Artificial intelligence in business: we reveal the secrets behind some successful examples

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If you still believe that Artificial Intelligence is a thing of the future, it is time to take a closer look at the present, as several companies that are part of our routines — especially Internet giants — are already using Artificial Intelligence technologies in their strategies.

When you visit an e-commerce site, search on Google or hire a zalo database company's services, you may already be interacting with intelligent machines without realizing it, because robots are increasingly behaving more like humans.

The intention is to take advantage of the resources of the digital age to make the user experience increasingly more efficient, agile and relevant.

But, since we don't always notice Artificial Intelligence used on a daily basis, we prepared this article to show you how big companies use these technologies.

Here you will discover how AI is present in your life! and you will learn:

What is Artificial Intelligence?
Top Artificial Intelligence Technologies for Marketing
10 practical applications and examples of Artificial Intelligence in companies
Business Intelligence Case Study
The biggest challenges for applying AI in business
Conclusion
Are you ready? Keep reading!

What is Artificial Intelligence?
Artificial Intelligence is a concept in the field of technology that refers to the ability of machines to think in a very similar way to human beings; and we often refer to this type of technology with the acronym "AI" or "AI".

For its part, this capacity allows machines to understand human behavior, analyze the environment, reason, learn and make decisions autonomously, without human intervention.

To do this, they need to receive and analyse large volumes of data, which will expand their knowledge and make their actions smarter.

Furthermore, Artificial Intelligence is one of the main elements of the fourth industrial revolution, Industry 4.0, which is also marked by the Internet of Things ( IoT ), Big Data , Cloud Computing , among other concepts.

For this reason, companies are experiencing a digital transformation, which places technology at the center of their strategies and promotes the digitalization and automation of processes .

Artificial Intelligence has thus become essential to create smarter products and services and make organizations more competitive.

The strategies behind Artificial Intelligence range from recommendation systems for purchasing products to offering predictive prices for a service based on demand.

But don't worry. We'll see examples of practical applications later on.

Top Artificial Intelligence Technologies for Marketing
AI is much more than a technology , it also brings together algorithms , codes and data that can perform different functions.

Now, let’s better understand what are the main Artificial Intelligence technologies that can be used in business marketing, as they are often used together to develop better products, services, and strategies.

So let's look at some of them!

Machine Learning
Machine Learning stands for automatic learning. According to this concept, machines process large volumes of data and identify patterns that generate knowledge about user behavior .

This way, they can continuously learn and improve their decision-making , even without any human intervention.

Deep Learning
Deep Learning is a deep dive into machine learning . It is based on neural networks, which use more complex algorithms to approximate the functioning of neurons and the human brain.

Combined with machine learning—which works in a more linear manner—this concept improves the ability to process data and generate intelligence.

Natural Language Processing
Natural language processing (NLP) is the ability of machines to communicate with people in human language .

In other words, it is the area of ​​Artificial Intelligence that approaches linguistics to understand the expressions, idioms, jargon, syntactic rules, semantic relationships and everyday errors that make human language so complex, that is, an unstructured language.

Because computers use structured language, they need algorithms and systems to understand humans and give them answers using natural language.


Computer vision
It is the ability of machines to see like human beings . The intention is to imitate human vision, which can capture the light reflected from an object, identify the environment around it, analyze the information and store it in memory.

In computer vision , machines are also able to do this and can make intelligent decisions based on what they see.

10 practical applications and examples of Artificial Intelligence in companies
Now that you know the main Artificial Intelligence technologies used in marketing, let's now see how these can be applied in practice and how companies are taking advantage of the potential of Artificial Intelligence .

1. Recommendation of products and services
Spotify and Netflix are experts at personalized recommendations . Both platforms seek to understand users' behaviors and interests in order to make suggestions that they will actually enjoy; and of course, Artificial Intelligence is behind that.

Both Spotify and Netflix are powered by Big Data, and the vast volume of data — both internal and external to the platforms — is used to feed algorithms, which hone their knowledge and make better recommendations.

In this way, the huge catalog on the platforms becomes more interesting for users.

On Spotify, meanwhile, the highlight is the “Discoveries of the Week” playlist, whose personalized suggestion of 30 songs almost always pleases you, right?

These recommendations are based on a cross between three models:

Collaborative filtering models: These process data on user behavior in relation to other users of similar platforms.
Natural language processing models: These process data about what Internet users say about Spotify's catalog.
Audio Templates: Process raw audio files from the Spotify catalog.

Source: Harvard Business School
On the other hand, on Netflix, the personalized home page is the main way subscribers interact with the platform's recommendations.

Netflix's strategy is to recommend titles that are of interest, but also to encourage people to explore and browse the catalogue. To do this, the home page is organised into lines, classified by genres or subgenres of films and series.

The lines and order of the titles take into account the user's interests in relation to other similar users of the platform (collaborative filtering) and a series of rules.

In general, the most relevant titles tend to be closer to the bottom right corner, which tends to get more attention from users. But machine learning at Netflix goes even further!

The algorithms learn from interaction with the home page and understand how each user consumes its content, and can reorder titles to create a specific page for each user.


Source: Netflix TechBlog
2. Service automation through chatbots
Chatbots are one of the main examples of Artificial Intelligence for companies. For interactions between robots and customers to be relevant, machines must understand what people are talking about and provide them with answers and solutions .

It is important to mention that many companies are investing in this type of application to optimize customer service. Among them, banks stand out in the power of investment in technology, with Artificial Intelligence assistants that interact with customers , clarify doubts, report balances and carry out transactions.

Furthermore, the more users interact with the chatbot, the more it learns about them and even anticipates their needs.

And natural language processing is one of the core elements of virtual assistant AI.

3. Voice recognition
Amazon's Alexa and Apple's Siri aren't just virtual assistants that you can ask for the day's weather forecast.

Depending on the interactions you have, they can learn your interests and make the conversation much deeper, as both platforms are voice user interfaces (VUI), which use conversational AI technology.

This represents a major advancement in human-computer interaction. Instead of menus, clicks or taps, we use voice, which is the most natural way humans interact with the world.

For this reason, Artificial Intelligence has the task of understanding what people say in order to be able to talk to them and perform the tasks they want.

To do this, Amazon and Apple's systems rely on natural language processing, which not only understands what people say but also responds, interacts and increasingly learns.

However, VUIs go further: they understand not only what we say, but also how we say it , allowing us to capture the emotional nuances of a speech.

The number of Alexa skills, for example, is growing year after year. According to the website Voicebot.ai , there are about 5,000 new skills every 100 days, such as making payments at banks, ordering food delivery or requesting an Uber.

Alexa Skills Milestones
4. Image recognition
Are you also surprised when the Google Photos app recognises all your family members in the photos on your phone? Yes, Artificial Intelligence is behind this.

However, computers do not read images, as if you see a picture of a dog, for example, Google only sees codes. Therefore, they need to learn what the characteristics of a dog's photograph are in order to understand when they are there.

That's where computer vision comes in. This technology allows you to train your computer to recognize patterns of colors and shapes in images. This way, machines are closer to human vision and can make decisions based on what they see.

So the app doesn't just recognize photos of dogs, it also recognizes photos of your dog. It doesn't just recognize photos of people in general, it also recognizes photos of your family or friends. And the more users tell the bots who or what is in the pictures, the more they learn.

This way, Google Photos can organize and group the photos you save, so you can find them with a simple search.

And to give you a clearer idea, in this article Google explains how this technology works.


Source: Android Police

5. Product prices
Who hasn't been scared by the price of an Uber on a busy afternoon? Yes, Artificial Intelligence is also behind!

Dynamic pricing, based on demand and supply of a product, is another possibility for the practical application of machine learning.

For example, when a lot of people leave a football match, Uber fares go up.

At the same time, more drivers tend to come to the venue because the prices are better. But once the event is over, the rates return to normal, often cheaper than a taxi.

The same is true for Airbnb, which offers the Smart Pricing feature for hosts who want to adopt it. In this way, prices vary according to the demand for accommodations with similar characteristics to those of the host, as well as data such as location, season, accommodation rating, proximity to check-in, among other factors.

Okay, so dynamic pricing is nothing new – hotels and airlines have been using this strategy for years: as demand increases, the price goes up.

However, before AI, this dynamic depended on user-defined rules.

Machine learning, on the other hand, enables algorithms to recognize patterns that humans miss, predict future situations, and update prices in real time. In other words, pricing becomes dynamic, accurate, and fast.

AI-powered dynamic pricing takes into account current demand for a product and user behavior, as well as external data such as news, weather, local events, time, traffic, etc.

So if a show is advertised in a particular city, algorithms can capture this information and adjust prices instantly, which would be very difficult for a human to do.

6. Audience segmentation
Audience segmentation is one of the most traditional marketing activities, as companies orient their strategies around consumer behavior, to reach people with the right profile for their solution and with the right offers.

Artificial Intelligence can take advantage of this segmentation!

Netflix uses machine learning to understand the behavior of its subscribers and segment them based on their actions. The group of customers who watched the last episode of a certain series, for example, can receive an email with a recommendation of new content to watch.

However, segmentation can become much more precise and personalized as algorithms understand each user's profile.

They are able to identify behavioral patterns that humans cannot detect, as well as avoid prejudices, since it is the data that shows who the consumer segment of a type of content really is and feeds the algorithms to make better segmentation decisions.