In a research report, Databricks compares the use of AI in different sectors. The role of AI is increasing everywhere, but there are also big differences.

Which industry is most advanced in using AI? With this question in mind, Databricks began investigating. The State of Data + AI report provides a comprehensive comparison of how companies are approaching AI across industries. The research also reveals the differences between sectors.

Databricks sees one constant: the importance of AI is increasing everywhere, regardless of the industry. “The accelerated adoption of AI across industries signals a broader transformation of business operations. As AI technology continues to evolve, companies that harness its potential are likely to gain a competitive advantage,” said Kevin Jonkergouw, RVP Benelux at Databricks.

Retail efficiency

Retail shows the greatest efficiency in AI implementation: 25 percent of the models developed are ultimately used. This efficiency is due to competitive pressures within the industry and customer expectations, both of which are driving the rapid adoption of AI.

Most testing takes place in the financial sector, with 29 experiments per registered model. Companies in the financial sector have become three times more efficient in this regard over the past year. This need for robust testing is also evident in other regulated sectors, such as healthcare, where compliance remains a key risk.

NLP

Healthcare is the most focused on NLP (Neurolinguistic Programming): This sector uses the highest percentage of Python libraries (69%) for NLP. According to recent studies, the healthcare industry already provides around 30 percent of all data worldwide. NLP is therefore useful, among other things, for supporting clinical research analysis and accelerating drug development.

The manufacturing industry is also catching up with NLP: usage increased by a whopping 148 percent. In the manufacturing industry, APIs are most commonly used to perform AI work. The main use cases in this industry are supply chain optimization, quality control and production efficiency.

“The differences in AI usage between sectors highlight the importance of good coordination of AI strategies and technologies with specific business needs and the regulatory landscape,” adds Jonkergouw.