Extractive AI Changing The Start-Up Game

Businesses use AI for reasons categorised in two, I. e., Generative and Extractive. However, while many of them are highly making use of ChatGPT, it doesn't help them for extractive purposes.

Extractive AI Changing The Start-Up Game

The AI industry is blooming among businesses today. ChatGPT and Google Bard, known to be two dominant AI & LLM tools have taken the technology game one step ahead. Now the question is how much should businesses rely on AI and for what purpose specifically?

Businesses use AI for reasons categorised in two, I. e., Generative and Extractive. However, while many of them are highly making use of ChatGPT, it doesn't help them for extractive purposes. It is thus essential for businesses to understand how to consider these two approaches so as to make better use of AI.

Is Extractive AI different from Generative AI?

Speaking of Generative AI, it is used when businesses are involved with tasks that require creativity. It may involve the user to input a prompt and expect an answer from AI. Businesses often use this type of AI to promote operational efficiency within their workflow.

However, Extractive AI has nothing to do with creativity. Rather, businesses use this approach for tasks that generally require accuracy. Since a lot of businesses are involved with gathering, storage, and accurate analysis of data, extractive AI is more suitable to save time and money.

What is Extractive AI

One of the examples of Extractive AI is Google's featured snippet that pulls off essential information and highlights text—for the human eye.

Extracted text from the Extractive AI is often helpful to make sense of data as well as improve the user experience. This also helps businesses who have large product manuals and desire to extract specific information out of it and make decisions accordingly.

Extractive AI Generative AI
Identifies and summarises existing information Generates new text/content
Trained to extract key phrases from existing data Trained on vast datasets
More controllable, confined to existing knowledge Less controllable, outputs are unpredictable
Aligns with enterprise data, stays on-brand Potential for misalignment with enterprise data
Emphasises transparency and auditability Black-box nature, hard to audit
Search, discovery, knowledge management Content writing, code generation, experimental
Challenges in tuning models for different domains Rapid innovation but immature market
Less likely to displace human jobs Higher risk of displacing human creativity

The Importance of Extractive AI in Businesses

Consider you own a business that handles 1000+ invoices daily. Your employees work more than 9 hours just to record data from these invoices manually into the computers. However, this not only makes them largely prone to making errors but also makes the tasks repetitive.

Extractive AI rather solves this problem. It has the ability to quickly identify different forms of data (tables, line format, and so on), extract and store it within the computer device—without compromising on its accuracy. This allows employees to focus on other strategic tasks of businesses as Generative AI takes care of these repetitive tasks throughout.


Advantages of Using Extractive AI

Extractive models enhance operational efficiency by summarising and extracting key information from large datasets, saving time in decision-making processes.

Following are the advantages of using extractive AI:

  • Efficiently organises and summarises information from diverse sources, improving accessibility for teams.

  • It provides transparent reasoning and facilitates auditing. This is critical for industries with regulatory requirements.

  • Minimises risks of exposing sensitive customer data. This ensures better data security and compliance with privacy regulations.

  • It enables better decision-making by extracting relevant insights from large datasets, supporting informed and data-driven choices.

  • Helps maintain brand alignment by summarising information from approved knowledge bases, reducing the risk of generating off-brand content.

  • contribute to innovation and creativity by generating new content, supporting tasks such as creative writing, idea generation, and product innovation.

Use Cases for Extractive AI

  1. Analysing Financial Data
    Extractive AI can analyse lengthy financial reports, earning statements, and regulatory filings. It identifies and summarises key information, such as financial performance, risks, and strategic initiatives, allowing analysts to quickly grasp essential insights. Additionally, it can review large datasets of financial transactions, summarising patterns and identifying anomalies that may indicate fraudulent activities. This aids in real-time fraud detection and prevention.

  2. Customer Service in Banking
    Customers who visit the bank often have queries regarding their personal bank account, money transacted on a specific date, or other finance related information. Extractive AI supports customers’ needs by extracting key details for efficient issue resolution. This enhances the overall customer experience in the financial sector.

  3. Analysing Customers’ Feedback
    Extractive models can process customer reviews and feedback, summarising sentiments and extracting key insights. This helps retailers quickly identify areas for improvement and make data-driven decisions to enhance customer satisfaction. It can also summarise information from competitor reports, industry analyses, and market research. This enables retailers to stay informed about competitors' strategies, pricing, and product offerings for better positioning in the market.

  4. Price Monitoring and Adjustment
    Extractive models can summarise pricing data from competitors and market trends. Retailers can use this information to make informed decisions on pricing adjustments, ensuring competitiveness and maximising profit margins. Additionally, it can process product descriptions, user manuals, and specifications, summarising essential information. This facilitates quicker product onboarding, simplifies training for sales associates, and enhances the overall product knowledge within the organisation.

  5. Trend Analysis in the Market
    Trends and customer preferences in the market never remain; the same. They are dynamic. Thus, with the help of extractive AI, you can process fashion trends, consumer preferences, and industry reports, by summarising key insights. This aids retailers in making informed decisions about merchandising, inventory selection, and marketing strategies.

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