2 min

Despite the widespread implementation of AI in various sectors, few companies are prepared to use AI in natural language processing.

A new report by Expert.ai surveyed data and analytics decision-makers. The report’s results show that nine out of ten CDOs agree that managing unstructured language data is crucial in the industry. In the report, 91% of experts stated that this issue must be addressed in 12 months.

AI and language data management

Companies have been using artificial intelligence to structure and interpret data for a long time. However, there is a new sense of urgency around the ways data is managed, analyzed, and governed.

As this newfound emphasis keeps rising around data, business chief data officers (CDOs) are directly put in the spotlight. Moreover, the new reports state the importance of AI in different domains such as language understanding, data surveillance, and analytical decision-making in the companies’ success.

Most experts in the survey agree that businesses should use AI for unstructured language data. This includes text from business documents and emails, for example. However, very few companies are prepared with the right tools to make this transition successfully.

Growing awareness across enterprises

Unlike before, enterprises realize the importance of unstructured language data is not merely a byproduct of operations. But it is a vital resource that can produce actionable insights.

In the future years, extracting value data from unstructured language data will separate businesses, and will show which ones are more successful. This is because they will reduce manual document handling, reducing paper costs.

Natural language processing (NLP) and natural language understanding (NLU) tools are identified as the proven way to achieve this goal. Currently, 8% of data teams have NLP and NLU projects under their belt. Now, these businesses can fully unlock the actual value of their unstructured language data.

However, most businesses don’t plan to be left behind, so 34% of data teams are working on activating plans for NLP, and 24% are trying to define their objectives but are not ready to activate them yet.