{"id":11569,"date":"2022-04-11T16:40:00","date_gmt":"2022-04-11T14:40:00","guid":{"rendered":"https:\/\/laigo.ai\/?p=11569"},"modified":"2025-08-17T09:52:37","modified_gmt":"2025-08-17T07:52:37","slug":"machine-learning-from-hype-to-the-new-standard","status":"publish","type":"post","link":"https:\/\/laigo.ai\/en\/blog\/machine-learning-from-hype-to-the-new-standard\/","title":{"rendered":"MACHINE LEARNING \u2013 FROM HYPE TO THE NEW STANDARD"},"content":{"rendered":"
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Like most topics related to artificial intelligence, machine learning (ML) is also in vogue. This refers to IT systems that evaluate large volumes of data with the aim of independently recognizing patterns and regularities and applying them to new data in order to make forecasts and identify correlations. For companies, the question will sooner or later arise as to whether ML is really the technology of the future that it is portrayed as in public, whether it can do what it is expected to do, and whether it is also suitable for them in order to get to grips with current challenges and secure the future of the company.

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UPHILL AND DOWNHILL TO MARKET MATURITY: THE GARTNER HYPE CYCLE<\/b><\/h2>

The “classic” source of information for assessing new technologies is the\u00a0Hype Cycle from the market research company Gartner. It is actually a curve that traces and forecasts the regularity of rise, decline and transition to an accepted technology. Gartner coined the term for this up and down and developed the representation in a progression curve. The classification of technologies in this scheme is certainly subjective, but it helps companies to compare their own assessment of the potential of new technologies with the accumulated assessments of others.
A look at\u00a0the 2021 chart for AI\u00a0shows that ML has passed the zenith of exaggerated expectations and is on its way to the valley of disillusionment – combined with the expectation that the transition to a widely used and accepted technology will take place within a few years. In other words, ML is not yet standard, but it is on its way. Companies that already rely on ML are gaining a significant head start, but they are not immune to paying a lesson.

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MORE AND MORE COMPANIES ARE USING ML<\/b><\/h2>

Studies in Germany show the growing acceptance of ML in companies. According to the IDG report “Studie Machine Learning 2020”, ML technologies are used by more than 43 percent of the large companies surveyed and around 30 percent of the medium-sized companies. Smaller medium-sized companies with 50 to less than 500 employees are still lagging behind with ten percent. In the\u00a0follow-up study in 2021, which takes a different approach to company sizes, the proportion of mid-sized and enterprise-level companies with ML projects is 70 percent, while the figure for companies with fewer than 1,000 employees is just under 60 percent. Forty-one percent of respondents to the “Artificial Intelligence in the Midmarket” study released by accounting firm Deloitte in 2021 see ML as an important technology for midmarket companies.

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FROM NICHE TO FUNCTIONAL APPLICATION<\/b><\/h2>

But not only is the number of companies working on ML projects increasing, the spectrum of use cases is also growing.\u00a0According to the Internet industry association eco and Arthur D. Little, at least 50 different use cases are expected to be firmly established in companies by 2025.
Areas of application continue to be found increasingly in IT with its increasingly complex infrastructures and for defense against cyber attacks, as well as in production. There, ML is primarily used for predictive maintenance, quality assurance and the optimization of production processes, as well as for the optimization of supply chains. In marketing and sales, market analyses and support by digital assistants play the main role. The participants in the Deloitte study also see great potential in finance and accounting, HR and R&D, among other areas.
According to the IDG follow-up study, new applications such as customer self-service offerings, which are being developed by some companies, sometimes lack customer acceptance, for example when it comes to medical diagnoses. What ML needs is time to establish itself in companies. In many companies, it is not so much the money for ML projects that is lacking as the acceptance of the workforce and fears of a lack of acceptance among customers and partners. And there is a lack of specific know-how, which can hardly be found on the labor market at present, so that medium-sized companies in particular are resorting to cooperation with appropriate service providers.

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CONCLUSION<\/b><\/h2>

ML has evolved from a niche application for nerds to a technology with a wide range of potential applications. Most companies have understood that the new technology has a high value for their future development. At the same time, ML solutions have reached a level of maturity that can bring positive impact to the majority of users within a few months. Companies that have so far been hesitant to get involved with ML should not let the know-how lead of their competitors become too great, lest they be left behind in a few years, because ML is well on its way to becoming the future standard.

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Like most topics related to artificial intelligence, machine learning (ML) is also in vogue. This refers to IT systems that evaluate large volumes of data with the aim of independently recognizing patterns and regularities and applying them to new data in order to make forecasts and identify correlations. For companies, the question will sooner or […]<\/p>\n","protected":false},"author":7,"featured_media":18573,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-11569","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-regulation"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t