The Unsung Hero of AI Transformation: Why LLMs for Extractive AI is the Powerful, Yet Safer, Enterprise AI Use Case

The buzz surrounding Generative AI is deafening. At this year’s Snowflake Data Cloud Summit, every conversation seemed to touch on the potential of large language models (LLMs), like Snowflake Arctic, to revolutionize how businesses operate. While the excitement is warranted, I found myself drawn to a less flashy, but equally transformative application of LLMs: Extractive AI, particularly in document processing.

Here at evolv Consulting, we’ve been working on the cutting edge of this technology. Our Intelligent Document Processing (IDP) Accelerator, designed for the financial services industry, showcases the power of LLMs for semantic matching. But it also highlights a crucial distinction that I believe many enterprise leaders are missing: the difference between Generative and Extractive AI, and why the latter may be the key to unlocking value while mitigating risk.

The Generative AI Risk

Let’s be clear: the concerns enterprises have about Generative AI are valid. The potential for these models to create content that is inaccurate, biased, or even harmful is very real. Consider a handful of potential scenarios…

• A health insurance company deploys a Digital Assistant to answer customer questions about their coverage and benefits. However, the chatbot, trained on a vast but unverified dataset of medical information, provides inaccurate or outdated information about a specific treatment, leading the customer to make a detrimental healthcare decision.
• A financial analyst at an Asset Management firm relies on a generative AI tool to summarize complex financial reports and generate investment recommendations. The AI is trained on data that doesn’t account for current market volatility and provides overly optimistic projections, leading to poor investment decisions.
• An e-commerce platform integrates a generative AI chatbot to personalize product recommendations. A malicious actor exploits vulnerabilities in the chatbot’s architecture, extracting sensitive customer data and resulting in a major data breach.

No doubt you’ve heard of similar use cases. And the risk scenarios are all too real.

A Safer Alternative

Extractive AI, on the other hand, is fundamentally different. It doesn’t create new content; it extracts and interprets existing information. Think of it as a highly intelligent research assistant, capable of understanding the nuances of human language and identifying key pieces of information within vast amounts of unstructured data.

Our IDP Accelerator, for example, uses Snowflake Arctic to semantically match entities within loan applications and supporting documents. It understands that “gross income,” “total compensation,” and “pay before taxes” are all referring to the same thing, even if the words are different. This allows for faster, more accurate loan processing, but without the risks associated with generating new content.

The Power of Extractive AI in Document Processing

Semantic matching is the secret sauce of Extractive AI. It’s what allows LLMs to understand the meaning behind words and phrases, not just the words themselves. This is a game-changer for document processing. In the financial services industry, for instance, it can automate the extraction of key data points from paystubs, tax forms, and other documents, significantly reducing manual effort and improving accuracy.

But the applications extend far beyond finance. In healthcare, semantic matching can be used to extract critical information from medical records, accelerating research and improving patient care. In the legal field, it can automate the review of contracts and other legal documents, saving time and reducing errors.

Beyond Document Processing

The power of Extractive AI isn’t limited to document processing. It can also be used for tasks like:

• Customer Service: Analyzing customer feedback to identify common issues and sentiment.
• Market Research: Identifying trends and patterns in large volumes of market data.
• Cybersecurity: Detecting threats by analyzing log files and other security data.

The Enterprise AI Opportunity

The AI revolution is here, and it’s not just about generating flashy new content. The real value for enterprises may lie in the less glamorous but equally transformative power of Extractive AI. Don’t get me wrong, I’m as excited as anyone about the possibilities of Generative AI. But for now, let’s not overlook the unsung hero of enterprise AI: Extractive AI and the power of semantic matching.

For enterprise leaders who are eager to harness the power of AI but are wary of the risks, Extractive AI presents a compelling alternative. It offers the potential for significant efficiency gains and cost reductions, without the same level of risk associated with Generative AI.

If you’re ready to explore the possibilities of Extractive AI for your organization, evolv Consulting is here to help. We have the expertise and experience to help you identify the right use cases and implement the right solutions.