What is extractive AI?

Extractive AI is predominantly a subfield of natural language processing that concentrates on identifying and extracting crucial information from present data sources. It surpasses its generative AI counterpart by finding and summarising relevant information within documents, databases and other structured and unstructured data formats rather than creating new content. 

It can be considered a superpowered search engine, not just pulling up websites but highlighting the exact sentences or passages that answer the queries. This approach makes extractive AI superior for tasks that demand accuracy, transparency and control of the extracted data. 

How does it work?

Extractive AI harnesses a combination of NLP techniques that include:

• Tokenisation- It helps break down text into individual words or phrases.
• Named entity recognition- This identifies and categorises entities such as people, places and organisations. 
• Part of speech tagging helps assign grammatical roles to words within a sentence.
• Semantic analysis- It helps in understanding the meaning and relationships between words. 

By leveraging these techniques, extractive AI algorithms monitor the data, search for patterns, and identify segments appropriate for users' queries or desired information. Then, the extracted data can be presented in several formats, such as summaries, highlight points, or passages. 

The upsurge of extractive AI in enterprises

According to a report, the global extractive AI market is expected to hit $6.8 billion by 2027, driven by its surge in adoption across industries. Enterprises worldwide are identifying the value of extractive AI in restructuring processes, improving decision-making and attaining deeper insights from their data. 

Below are a few crucial use cases that drive the adoption of extractive AI.

• Understanding and summarising the documents- Extracting meaningful information from contracts, legal documents, financial reports, and customer reviews is one of the critical use cases of extractive AI. 
• Retrieving information and search- Enhancing the accuracy and efficiency of search queries within organisation databases. 
• Competitive intelligence involves collecting and analysing market data, news articles, and social media to gain insights into competitor strategies. 
• Customer service and support- It helps automate FAQs, monitor customer feedback, and enhance agent efficiency. 
• Detecting frauds and managing risks- It helps recognise suspicious activities and patterns within financial transactions and other data sources. 

The benefits

Extract the exact points

Extractive AI excels at pinpointing essential facts and figures from unstructured data such as documents, reports, and even social media. This saves time on manual searching and ensures no crucial detail is missed.

Unlocks the knowledge 

Extracted data is not just raw data. It can be considered as the knowledge waiting to be unlocked. AI can then analyse these bits, revealing patterns, trends, and insights hidden within complex sentences. This aids businesses in making data-driven decisions, optimising processes, and gaining a competitive edge.

Efficiency 

Repetitive tasks such as data entry and document analysis are the most time-consuming. Extractive AI automates such processes and allows human resources for higher-level thinking and creativity.

Transparency

The reasoning ability of Extractive AI is clear and traceable compared to other AI models. We can find out exactly where the information came from and how it was extracted. This transparency aids in developing trust and makes it easier to validate the accuracy of the data.

Cost-effective

Extractive AI offers substantial cost-saving benefits by automating tasks and unleashing the power of data. Reduced labour costs, enhanced decision-making, and efficient processes lead to healthier results.

To sum up, besides extracting precise information, extractive AI is more about extracting value, efficiency, transparency and insights that can boost enterprises.