In a research report, Databricks compares AI use across industries. The role of AI is increasing everywhere, but there are also big differences.
Which sector is furthest along in its use of AI? With that question in mind, Databricks set out to investigate. The State of Data + AI report contains a comprehensive comparison of how companies across industries are handling AI. As such, the research also exposes the differences between industries.
Databricks sees one constant: the importance of AI is increasing everywhere, no matter what industry. “The accelerated adoption of AI across industries indicates a broader transformation of business operations. As AI technology continues to evolve, organizations that embrace its potential are very likely to gain competitive advantage,” said Kevin Jonkergouw, RVP Benelux at Databricks.
Efficiency in retail
The retail industry shows the greatest efficiency in AI adoption, with 25 percent of the models developed eventually being deployed. This efficiency can be attributed to competitive pressures within the industry and customer expectations, both of which encourage rapid adoption of AI.
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Then again, the financial sector has the most testing, with 29 experiments per registered model. Companies in the financial sector have become as much as three times as efficient in this over the past year. This need for thorough testing is also visible within other regulated industries, such as healthcare, where compliance remains a significant risk.
NLP
The healthcare industry is betting the most on NLP (neurolinguistic programming): it deploys NLP with the highest percentage of Python libraries (69%). The healthcare industry already provides some 30% of all data in the world, according to recent research. NLP is therefore useful, supporting clinical research analyses and accelerating drug development, among other things.
The manufacturing industry is also catching up with NLP: its deployment grew by a whopping 148 percent. In the manufacturing industry, APIs are most commonly used to perform AI work. The main use cases within this industry are supply chain optimization, quality control and production efficiency.
“The differences in AI use across industries thereby highlight the importance of properly aligning AI strategies and technologies with specific business needs and the regulatory landscape,” Jonkergouw adds.