Competitive Advantage

In this sense, part of the large companies already included Artificial Intelligence in their race for innovation. Despite this, and according to the latest annual report from the Boston Consulting Group, few companies have harnessed the full potential of AI.

Taking advantage of all the advantages it offers will make a significant difference compared to the rest of the competitors in your sector. Following the BCG report data, nine out of ten executives agree that AI is a business opportunity for companies. It is the technological revolution of the moment. Investing in a system that promises to be the future is a long-term investment that is likely to bring numerous benefits to your business over time.

The Best Services

There are many production processes and services that, thanks to AI, make their quality much better. An example of this can be seen thanks to machine learning. In e-commerce or VOD platforms, they constantly use it when recommending their products. Machine learning improves bid to reach your customers and is one of the most beneficial online shops. The products in your catalogue will automatically adjust to the consumption patterns of your customers.

Optimizing Your Business

If we continue with the previous example, in addition to helping to improve customer-facing services, Artificial Intelligence optimizes many processes. Knowing perfectly what your customers demand will help you obtain a global vision of their consumption patterns, which you can geographically segment and establish areas of interest. In this way, each commercial action you implement will be much more effective, thanks to AI. You will get more data on what the public wants, and this, today, is the best route to achieve success as an entrepreneur.

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For the past couple of years, digital transformation or digitalization has been quite the buzzword among businesses and organizations. However, digitization in the most real sense transcends a mere technological up-gradation and entails a reorganization of the entire company, its workforce, and its day-to-day operations. 

The process of digital transformation has been fuelled with advancements in Artificial Intelligence (AI) techniques like Machine Learning, and thus, demands for machine learning online courses have skyrocketed. A recent survey conducted by Infosys has shown that organizations that use AI-enabled activities to drive digital transformation have generated substantial profits. Machine learning, specifically, has had the maximum impact in spurring the digital revolution by making the business processes more efficient, saving time and effort, boosting productivity, improving customer experience, reducing costs, driving innovation, enriching the staff’s knowledge and skillset and simplifying daily organizational work.

Data Mining and Machine Learning have revolutionized the digital space and popularized the machine learning online course. Let’s see why.

What is Data Mining?

Data Mining is the process of investigation and analysis of large sets of data to find meaningful correlations, patterns, and anomalies for the prediction of future trends and outcomes. In the last ten years or so, advancements in speed and processing power have replaced manual, time-consuming, and tedious data analysis practices to move on to automated, easy, and quick methods of analyzing data. From bankers and manufacturers to insurers and telecom providers, data mining is used by everyone to understand the relationships among demographics, pricing, and promotions and how various economic and social factors affect business operations, revenue generation, and customer relationships. Data mining enables:

• Screening and filtering the noise and redundancy in the data
• Understanding which data is crucial and relevant for the assessment of likely future outcomes
• Accelerating the rate of making quick and well-informed business decisions

The process of data mining involves the following steps:

1. Understanding the business and its needs
2. Understanding the data collected
3. Preparation of relevant data to be mined
4. Modeling the data using mathematical tools and models
5. Evaluation of the findings of data analysis and their relevance across the business or organization
6. Deployment of the conclusions of the data mining procedure to daily operations

What is Machine Learning?

As business landscapes are undergoing a rapid digital transformation, machine learning online courses and certifications are gaining quite the hype. Machine learning is the method by which a computer can be taught to make precise predictions when fed with data. Say, a system trained on machine learning models has to determine whether a flower in a photo is a rose or a tulip.

The system would conveniently discriminate between the flowers based on the large number of images it has been trained on and labeled as containing a rose or a tulip. The difference between traditional computer software and machine learning is that in the former, a human developer writes the codes instructing the system to differentiate between the given objects, and in the latter, the system is trained to detect the differences based on data observations and instructions automatically.

Classification of machine learning algorithms: 

• Supervised learning: In supervised learning, the algorithm is exposed to data consisting of labeled examples so that it can reliably single out specific data types based on any given input. 
• Unsupervised learning: In this learning method, the algorithm learns to identify patterns in unlabelled data and sorts it based on similarities or anomalies.
• Semi-supervised learning: This technique utilizes labeled data for partial training of a machine learning model and subsequently uses the latter for labeling unlabelled data.
• Reinforcement learning: This learning technique is based on the system’s interaction with the environment and taking appropriate action to reward maximization in a particular situation.

Data Mining Vs Machine Learning

Very often, people get confused between the concepts of machine learning and data mining for apparent reasons:

• Both involve data analysis
• Pattern recognition is a characteristic of both the processes
• Both entail decision making based on data-driven insights
• Both data mining and machine  learning require vast amounts of accurate data

So, before you sign up for a machine learning online course, you must understand that even though data mining and machine learning are similar on many levels, the two concepts differ significantly from each other. Let us discuss some of the aspects that set apart data mining from machine learning:

• While data mining means the extraction of knowledge from an extensive repertoire of data, machine learning entails building algorithms based on data as well as previous experience.
• Data mining methods look for patterns already existing in the data. But, machine learning goes beyond the scope of past events to predict future likelihoods based on pre-existing data.
• At the start of data mining processes, the patterns and rules are mostly unknown. But with machine learning, the machine understands and learns the data based on the standards and variables given to it.
• Traditional databases consisting of unstructured data call for data mining operations. However, machine learning owes its origin from algorithms as well as existing data.
• Data mining relies on manual human interference and decision making. But once self-automated, machine learning models can run without human maneuver.
• Data mining techniques are used for building machine learning models. Whereas, machine learning algorithms are implemented in neural networks, decision trees, and other areas of AI.
• Data mining is most frequently used in research fields such as text mining, web mining, recognizing sales patterns, and fraud detection. Machine learning finds applications in cases such as making product recommendations and estimating delivery time. 
• Data collection and human efficiency influence the accuracy of data mining procedures. Machine learning algorithms are more accurate than data mining methods.

Conclusion

Be it data mining or machine learning, data remains the most crucial factor. While data mining looks for trends and patterns in the existing data to project future outcomes, machine learning enables the quick and automated processing of data to generate results in the form of models. As global trends shift towards increased digitalization, these technologies have the potential for immense growth.

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With increasing competitors and a saturated market, the traditional ways of marketing and selling your products might not just cut it anymore – not to mention the long hours plus energy you spend on perfecting your campaigns.

However, by integrating A.I. into your ecommerce business, you’ll be in a better position to dominate your industry.

You can spend fewer hours auditing your content, doing menial tasks, and it can even help you provide better experiences for your customers. 

This allows you to improve your business operations while delivering better consumer services and engagement at the same time.   

To help you take that leap into the realm of A.I., here are four tips on how you can use artificial intelligence to grow your business and take your ecommerce store to the greater heights.

Increase Your Conversions with Autocomplete Search Plugins

One of the best uses for artificial intelligence is for increasing your conversions.

Using machine learning, a subset of AI, you can provide simple, memorable, and fast shopping experiences by helping your customers find products easily. An excellent way of doing this is by using autocomplete search plugins and features.

For example, if your visitors want to search for “smartwatch” on your ecommerce website, the autocomplete feature will give them the suggested keywords within seconds just by typing in the first three letters. Big companies like Amazon use this feature.

With autocomplete, your website visitors can be exposed to truckloads of related products that they hadn’t initially thought about, leading them to stay on your site longer.

Of course, the longer they stay on your ecommerce site, the more chances you’ll have of converting them from visitors into buyers. Autocomplete search features are also great when subtly promoting your other products.

Use Predictive Marketing Technology

There are countless ways to create an effective marketing Operations strategy, but one thing you shouldn’t miss out on is using A.I.to optimize your marketing efforts.

Wouldn’t it be amazing to know what your customers will buy next and the most probable time they will buy again? With machine learning used for predictive marketing, it’s possible.

Machine learning tools can analyze customer data to help you come up with targeted marketing campaigns that can optimize your ecommerce store. It allows your customers to see what, when, and how they want to see your products.

By using predictive platforms like Intempt, you can create customer segments and set properties that will help you predict the corresponding conditions for specific customer actions.

For instance, you can set “Returning visitor is predicted not to go to checkout,” and the predictive feature will show you the stats on the number of visitors, orders, and revenue. 

Manually going through that kind of customer data might take you years, but by using machine learning and AI functions, you can do this faster and easier. 

Plus, through new technologies, like using AI for customer service, you can engage your consumers better. 

Keep in mind that these calculations and analysis are based on data you feed the system – meaning the more data you can provide from your ecommerce website, email, social media, and other customer touchpoints, the better the predictions you’ll have.

Improve Customer Experience with A.I. and Chatbots

What can A.I. do that human employees can’t?

They never sleep, never get tired, and can perform tasks faster and more efficiently (to some extent), allowing you more time to work on other aspects of running your business.

A.I. and chatbots are excellent tools to help you deal with customer inquiries and concerns more efficiently, plus provide a better user experience.

Aside from providing timely answers to customer requests, chatbots are great channels to offer discounts and gather your customers’ preferences and needs.

Let’s say you’re running a dropshipping ecommerce business and one of the most frequently asked questions to your chatbot are gift recommendations for special occasions.

You can answer that need by publishing a blog post about the best products that your customers can buy from your shop to give during special events.

By leading your potential customers (with the same questions/needs) to your blog post, you are addressing their concerns – which can lead them to buy your products.

This is just one of the ways you can use chatbots to improve your customers’ experience while promoting your products at the same time.

The best part is, there are several extensive marketing guides that you can study to improve your ecommerce store’s performance.

When you pair the exhaustive marketing strategies that you’ll learn from the guide with your chatbots, and ensure that you’re using a reliable ecommerce platform, you are bound to see great results out of your ecommerce business.

You can also use chatbots to provide personalized product catalogs, remind customers of your new products or their abandoned carts, recommendations, and more.  

By integrating A.I. and chatbots into your ecommerce store, your customers can get answers to their inquiries quickly, have a personalized interaction, and a seamless customer experience.

Optimize for Voice Search

People use voice search mainly because it’s more convenient to use compared to typing.

When you’re busy making dinner or washing the dishes, with voice search, it can be more convenient to speak to your virtual assistant like Alexa or Siri than type it in.

A good strategy to help you take advantage of this artificial intelligence function for your ecommerce store is by implementing voice search SEO.

However, how people phrase their keywords when doing voice search can be different to how they do it when they type it in using desktop or mobile devices.

With voice search, your potential customers might prefer to use questions because it can feel like they are having a conversation with their virtual assistants.

For example, when looking for places to eat like “Best restaurants in Palm Springs,” users may prefer to say “Which restaurants in Palm Springs are open now?”

You can use tools like AnswerThePublic to get insights on the kind of questions that people are asking on the internet.

Using “Solar panels” as keywords, here are some of the question phrases that the tool came up with.

By using the tool, you can get ideas on the kind of questions that people are using to search for things, and find which ones you can optimize for voice search.

Final Thoughts

Artificial intelligence can be used for a lot of ecommerce functions and these are just some strategies you can employ to take your store to the future.

The key is to determine which AI tools and functions you can leverage to successfully market and sell your products, streamline your business processes, and get ahead of your competitors.

If you found this information helpful, share it with your network. Cheers!

Also Read : 7 Ways To Know Your Customers Better

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And it is that Artificial Intelligence and machine learning-based systems make us optimistic. They will be able, very soon, to replace us for repetitive tasks that involve interaction with humans, and that is precisely one of the best news for sectors such as e-commerce and, especially, their customer service. Let’s see why.

Automating Repetitive Tasks in Customer Service

The qualified human staff that attends the queries and requests in the customer service centers has to perform very repetitive tasks throughout the day that, in reality, consume time that could be devoted to more complex and high-level tasks. That is, they could invest their time in more appropriate tasks for a human brain.

Every day, several requests are met that are virtually the same: they require the same answers, the same actions and do not need a singular effort. For example, refunds, cancellations, updates … Such tasks could be automated by a machine-based system and, in turn, allow people to focus on other aspects.

Because, deep down, we have to keep in mind that the reality that “conversations” (real and real conversations with customers by email, chatbots, phone calls, etc.) is barely accounted for is only 30%. of the average customer service experience. The other 70% consists of actions and processes that customer service agents must perform manually each time a customer requests a refund or the status of an order, to give two examples.

That is why machine-based systems focus on tasks that, a priori, seem so simple to us.

A Customer Service Center is Ideal For Training Smart Systems

Contact centers or customer service centers are ideal for training systems based on machine learning. They have extensive data records and past queries that form a perfect training data set for the predictive capabilities that machine learning provides.

Upon completion of this training, machine learning models are able to understand the questions that are asked, even if their construction is different from what they already know. These models determine the client’s objective based on the conversation, among other things, and are able to pass the information to the professional, who scores it, and if it is correct, said response is sent to the client. In this way, the attention times are cut and excellent customer experience is offered.

The future is already present, and today we have customer services that have agents based on Machine learning that, in a transparent way for the client, carry out the first phases of the process. After that, they pass the information to the professionals in charge of the back-end processes that will solve the “tickets” and close the case.

Customers come to the customer service center because they need to solve very specific problems in the shortest possible time. Therefore, machine learning models are transforming customer service, since they either immediately resolve the most obvious issues, or accelerate the process for the customer, which will be more satisfied.

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1) Artificial Intelligence

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new contributions and perform tasks as humans do. Most of the examples of artificial intelligence that you hear today – from computers that play chess to cars that drive themselves – are mostly based on deep learning and natural language processing.
The challenge of AI is to make machines capable of guessing, on a scale and with a level of precision that we as humans cannot reach. Machines “learn” while processing specially designed and collected data for training. Artificial intelligence is based on the probabilistic method, according to which machines are oriented towards what they perceive as the most probable reality. And it is always changing, By processing more and more data, machines continually adjust their reality model, intuiting what they are “seeing,” “hearing,” or “interpreting.” An intelligent assistant – like Alexa on Amazon Echo, Apple new Siri and Sony’s Xperia Agent – listens to what you say, analyzes how to interpret it, and finally offers you an answer. Will it be adequate? Maybe not, but Alexa learns: if he makes a mistake, he adjusts and improves.

2) Blockchain Technology

The technology that underlies Bitcoin and similar cryptocurrencies – often referred to as blockchain or distributed ledger technology (DLT) – has attracted considerable attention in a wide range of industries and investors. From government to retail, through financial services, innumerable use cases are proposed to boost them.
Many start-ups, technology companies, consulting companies, and communications service providers are promoting products and services to sell Blockchains and associated technologies.

The birth of potentially disruptive Blockchain technology is occurring in a market in which companies are influenced by a series of existing technologies, based on the 3rd platform ( mobile, social, cloud and Big Data/analytics). IDC believes that the technology we currently refer to as “Blockchain” will be totally disruptive in the industry, generating new use cases, products and business lines.
Blockchain technology, in contrast to AI, focuses on validation, permanence and achieving higher levels of certainty and control. Like solving a block by block puzzle, a chain of transactions is encrypted and saved so that it cannot be altered. While artificial intelligence leads to the discovery of opportunities and risk assessment, Blockchain technology takes action at the time of executing the plan. In that sense, smart contracts and Blockchain must be, above all, deterministic. Not probabilistic.

Blockchain technology is, like Detective Joe Friday – from the Dragnet series – the pragmatic member of the team: “Only the facts matter.” Blockchain validates all identities and transactions, automatically, throughout the entire chain.

Both artificial intelligence Blockchain use algorithms. While AI algorithms are vital to their cognitive and perception objectives, in Blockchain technology they are mere means of validating and recording the accuracy of transactions. The data becomes a reference – tamper proof – of who did what, when, why and for how much.

3) IoT(Internet of Things) 

The Internet of Things is a concept that is becoming increasingly important, although there are still tests about its functionality, there is no doubt that it is already a reality. Find out exactly what it is.
If we had to define this term, it would be to interconnect physical objects or hardware to a network using the internet, which will allow this hardware to program-specific events or be managed remotely. Who would not like your refrigerator to let you know when your milk ran out or when a product is about to expire? Through an application, we could order our kitchen to prepare dinner.

This would work through a specific IP, through which each object can be accessed for instructions. You can also contact an external server and send the data you collect.
There are also applications of the Internet of Things for transport, the energy industry, and virtually all commercial sectors. The consumer market, that is, homes, is the great slope, although it is probably only a matter of time before we see the internet of things present in the vast majority of houses around the world.

4) Machine Learning

Machine Learning  is the branch of Artificial Intelligence that aims to develop techniques that allow computers to learn. More specifically, it is about creating something capable of generalizing behaviors and recognizing patterns based on information provided in the form of examples. It is, therefore, a process of knowledge induction, that is, a method that allows a general statement to be obtained by generalization from statements that describe particular cases.

When all the particular cases have been observed the induction is considered complete, so the generalization to which it gives rise is considered valid. However, in most cases it is impossible to obtain a complete induction, so the statement to which it gives rise is subject to a certain degree of uncertainty, and consequently, it cannot be considered as a formally valid inference scheme or Can justify empirically. In many cases, the field of machine learning action overlaps that of Data Mining, since the two disciplines are focused on data analysis, however, machine learning focuses more on the study of the computational complexity of problems with the intention of making them feasible from a practical point of view, not only theoretical.

At a very basic level, we could say that one of the tasks of the AA is to try to extract knowledge about some unobserved properties of an object based on the properties that have been observed of that same object (or even of properties observed in other similar objects ) … or, in plain words, predict future behavior based on what has happened in the past. An example of a lot of topicality would be, for example, to predict whether a particular product will be liked by a customer based on the valuations that the same customer has made of other products that he has tried.

In any case, as the topic, we are talking about is related to learning, the first thing we have to ask ourselves is: What do we understand by learning? and, since we want to give general methodologies to produce learning automatically, once we set this concept we will have to give methods to measure the degree of success/failure of learning. In any case, since we are moving an intuitive concept and that we normally use in everyday life to a computational context, it must be taken into account that all the definitions we give of learning from a computational point of view, as well as the various forms of measure it, they will be intimately related to very specific contexts and possibly far from what we intuitively, and in general, understand by learning.

5) Robotic Process Automation (RPA)

• RPA ( Robotic Process Automation ) means robots, are fragments of software that are developed to do certain processes automatically. At great risks, what it does is simulate the actions of humans in the workplace using their keyboard and mouse as a person would normally do.
• Robots execute a large number of repetitive and predetermined tasks efficiently and quickly. Instead, people can be in charge of creating different relationships to improve processes, issue subjective criteria, execute low-frequency tasks and manage change and improvements.
• RPAs do not usually learn from their experience and are not trained to solve certain exceptions they encounter when executing their tasks. There is a variant to this automatic process that is the implementation of artificial intelligence. In this way, the machines do have the ability to make simple decisions just like a human does, and learn from their mistakes to optimize the process.
• This powerful technology will be imposed not only in the banking sector but in the vast majority of them. It allows removing the burden of those monotonous and repetitive tasks to the workers. Thanks to this measure, they can provide greater added value to the company’s strategy.

6) ERP(Enterprise Resource Planning )

Today, many companies are looking for an ERP system for their company, but what exactly is an ERP and how can it benefit a company?

The term ERP refers to Enterprise Resource Planning, which means “enterprise resource planning system.” These programs are responsible for different internal operations of a company, from production to distribution or even human resources.

ERP systems became a great investment these days for companies. According to a 2019 Consulting survey, 40% of companies that acquire an ERP notice an increase in productivity. Request the ERP Guide to find the differences between the best known ERP systems in the market, their price and a review of ERP providers.

Advantages of an ERP system

The main advantages of these systems are:

• Automation of company processes.
• Availability of company information on the same platform.
• Integration of the different databases of a company in a single program.
• Time and cost savings.

7) Cyber Security

Cyber Security is a discipline that is responsible for protecting the integrity and privacy of information stored on a computer system. Although there are already methods that are capable of preserving this information safely, there is no technique to ensure the inviolability of a system.

Continuous cyber attacks show us that such an incident can have catastrophic consequences, from the interruption of production to the closure of sites, the loss of vital data or a negative impact on reputation and brand. Every month we witness increasingly sophisticated attacks that reveal how vulnerable companies and countries are to these new threats. Thus to prevent the cyber attacks cybersecurity technology has become a trending one.

You can consider SSL certificate for data protection as Google also promoted HTTPS few years back. For instance, if your website has multiple subdomains, then you can think about comodo Positive ssl Wildcard for all subdomains’ security.

8) Virtual Reality

It is a fictional reality where they show us unreal scenarios but that immerses us in them until we feel that we really live them.

All this is achieved with technology and accessories such as virtual reality glasses or helmets, including suits, controls or headphones. All of them give us stimuli that are what allow us to enjoy this experience of virtual reality. Like augmented reality, virtual reality is beginning to be extended in several areas, especially in the entertainment and videogames sector and in education.

Types of virtual reality

There are three clearly differentiated types, immersive, non-immersive and semi-immersive virtual reality.

Immersive reality

• In this type, the user perceives sensory stimuli and lives as if the three-dimensional world that is being shown is real.
• This is possible provided that devices prepared for it are used, such as gloves, glasses, suits or helmets.
• Thanks to them, information about the position and rotation of the body is obtained, which allows the technology used to launch the appropriate stimuli to live this experience.

Non-immersive reality

• In this case, we experience all the experience of virtual reality through a screen and not other elements such as the previous type.
• Accessories compatible with the screen are used, ie keyboard, mouse or microphone.
• These are the top technologies that are trending in 2020 and can make a bright impact on our future by bringing many advantages and making human life more comfort

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• Internet of Things(IoT) is the idea that more and more common objects are connected and able to communicate directly with each other (with other connected objects or with the mobile, for example) or through the Internet.
• Some examples that are usually mentioned, and of which products are already marketed, including from the security system, air conditioning, lighting or control of household energy consumption to locks, coffee maker or refrigerator.
• Despite its proliferation – and the interest that the objects connected and intelligent among consumers – “It is common that the term” security ” is not associated with connected home devices”.
• In that sense, many connected objects lack the sufficient level of security and protection that could be desired in the case of devices that interact with other more delicate systems – the user’s mobile phone case – that give physical access to the home or that are designed to obtain and send data to it or through the Internet.
• A classic example that has been going on for years and that is “normalized” refers to Bluetooth devices, many of which have a factory default password as simple as ‘1234’ that also cannot be changed on many occasions. This is perhaps not something important when it comes to connecting headphones or a wireless speaker, but it begins to gain relevance when it comes to transmitting data from the car’s computer that is also then sent open to the Internet, for example.
• But above all, getting used to a relative unconcern when it comes to securing connections can lead to the false sense that security is unnecessary or that it does not deserve too much attention. Even if this premise were true right now, it is necessary to establish the culture and custom of security for the future. In fact, many of today’s security problems with computers and mobiles have a lot to do with the lack of computer security education; Ignorance can also feed more or less well-founded fears or lead to erroneous conclusions, as in the case of the fridge that sent spam messages.
• The report analyzes fifty domestic marketed devices and establishes how they score on security. Most of these devices fail in aspects such as insufficient and even non-existent authentication, weak points in the connection and transfer of data over the Internet and vulnerabilities in hardware or software that make them susceptible to succumbing to potential attacks, “capable of exposing people and homes.”

IoT In The Smart Home

• Thanks to the development of technology in smartphones, homes can be remotely controlled, with maneuvers as routine as starting the oven or closing the blinds. Home automation has advanced in this regard and IoT (Internet of Things) has given rise to a new concept: smart homes.
• Nowadays, many smart appliances can be installed in the kitchen, such as refrigerators capable of initiating a purchase by detecting that the stocks of some type of product of habitual consumption are running out, even of notifying the service of failures before the failure in a piece before Let the owner realize.
• As far as the kitchen is concerned, it is important to save tasks that the smart home brings to its tenants, with self-programmable kitchen robots available, which are set in motion in the preparation of the meal upon receiving an order; for this, they have thousands of integrated recipes, which allows a varied diet without mastering the culinary arts.
• The safety of home is another of the obsessions of the brands that develop applications for home automation and smart home. In the latest edition of the Mobile World Congress in Barcelona, Samsung presented Kwikset Kevo, a lock that works with the Bluetooth of the mobile, recognizing the proximity of the owner, with an approximate sensitivity of one and a half meters, opening the door just by touching the lock.
• You can also save energy thanks to home automation, based on D-Link Smart Plug Smart Wi-Fi plugs or the Belkin WeMo, which allow you to disconnect electrical appliances from a distance.
• And to keep track of the home-based on wearables, the nod ring, consisting of a tactile zone and a pointer, which allows you to turn on and off the connected devices, simply by pointing a finger towards them. The IoT in smart homes, no doubt, has come to stay.

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The report ‘Trends in the work environment 2020: the skills of the future’, prepared by Udemy for Business, the product of Udemy to learn for work environments, highlights that AI will become one of the tools that will be developed and will help to professionals in 2020. Its application has already reached sectors such as marketing, allowing to analyze the behavior of consumers to adapt their campaigns to their preferences, or to financial teams, which for example can already reduce transport costs Thanks to your application. In any case, we can consider the methods of application of AI in companies infinite.

Every day, the advantages of Artificial Intelligence begin to be valued more when it comes to boosting the efficiency and effectiveness of the processes carried out by business professionals.

Although this technology may be novel, its arrival and evolution have not occurred overnight. Among the 10 most learned skills in Udemy during the last triennium (2016-2019) are OpenCV, the free artificial vision library originally developed by Intel, and artificial neural networks, which mimic the human neuronal process; both with a great impact on the development and evolution of AI techniques.

What Do AI Professionals Think?

The development and leadership teams of the companies have decided to bet on AI and the research promoted by Udemy for Business indicates that 35% of those responsible for these teams include it among the 10 technological skills on which their professionals should be trained in 2020. When questioned about how employees view automation and disruptive technologies, such as AI, a more positive view of its application in jobs is evident and only 12% believe that it could lead to the loss of their jobs. On the other side of the balance, 29% acknowledge being excited to learn skills of this type that allow them to play new roles in their sector.

One of the firms that have opted for AI is Publicis Sapient. The company dedicated to the transformation of digital businesses already has an AI academy for all its professionals, who will see their knowledge updated in a subject that can be used and developed in various business lines.

Companies Become Data Collectors

AI cannot work without data on which to perform calculations and, therefore, companies are focusing their efforts on collecting them. To obtain all this information, companies have begun to boost the technical skills of their employees by training them in algorithms, Deep Learning or automation. The application of these capabilities subsequently allows the development of AI processes.

One of the sectors that will bet on AI in 2020 will be that of consumer goods. According to the same report, among the 10 technological skills that will be developed the fastest in 2020 are learning algorithms for application in AI and natural language processing; which are in first and eighth place, respectively.

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