55% of companies now employ at least one dedicated AI / ML solutions team member, although only 15% have their own separate AI division
A study from STX Next, Europe’s largest software development company specializing in the Python programming language, found that 68% of CTOs have implemented machine learning in their business. This makes it by far the most popular subset of AI, with others such as natural language processing (NLP), pattern recognition, and deep learning showing tremendous growth as well.
Despite the popularity of AI and its various subsets, it is also clear that the implementation of AI is still in its infancy and there is room for improvement in recruiting. talents necessary for its development. In fact, 63% of CTOs said they are not actively hiring AI talent and of those who do, over 50% say they face recruiting issues.
The results were taken from STX Next’s 2021 Global CTO Survey, which gathered information from 500 global CTOs about their organization’s technology stack and what they are looking to add to it in the future. Other key research findings include:
- 72% of those surveyed identified machine learning as the technology most likely to gain prominence over the next two to four years, with 57% predicting the same for cloud computing.
- 25% of CTOs said they implemented natural language processing, 22% implemented pattern recognition, and 21% applied deep learning technologies.
- 87% of companies employ up to 5 people in a dedicated AI, machine learning or data science capacity.
- However, only 15% of them currently have a dedicated AI department in their business, which highlights that there is still room for development.
Łukasz Grzybowski, Head of Machine Learning and Data Engineering at STX Next, said: “The implementation of AI and its subassemblies in many companies is still in its early stages, such as This is evidenced by the prevalence of small AI teams.
“It’s no surprise to see machine learning as a clear leader in future technologies, as its applications become more widespread every day. What is less obvious are the skills people will need to take full advantage of its growth and face the challenges that will arise at the same time. It is important that CTOs and other leaders are aware of these challenges and are prepared to take the necessary steps to increase their AI expertise in order to maintain their innovative edge.
“Deep learning is a good example of an area where there is a lot of room to progress. It is one of the fastest growing areas of AI, especially in its application in natural language processing, natural language understanding, chatbots, and computer vision. Many innovative companies are trying to use deep learning to process unstructured data such as images, sounds, and text.
“However, AI is still most often used to process structured data, as evidenced by the great popularity of classical machine learning methods such as linear or logistic regression and decision trees.”
Grzybowski concluded: “To adapt AI to unstructured data, the technology will need to mature further. This is why initiatives such as MLOps have a major role to play, as long-term success will only be achieved when data scientists and operations professionals are all on the same page and fully committed to doing so. making AI and machine learning work for everyone.
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