Machine learning and artificial intelligence: the future is today

Machine learning and artificial intelligence: the future is today
Machine learning and artificial intelligence: the future is today
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Machine learning (ML), a subset of artificial intelligence (AI), is defined as a set of mathematical techniques that, when implemented on computer systems, allow you to extract information, discover models and draw inferences from data. In particular, it refers to the ability of a machine to use algorithms and processes that are able to generalize past data and experiences to predict future results.

This article aims to demonstrate how pervasive machine learning is in today’s reality and has been able to change the way everyone lives and works. Two considerations clarify the importance of the question. The first, nowadays it is no exaggeration to say that each of us comes across machine learning systems several times a day: mobile banking, pop-ups of the most important news, recommended contents in social networks, chatbots – these are just a few. examples. Second, companies in every sector, relying on consulting services, use machine learning-based solutions to improve productivity, decision-making, product and service innovation, the customer journey and more.

Machine learning and artificial intelligence: i numbers

The global machine learning market is constantly expanding. It was valued at $ 15.44 billion in 2021, and with the growing adoption of technological advances, Fortune Business Insights expects it to reach $ 21.17 billion in 2022. The billion will be increased tenfold by 2029: 209. 91, with a CAGR of 38.8%. In 2023, the artificial intelligence market is expected to hit $ 500 billion. In 2030, 1,597.1 billion (CAGR of 38.1% from 2022 to 2030). In addition, the IBM “Global AI Adoption Index 2022” report reports that 34% of companies say they use artificial intelligence in their business. A further 42% of respondents say they are exploring AI systems (global average).

To date, the most advanced country is China, with an overall adoption rate of 88%. Italy ranks fourth, preceded by China, Singapore and India. Again, the data in the IBM report shows that larger companies are twice as likely to have actively implemented AI. While the smaller realities have a better chance to explore or not pursue AI. According to Deloitte, 46% of organizations plan to implement AI in the next three years.

Forrester predicts that by 2025 nearly 100% of enterprises will implement some form of artificial intelligence. Among the major drivers of AI adoption, IBM identifies the increasing accessibility of the technology, the need to reduce costs and automate key processes, and the increased implementation of AI in off-the-shelf standard business applications.

While machine learning use cases are increasingly varied today, customer-centric applications remain the most common. A survey conducted by Statista reports that 57% of respondents state that customer experience is the main use case of ML and AI. 50% say they use them to generate customer insights and intelligence; 48% for customer interaction.

Let’s find out the benefits

Being an extension, and not a replacement, of human capabilities, machine learning allows companies to automate complex processes, improve the quality, effectiveness and creativity of employee decisions, discover gaps and opportunities in the market to introduce new products. and services, hyper-personalize the customer experience and much more.

By spreading ML and AI initiatives, companies are getting more value from their investments. According to IBM research, 30% of global IT professionals say their employees are already saving time with new AI and automation software and tools. Additionally, a Deloitte study reports that the average cost reduction expected by organizations adopting intelligent automation over the next three years will be 31%.

Furthermore, research by PwC shows that world GDP could increase to reach + 14% in 2030 (up to 15.7 trillion dollars). How? By accelerating the development and adoption of ML and AI. It is also projected that 45% of total economic gains by 2030 will be the result of AI-driven product improvement. The latter will also be able to stimulate consumer demand. All regions will therefore benefit from machine learning and artificial intelligence. China and North America will record the highest economic returns, respectively + 26.1% and + 14.5%.


However, the exponential growth of this market is causing difficulties for many companies which, wanting to benefit from the numerous advantages in this field, are unable to find qualified personnel. According to Statista, 82% of organizations need machine learning skills and only 12% of enterprises report that the supply of ML skills is at an adequate level.

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This shortage has the potential to hold back digital innovation and economic growth. According to the IBM research “Global AI Adoption Index 2022”, 34% of organizations consider insufficient AI skills, competencies or knowledge the main reason blocking their successful adoption. With the belief that a specialized team can exploit machine learning and artificial intelligence to their full potential, 68% of companies are setting aside a budget for the retraining and updating of existing employees; 58% to identify and recruit skilled talent from other companies and organizations; 49% for hiring university students (SnapLogic survey).

Specializations most requested by companies include: coding, programming and software development (35% of companies), understanding of governance, security and ethics (34%), data visualization and analysis (33%), degree advanced in a field closely related to AI or ML (27%). Regarding the soft skills needed to fill these roles, 37% of respondents in the IBM “Addressing the AI ​​Skills Gap in Europe” survey believe that problem solving is the most critical soft skill and that 23% of technology recruiters struggle to find candidates with this attitude.

What are the challenges

According to Statista and IBM, the main challenge preventing AI and ML from reaching their potential is the inability to develop their projects. 85% of IT professionals agree that consumers are more likely to choose a transparent company about how its AI models are built, managed and used. In this regard, the IBM research “Global AI Adoption Index 2022” reveals that the majority of organizations have not taken key measures to ensure that their AI is reliable and responsible. The main problems include the failure to reduce involuntary biases (74% of firms), not taking into account changes in performance and model drift (68%) and failing to ensure that AI-based decisions can be explained ( 61%).

As can be seen from the above statistics, every company, regardless of the sector, has an infinite number of machine learning adoption scenarios and a high probability of success if it pursues the initiative to employ them within its processes. The progress in the use of machine learning and artificial intelligence, aimed at obtaining tangible economic gains, needs to be supported by a holistic approach.

A vision that does not focus on implementing scattered solutions in response to specific needs is therefore essential. These two elements need to be considered as business transformation factors. Which can lead to improved decision making, to the modernization of current systems. And which will be, in the near future, essential for any company.

Asya Peruzzo

The article is in Italian

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