Artificial intelligence. Machine learning. Nowadays, these terms are like a constant mantra. Everyone knows that we are on the eve of a new business era, however, it’s still early days and it is difficult to predict how this technology will shape our future. Nevertheless, it’s already here, and we have to start dealing with it.

Although we still can’t draw any firm conclusions about AI and ML’s impact on the economy, we know that this technology is the next big thing. Industry-leading corporations like Google, Amazon, Apple and Microsoft are investing heavily in this area, spending billions of dollars on development and to gain a market advantage.

In more detail, the big players in 2016 invested in AI (which includes ML, deep learning, computer vision, natural language processing, voice recognition and other linked technologies) to the tune of $20 to $30 billion. And they’re not alone: VCs and other investment funds invested from $6 to $9 billion in startups developing solutions based on these technologies. It’s an enormous pile of cash being poured into tech that we still don’t fully comprehend.

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The subject of AI and its related technologies is vast and complex so for the purposes of this article, let’s focus on three charts which illustrate how artificial intelligence, and more specifically, machine learning is crucial for today’s business.

Chart no. 1 – Making money

The companies in question instinctively know that AI and, more accurately, machine learning is something from which they can benefit big time. This feeling is confirmed by more tangible economic predictions. Let’s take a look at this first chart:

According to this data, revenue from the artificial intelligence market worldwide in 2016 reached more than $1.3 billion. Perhaps this number is not so impressive – especially in comparison to Apple’s net sales of $215 billion, or a net income of $45 billion; both without any need for AI. A the same time Google’s (Alphabet) revenue in 2016 was $90 billion with a profit of $19 billion. And yet, both giants are spending money like crazy on machine learning and other AI-related technologies. Why?

Because the figure of $1.3 billion is just the beginning. The predictions are strongly in favor of AI. By the end of 2025, revenue from this tech will likely reach a level of $59 billion (according to a McKinsey report it might be as much as $126 billion). This is a huge piece of pie and everyone wants a slice. What is more exciting is that these forecasts could change at any time. We still can’t adequately predict how AI will evolve nor how we can use it. We have only just left the starting line of this particular race.

Chart no. 2 – The race has already begun. Anyone who doesn’t run will lose

Despite the fact that AI and machine learning are still in the early stages of development, companies from all around the world are investing heavily and machine learning is at the top of these investments. According to the McKinsey report, companies which invested externally in AI-based businesses spent their money mostly on machine learning, which has attracted almost 60% of the investment pot.



In 2016, external investments in machine learning reached $5-7 billion. Other AI-linked technologies also received serious investment, as you can see in the above chart. Machine learning is attracting the majority of the money mainly because implementing this technology is the most promising in a business context. For example, ML might improve product marketing processes by providing sales forecasts. Or improve medical predictions and diagnoses. Or speed up data and information processing. Or offer estimates for manufacturing industry. The possibilities of using ML are practically endless.

>> See how we implemented machine learning for content moderation for our client.

Businesses are more and more aware of the importance of machine learning and are allocating an increasing portion of their budgets to this technology. According to a Statista survey, 26% of companies asked dedicate more than 15% of their IT budget to machine learning.

All good so far, but what does the adoption of this technology look like today? The early-adopters are mostly in sectors related to high-tech and telecommunications, automotive and assembly, financial services, resources and utilities, and media & entertainment. Companies from these industries are trying to embrace the potential of machine learning and build a market advantage through it.

Bringing up the rear in this race are companies from travel and tourism, building materials and construction, health care, professional services, education, and the retail sector. The adoption of machine learning in these industries is currently slow, but at the same time, these sectors still stand to significantly benefit from ML implementation.

Regardless of the speed of adoption, or the lack of it, companies need IT specialists who can help them to develop machine learning algorithms.

Chart no. 3 – The craving for Machine Learning Engineers

source: LinkedIn


The above chart is from a LinkedIn report on emerging jobs in 2017. Can you guess which positions are required by the market? At the top of the list is machine learning engineer – no surprise there. There are almost ten times more jobs in this category than there were five years ago.

However, the report only refers to the U.S. market; it doesn’t give us any detailed insight into the global demand for ML devs. No wonder. It’s extremely challenging to estimate global market demand for machine learning developers because of the continually changing business environment and an increasing demand for ML-related skills.

Nevertheless, we can identify which languages a machine learning developer should know. Among them, according to Towards Data Science findings, Python is the most popular – 57% of data scientists and machine learning developers are using it. In second place is C/C++ (44%) and then Java, JavaScript and others.

It is also worth noting that there are many other positions related to machine learning, including data and research scientists, business analysts and many more.

For example, based on the LinkedIn report, the demand for data scientists has increased massively in the last five years – by almost 6.5 times. This trend is also partially confirmed by an IBM report cited by Forbes, according to which by 2020, in the U.S., there will be 700,000 openings for data scientist-type jobs.

This growing demand has the effect of raising companies’ recruitment and salary costs. According to a TechRepublic article, annual earnings could be as high as $142,000 for a machine learning engineer and $141,000 for data scientists. Of course, that’s if you are lucky enough to find a suitable candidate.

Machine learning is on the rise – be prepared

Let’s be clear, this article just scratches the surface of the current state of the machine learning market. But we can be sure of one thing, that AI-related technologies, and machine learning in particular, will shape the future of the business. There is no doubt. It’s not a question of “if” but “when.” We already can see the consequences of implementing machine learning in various economic sectors, for example, autonomous vehicles. The biggest tech-related corporations are spending billions on ML, VCs are investing in AI startups, and IT budgets dedicated to this tech are growing. The market demand for machine learning engineers and data scientists is already skyrocketing.

This trend can’t be reversed and companies which want to be ahead of the change have to embrace the fact that the role of machine learning and AI in business will only grow. It is better to sit at the front of this accelerating train.

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