorithms have prior learning based on a labels system associated with data, which allows them to make decisions or make predictions. Ex: A spam detector that labels an email depending on the patterns it has learned from the email history (sender, text/image relationship, keywords in the subject, etc.).
  
• Unsupervised learning: These are characterized by not having prior knowledge to foresee a scenario. They face the chaos of data to find patterns that allow them to organize them in some way. For example, in marketing, they are used to create segmented advertising campaigns based on data extracted from social networks.
  
• Reinforcement learning: They are the most complex and aim to create and learn an algorithm from their own experience. Currently, are evidenced in facial recognition when making medical diagnoses or classifying DNA sequences.
  

How and where to apply Machine Learning solutions in a company?

• Recommendations: It is used to make personalized purchase suggestions to users of an online platform. It analyzes your purchase histories and compares them with what other users have searched according to trends or similar tastes, which improves customer service and helps these consumers retain their loyalty.

• Process Automation: The possibility of automating mechanical tasks is one of the great benefits of this innovation. Thanks to Machine Learning, the devices know what processes to carry out, and it will refine them to expand the number of tasks to carry out. An example is digital payment processes.

• Decrease in errors: It helps management systems correct mistakes made and ensures that they won't repeat. The longer integration time a system has, the more robust it will be.

• Natural Language Processing Systems (NLP): This system is immersed in devices that work understanding human languages, such as virtual assistants such as Alexa or Siri. They can instantly translate from one language to another, recognize the user's voice, and even analyze their feelings. NPLs are also used for complex tasks such as translating contract legal jargon into plain language or helping lawyers sort through large volumes of valuable information. They are a great innovation alternative for companies around the world.

• Search: Search engines use machine learning to optimize their results based on their effectiveness and relevance. It is possible measuring them through user clicks.

• Greater cybersecurity: the new antivirus and malware detection engines and the use of machine learning to enhance the scanning, detection, and improvement of the ability to recognize anomalies, failures, or risks of cyberattacks.

Takeaways:

Over the years, companies and businesses have had to evolve and optimize their internal and external processes. This is because technology has constantly changed market scenarios.

Digitization has played a fundamental role in the most recent transformation that the business paradigm has had. This is due to companies have shown the benefits of using the internet and virtuality, thus deciding to start their digital transformation process.

Among the trends that have marked and will continue to mark this new era are Cloud Computing, Robotics, Immersive Virtual Reality, besides Artificial Intelligence and Machine Learning; the latter catapulted by data analysis and which today are vital in business foresight.

Although both innovations are similar, and sometimes hard to identify, the main difference between Artificial Intelligence and Machine Learning is that AI is a device capable of imitating human reasoning, while Machine Learning is a subset of Artificial Intelligence. In the latter, people 'train' the devices to recognize patterns based on data and make predictions.

It is valid to clarify that both solutions have turned out to be very profitable for companies to the extent that they can be implemented in different company areas, especially marketing, sales, logistics, finance, and human resources.

The most revealing thing here is that as the world market continues to optimize its internal and external processes with technological trends, we will continue to see substantial changes over time. Innovation never ends.