Research from STX Next, Europe’s largest software development company specializing in the Python programming language, has found that 68% of chief technical officers (CTOs) have implemented machine learning at their company. This makes it overwhelmingly the most popular subset of AI, with others such as natural language processing (NLP), pattern recognition and deep learning also showing considerable growth.

Despite the popularity of AI and its various subsets, it’s also clear that AI implementation is still in its early phases and there’s progress to be made in recruiting the talent needed for its development. In fact, 63% of CTOs reported that they aren’t actively hiring AI talent and of those that are, over 50% report facing recruitment challenges.

The findings were taken from STX Next’s 2021 Global CTO Survey, which gathered insights from 500 global CTOs about their organization’s tech stack and what they’re looking to add to it in the future. Other key findings from the research included:

• 72% of respondents identified machine learning as the most likely technology to come to prominence in the next two to four years, with 57% predicting the same for cloud computing.
• 25% of CTOs reported that they’ve implemented natural language processing, with 22% implementing pattern recognition and 21% applying deep learning technologies.
• 87% of businesses employ up to 5 people in a dedicated AI, machine learning or data science capacity.
• However, just 15% currently have  a dedicated AI department at their company, underlining that there is room for further development.

Łukasz Grzybowski, Head of Machine Learning & Data Engineering at STX Next, said: “The implementation of AI and its subsets in many companies is still in its early stages, as evidenced by the prevalence of small AI teams.

“It’s unsurprising to see machine learning as a definite leader when it comes to future technologies as its applications are becoming more widespread every day. What’s less obvious is the skills that people will need to take full advantage of its growth and face the challenges that will arise alongside it. It’s important that CTOs and other leaders are wise to these challenges, and are willing to take the steps to increase their AI expertise in order to maintain their innovative edge.

“Deep learning is a good example of where there is plenty of room for progress to be made. It is one of the fastest developing areas of AI, in particular when it comes to 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 commonly used to process structured data, which is evidenced by the high 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 making AI and machine learning work for everyone.”

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