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A report published by expert.ai, the premier artificial intelligence (AI) platform for language understanding, surveyed data and analytics decision makers to reveal how teams are faring as they attempt to guide their companies towards AI success.
Value of unstructured data
There is growing realization across enterprises that unstructured language data is not merely a by-product of operations but a vital resource to be mined for actionable insights. The ability to extract value from unstructured data is what will separate businesses from their competition via better Net Promoter Scores and reduced manual document handling and extraction costs. NLP and NLU technology have been proven the key to doing so.
Despite this, only 8% of data teams have completed NLP and NLU projects within their business that would enable them to fully unlock the value of their unstructured language data. More than a third (34%) of data teams have started activating plans for NLP projects. Nearly a quarter (24%) are still defining their plans but are not ready to activate them.
The report, titled “Harnessing the Power of Unstructured Data with NLP and NLU” was prepared by The AI Journal. It was fueled by a survey of CDOs, which also revealed the top three most popular types of NLU solutions are platform (44%), open-source (34%) and cloud vendor offerings (34%).
AI skills in demand
Nearly all CDOs (96%) see delivering business impact through AI as their top concern in 2022. However, while two-thirds of organizations claim to be knowledgeable about AI, they often lack employees with the skills to build and execute programs. Organizations have found it difficult to acquire the necessary talent, whether through internal training or external recruitment.
Given the low supply of data skills on the market, it’s no surprise that organizations looking to fill their skills gaps choose not to seek external expertise as a first option. With that said, the timing of AI projects is an important consideration. Companies that have made definitive AI plans but have not yet activated them are more likely to look for external expertise (58%) versus upskilling methods.
When data teams identify holes in their team members’ knowledge and understanding, the most common solution is to upskill through training. This was the primary method for every specific knowledge area including AI (51%), NLP (41%) and NLU (35%). Few organizations felt online content or mentoring from team leaders were viable methods for bridging the skills gap.
“Unstructured data is the white whale of the business world. It represents the great majority of enterprise data but is extremely difficult to extract value from,” said Marco Varone, founder and chief technology officer, expert.ai. “Those that can do so effectively put themselves in a prime position to make more intelligent business decisions and operate with greater efficiency.
“To make the most of unstructured data, AI and NLP must be priorities,” added Varone. “However, historical approaches to AI and NLP no longer suffice. To succeed, you need the right approach, the right expertise and a focus on the right data. Natural language understanding is the answer to these broad language data challenges.”
