Half of the companies currently apply the artificial intelligence (AI), according to a study by the consulting firm McKinsey & Company, which points to the increase that has occurred since 2017, when only 20% of those surveyed used AI in at least one business area. LOOK:
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This is one of the main conclusions of the report ‘The state of AI in 2022 and the balance of half a decade’, which collects data from 1,492 participants covering the full range of world regions, industries, company sizes, functional specialties, and job titles.
In addition, the number of artificial intelligence applications used has doubled, going from 1.9 in 2018 to 3.8 in 2022. Specifically, robotic process automation (39%) and computer vision (34%) continue to be the most applied technologies each year.
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In contrast, antagonistic generative networks —machine learning in which two neural networks compete with each other to increase the probability of success in their predictions— and transformers —learning model that uses the mechanism used in natural language processing—are at the bottom with 11%.
OPTIMIZATION OF PROCESSES AND CREATION OF NEW PRODUCTS
Regarding the functions in which artificial intelligence is most applied, process optimization has held the top spot for the past four yearswith 24% of respondents stating that it is used for this purpose.
Behind are the creation of new AI productswith 20%, customer service analytics, customer segmentation and AI-based product improvements, with 19% in all three cases.
In contrast, risk modeling and analysis and the predictive and intervention service are at the bottom, with 15% and 14%, respectively.
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Gloria Macías-Lizaso, partner at McKinsey & Company, explained that, over the last three years, those that obtain the greatest impact from the adoption of AI on results have been included in the group of companies with high performance in AI. i.e. 20% or more of EBIT (earnings before interest and taxes).
“The number of respondents who belong to this group has remained around 8%. The study indicates that these companies are doing better because AI accelerates profit growth, but they also say they are reducing costs”has stated.
SOFTWARE ENGINEERS, THE MOST HIRED
On the other hand, the report highlights that companies with better AI performance have an easier time finding tech talent, and the gap is especially noticeable among AI data scientists. While 60% of ‘top performing’ companies have incorporated a profile of this type into their team, only 31% of companies with average performance have done the same.
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With the machine learning engineers the same problem is perceived: only one 27% of companies with average performance have incorporated one, while among the ‘top performance’ companies the percentage rises to 58%.
According to the report, the software engineers are the most hired AI professional profile in the last year, with 39% of companies claiming to have hired him. They are followed by data engineers, with 35%, and data scientists, with 33%. This shows, according to the consultancy, that many companies have stopped perceiving AI as an experiment and have started to incorporate it into their business activity.
The most common strategy for having professionals who know how to work with artificial intelligence consists, according to the survey data, in training employees, something that 47% of companies do. In addition, it is common to opt for recruitment in good universitiess, as do 36% of companies with average performance, or in leading technology companies, with 23%.
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Macías-Lizaso has detailed that ‘top performance’ companies offer AI skills development programs based on experiences, while those with medium performance tend to limit themselves to offering self-directed online courses.
In addition, ‘top performers’ respondents are nearly three times as likely as others to say their companies have capacity building programs to develop the AI skills of technology staff.
LACK OF DIVERSITY IN TEAMS
Finally, the study has detected that, in the field of artificial intelligence, diversity is a pending task, since the proportion of women in the teams of the surveyed organizations is only 27% and the proportion of racial or ethnic minorities developing AI solutions is only 25%.
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Faced with this problem, 46% of those surveyed state that their company offers programs to increase gender diversity in AI solutions teams, while 33% indicate that their company has programs to increase racial and ethnic diversity.
According to McKinsey, this lack of diversity influences the company’s performance, which loses opportunities to increase it. According to the data in the report, organizations where there are more than 25% women are 3.2 times more likely than others to be high performers in AIwhile those where at least 25% of employees are from racial or ethnic minorities are more than twice as likely to be high performers in AI.
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