Blog > Artificial Intelligence, Chatbots, Digital Transformation

All about Artificial Inteligence: impacts and opportunities

Featured Image

You don’t need to understand everything about artificial intelligence (AI) to agree that it’s no longer science-fiction but a part of everyday life for businesses and individuals.

Whilst this is not a new subject, AI solutions are constantly evolving, as are the market landscape and customer behaviour. In other words, you need to think carefully about the best solution for you.

The aim of this post is to answer some of the most common questions about this topic by giving an overview of artificial intelligence as a tool, thinking about its main applications and benefits for customer relations and about current trends. See more.

What are the challenges today?

AI is used in a wide range of channels, interactions, processes and services, from the simplest – such as sending information via SMS to shoppers in a store who are close to the point of sale – to automated industrial networks managed by intelligent robots and their complex performance analyses.

When well used, AI makes strategic planning more efficient and generates a whole host of positive effects on the quality of services, the optimisation of costs and employee and, of course, customer experience.

But if it is not well-suited to the company’s needs and, even more so, to the needs of its audience, it can also negatively affect relationships and operating results. After all, a lack of the personal touch doesn’t exactly attract buyers, does it?

Also, is it better to buy an AI solution that relates directly to the customer or one that optimises company’s production processes?

So, as you can see, although it can be very beneficial for companies, it still needs some careful strategic planning before taking the plunge.

Read on to discover everything you need to know about artificial intelligence!

What is artificial intelligence (AI)?

Artificial intelligence is the planning of and development of computer systems capable of performing tasks that require the use of human reasoning, such as visual analysis, voice recognition, etc.

AI solutions can learn, plan, analyse variables, propose solutions to problems, promote interactions, execute procedures, such as credit card transactions, or make personalised offers according to the customer’s profile.

Automation and data analytics are a crucial part of its design. AI allows you to solve problems or run processes in a smarter, faster and more efficient way.

Chatbots are a classic example. They bring a strategic element to the omni-channel nature of the business, interact with customers based on rules, learn from their experiences and, thus, are able to carry out a much larger number of interactions in an agile and efficient manner, since they reduce common human errors in customer service.

Automated factory floor plans can also be very useful. In addition to the machines that assemble cars and equipment, for example, Industry 4.0 is all connected by artificial intelligence solutions that analyse operational performance – are machines under performing? – and even propose unscheduled maintenance to ensure the best results. All this without human interference.

But how do we get to the point of fully trusting an industrial operation to AI robots? After a lot more development, naturally.

How did artificial intelligence come about?

The history of artificial intelligence is older than it seems, and if we think that the codification of language and rules is its basis, we can make a link even with ancient civilisations, with communications that determined which guidelines should be followed.

Whilst wondering what impact it would have on people’s lives, philosophers and ancient thinkers already thought about machines created by men that would be capable of repeating and executing commands.

But the story of their development and society’s perceptions of robots is even more curious and has even – often – made its way onto the silver screen.

A historical list of first mentions of robotic inventions that would have AI in their designs – or at least some element of it – would give us:

  • 1921, Rossum’s Universal Robot by Karel Capek was built. It is the first known record in which a machine was called a robot;
  • 1927, the movie Metropolis was released, where a humanoid robot attacks a futuristic Berlin. It’s an interesting milestone because it popularises the discussion of robots in people’s daily lives, even if they are portrayed as enemies or uncontrollable and dangerous;
  • 1929, Gakutensoku was created, a Japanese robot developed by biologist Makoto Nishimura. His idea was that the robot would learn from the natural laws of humanity, and his frame was able to move its head and arms, and even change expressions;
  • 1939, the Atanasoff-Berry Computer (ABC) was developed by inventor Atanasoff and his assistant Clifford Berry. It was able to solve 29 linear equations simultaneously, though it weighed over 315 pounds;
  • 1949, scientist Edmund Berkeley wrote a scientific essay noting the incredible advances in computers and their processing capabilities. In his conclusions, he finished off by saying that “a machine can indeed think then”;
  • 1950 was the year of the publication of what was later called the Turing Test. In a scientific study on the possibility of machines having the ability to think, Alan Turing created the Imitation Game, which was used as a reference for analysing the intelligence of a robot;
  • 1965 was when the computer world met Eliza, a robot capable of interacting with a person. We could say that it was the predecessor to both chat bots and virtual assistants;
  • 1968 saw the development of SHRDLU, the first programme with natural language;
  • 1977, the robots C-3PO and R2-D2 hit the silver screen with the release of Star Wars. The first, a humanoid, had the ability to communicate fluently in 7 million intergalactic languages, while the other friendly robot interacted with “blips” and was a great spaceship pilot;
  • The 80s are considered the winter of artificial intelligence, as the government of major countries and their markets reduced investments and demands in the sector.
  • In 1995, another predecessor of today’s chat bots came onto the circuit, only now with natural language understanding in its programming. It was called A.L.I.C.E. (Artificial Linguistic Internet Computer Entity);
  • In 1998 and 1999 robots entered the toy market with Furby and AIBO, a robotic dog that understood and responded to over 100 commands;
  • In 2000, the world faced the millennium problem. Most computers invented up to that time only needed to change the last three digits of the dates, and at the turn of 1999 and 2000, there was a risk of the millennium bug;
  • 2004, NASA’s unmanned robots explored the surface of Mars for the first time;
  • 2010, visual recognition solutions emerged;
  • 2011, Apple launched Siri, its intelligent virtual assistant capable of deduction, responding to and suggesting products, services, locations and other information that is relevant to the user;
  • 2012, Google researchers trained a neural network of 16,000 processors to recognise images of cats, without any written data or information describing them;
  • 2014 was the launch year for two other well-known virtual assistants, Microsoft’s Cortana and Amazon’s Alexa;
  • 2016, the humanoid robot known as Sophia is considered a robotic citizen. She can see through visual recognition, make facial expressions based on her reactions and also communicate through AI;
  • 2017 was the year in which Facebook advanced its solutions and tests for the use of chat bots on its platforms;
  • 2018, Google brought to the market BERT, the first artificial intelligence robot with two-way, unsupervised language representation that recognises natural language, learns from its experiences, and can perform a range of tasks based on commands.

Looking at this time line, we see that the elements of automation and complex data analytics evolve together.

We can also see that the mechanisms have, in fact, become smaller, more agile and efficient because of the strong influence of digital transformation.

And if, before, they were designed to repeat human actions, today they bring complex solutions that would be impossible to be calculated by market professionals with the same efficiency and speed.

But that doesn’t mean robots have become smarter than humans. Besides continuing to be developed by humans, the fact is that they can perform repetitive activities for long periods, obeying rules with more accuracy, etc.

They, therefore, do not replace the professional workforce, they just shift it to more strategic functions of their design and monitoring, don’t they?

Why is it important these days?

To travel through this time line and think about how the cinema represented some of the fears and expectations of people in relation to robots is very interesting, because, nowadays, we already enjoy their benefits without even thinking about it.

But the impact of artificial intelligence on business and everyday life is more and more evident, shifting the economy and the way knowledge is transmitted and even raising a discussion about some of the ethical aspects about using people’s data.

They are able to analyse a larger set of data through complex, progressive learning algorithms – all with incredible accuracy. Accurate information is the key to successful actions and strategies.

There are several applications, all aiming at efficiency, agility and better experiences for their clients and users, but they must respect, for example, people’s privacy.

The caveats, therefore, should be around choosing a company that can develop rich and engaging AI solutions, but at the same time in an ethical and respectful manner.

What is AI made of? What makes it up?

Labelling automation, data analytics, storage, IoT or any other solutions as artificial intelligence is not exactly wrong, just unspecific considering their complexity and applications. Therefore, it is important to understand what AI is made of.

Machine learning

“A machine can indeed think then.” That’s what Edmund Berkeley said in 1949, and he couldn’t be more correct. Machine learning is about using as little programming as possible to enable a computer to identify patterns in a plethora of data.

With this functionality, however, machines are not restricted to basic analysis, but instead network to connect, compare, and associate with other pattern studies identified in Big Data. And guess what?

Their algorithms and analytics get better and better and more complex over time as they learn from their processes and new data.

It’s simple to understand when you consider a chatbot programmed to provide buying suggestions in an online store based on the customer’s profile. The more pieces, products, advertisements and other content presented to the visitor, the more reactions they will produce for the robot’s database.

So, whether customers react positively or negatively, the chat bot will incorporate such information and refine its buying suggestions according to the wealth of data it has. That is, it is learning and refining his actions and interactions.

From the customer’s perspective, the experience of having offers personalised and tailored to their needs and preferences is much more engaging and efficient.

Deep learning

Deep learning continues to follow the maxim of replicating human analysis functions, and here it is interesting to see how the ability of people is still a benchmark for even the most complex robots.

In this mode of AI, robots elevate their analysis and comparisons, using deeper relationships between data from different focuses and variable analysis, such as when a person is driving a car.

Interesting, isn’t it? But to drive, we need to know the traffic rules, the mechanisms of the car, its particularities and read the elements that make it go – such as fuel and oil – in addition to local traffic, weather conditions, etc.

This doesn’t seem complex for those who have already mastered driving, but for those who are learning, or even the AI robot, it’s quite different. So, as it is easy to assume, deep learning is used in autonomous vehicle projects, but also in other particular situations, such as fraud and security and data analysis.

Natural Language Processing (NLP)

NLP meets the need to humanise artificial intelligence solutions, after all, in addition to efficiency, it is also important to ensure the quality of managed services.

In this case, the technology is developed to recognise people’s natural language when it comes to accepting a command and even reacting to it. It’s much more pleasant to talk to someone or something that demonstrates human reactions, right?

In addition, there is a basic rule of communication: the message transmitted must be the same as the message received.

In other words, if the idea is to offer a voice-automated service, the technology becomes the receiver of the communication, and not just the mechanism that will process a request. It needs to understand what is being said.

Big data

Machine learning and deep learning have shown that their performance is increasingly better with the amount of data provided to them for processing. In this sense, big data is the term that describes the large volume of data stored by the business through different sources.

Data generated in a company’s call centre is stored in big data and can be used by a chat bot for rule learning, language and understanding customer reactions.

They can also integrate analysis of consumer behaviour and trends, using sales reports, information from the financial sector and market information when used by business intelligence.

What impacts has AI brought to the world?

Its applications are diverse, in addition to the areas under development and increased access to its technologies. So what are the impacts of artificial intelligence on business and society?

There’s a lot of talk about shrinking workplaces, and there are questions about whether digital transformation in customer service will lead to the estrangement of relationships. But is that the way to go? Absolutely not.

What are the applications of artificial intelligence in the market?

Among its applications in the market, we can list:

analysis of companies’ big data, creating rich reports on trends in customer behaviour and consumption and companies’ operational performance;

automation of customer service, with increased capacity for interaction, a variety of channels and platforms such as WhatsApp, use of customer data to offer personalised solutions and information, etc;

The multidisciplinary nature of chatbots, which are based on artificial intelligence and have several applications, such as consultative and personalised sales in online selling, product offers for segmented customer lists, social network relationships and payment transactions;

improvements in distance learning processes, with games and other pedagogical resources transformed into interactive videos, simulation environments, etc;

sentinel for program data security, detecting intruders on a corporation’s networks and initiating protocols that preserve data and eliminate the threat.

What are the applications of artificial intelligence in everyday life?

As for direct relations with users, the most significant applications are:

communication from wearables such as smartwatches and other connected devices through the internet of things, ensuring that they not only exchange information with each other, but also obey commands;

guides and maps that indicate best routes according to traffic or detours, as well as tips on driving in different weather conditions and road sections;

customer care services through social networks or chats on the companies’ websites and online catalogues, which is worth mentioning again from the customers’ perspective, as they facilitate relationships with brands;

security cameras, facial recognition, online porters in residential condominiums etc.;

de-banking and finance solutions, which provide analysis on investments and services such as cash back;

simultaneous translations from several languages;

applications in medicine in increasing the accuracy of test results and monitoring hospitalised patients.

What to expect from AI in the future?

The evolution of artificial intelligence in the history of humanity is curious, being dependant both on the scientific aspect of its mechanisms and languages and on the perceptions of people and professionals who are directly impacted by it.

This becomes even clearer when we realise that companies are investing in AI solutions first to deliver more valuable experiences, and then to bring more efficiency to their operations.

This is because a business’s relationship with its customers continues to be the key to its success.

With this idea in mind, we can list some AI developments that will be the most popular, either in the business world or in the daily lives of its users. Among them are:

Chatbots aligned with virtual assistants

Chatbots bring interesting applications for customer relationship, offering efficiency, agility and personalisation to customer services.

Virtual assistants, in turn, are tools increasingly used by users, and this is evident when we also cite the internet of things.

They allow the automated management of various stages and activities of the life of users, from sending the list of products to be purchased at the supermarket to the daily programming of the air conditioning in a house.

The proposal to unite chatbots and virtual assistants, therefore, brings even more efficiency and optimisation of the consumer experience.

Autonomous vehicles and other smart solutions

Self-driving vehicles are not just the vehicles for individuals that we constantly see in the media. Also included in this category are those that transport materials, supplies and even employees around the factory.

Thus, trains, cars and other computer-driven vehicles will be increasingly used by companies to improve their internal processes and reduce the costs and risks of human drivers.

Artificial Superintelligence (ASI)

A more pessimistic view on the evolution of artificial intelligence will also gain momentum in the coming years. In it, the concern is that AI will exceed human potential to manage it. This concern is strengthened by critics of meta-learning, which basically means where AI robots develop other such technologies.

We can also say that discussions about the use of artificial intelligence will gain more political and ethical contours. Just as we have to overcome the barrier of robots in our imagination to be able to accept them in our daily lives, we also need to understand that they don’t eliminate job openings or the like.

Artificial intelligence in business, however, is not going backwards. On the contrary, its applications will be increasingly beneficial for operational management, partnerships between channels, platforms, languages, services and, of course, for competitive differential and customer experience. It is, therefore, really important to keep up to date with developments.

A final tip for those who want to know everything about artificial intelligence: it is a fundamental part of our projects, so we will always bring you news, success stories and curious facts.