Artificial intelligence (AI) has been around for about 7 decades, constantly sprouting advanced concepts like machine learning (ML), robotics, natural language learning (NLP), computer vision, deep learning, and sentiment analysis, among others, over time.

All these powerful AI technologies have helped businesses dive into market trends and peek into customers’ brains to make accurate, mission-critical decisions.

Cut to November 2022, generative AI (GenAI) stormed onto the scene. It’s been just about 2 years, and answer engines like ChatGPT, Gemini, and Perplexity have already become central to boardroom discussions and everyday workflows.

Read the latest tech news, and you’ll find GenAI hogging more than half of them. 

Of course, GenAI has been a welcome addition to the business ecosystem. You use it to brainstorm specific topics and ideas, conduct quick research, generate stunning images, and convert lengthy content into easy-to-understand summaries.

But, there’s one area where GenAI has truly shone for businesses, where traditional AI technologies have somewhat struggled: Making sense of the unstructured data.

Buried Insights: The Massive Challenge of Unstructured Data

Every organization produces tons of unstructured data—say, meeting notes, emails, chat logs, customer-facing calls, PDFs, customer reviews, and product roadmaps. Unlike structured data, these insights don’t fit nicely into rows and columns, making them difficult to store, search, and analyze.

For years, businesses have turned to traditional AI solutions to house customer and industry-related information. While many captured it, few managed it well or made sense of it. That said, employees have had to dig for relevance and meaning from unstructured data and consistently update the systems. That was not only cumbersome but also created minimal value.

Imagine this: Valuable insights from team meetings scattered in meeting docs, emails, or even personal notebooks. Key conversations are siloed in call recordings, proposals, presentations, or support logs because employees can’t understand the key concepts well. All this crucial data remains buried in systems and documents that could illuminate strategic business needs.

So, what’s so big deal about it?

Here’s a quick reality check: In 2022, unstructured data made up a whopping 90% of the overall organizational data—only 10% was structured.

And get this: the world will store a staggering 200 zettabytes (ZB) of data by 2025.

This screams a massive gap in how businesses use data, leading to missed opportunities, frustrated customers, and business strategies that simply fall flat. 

Generative AI To The Rescue!

Organizations are swimming in data. While this “messy” data hides incredible insights, unlocking its real value is no cup of tea.

Fortunately, generative AI (GenAI) has changed all of that. Now, businesses can use unstructured data in countless ways, inventing new use cases every single day.

So, How Exactly Does Generative AI Help Here?

GenAI feeds on a huge corpus of data to learn and interpret how we humans communicate. Thanks to this immense training, coupled with its existing capabilities, it analyzes the context—tone, meaning, and intent—within the data. And that too within a few minutes.

This cutting-edge capability opens a plethora of use cases for your business:

• Quick Summaries: Get crisp, clear takeaways from lengthy reports, meetings, audios/videos, or feedback.
• Key Insights: Easily pull out important terms, keywords, topics, and subtopics from large bodies of text like news articles and research papers.
• Smart Organization: Automatically sort transcripts, reviews, or responses into predefined categories based on sentiment and intent.
• Spotting Trends: Analyze customer-facing conversations to find gaps in your processes, predict what they’ll need, and explore growth avenues. 
• Understanding Emotions: Find out how customers actually feel from their feedback—rant or compliment—across social media and news aggregator platforms.
• Personalized Experiences: Use captured insights to deliver at-scale personalized experiences to customers. 
• Creating Drafts: Turn raw notes from docs, meetings, and even handwritten scribbles into polished drafts.
• Breaking Down Silos: Organize scattered inputs from multiple teams into actionable outputs, keeping everyone on the same page.

Here are some examples:

Content Writing

• Unstructured data: Jotted-down notes and rough ideas.
• GenAI Output: Polished blog drafts, clear content outlines, and ready-to-publish social media posts.
• Example: You’re brainstorming with Gemini on a topic. You share your thoughts, notes, and ideas, and instruct Gemini to create a structured outline for a 1000-word article.

Digital Marketing

• Unstructured Data: Raw survey results, customer reviews, and campaign feedback.
• GenAI Output: Deeper insights, common customer questions (FAQ), and refined branding strategies.
• Example: You instruct ChatGPT to find out why people love your customer service software based on the reviews (squeezed out from a PDF file).

The Catch: Where Generative AI Still Needs Work

It’s not all that rosy with GenAI. Here’s why:

The “Messiness” Makes Mapping Difficult

Unstructured data has unlimited variants due to its non-formatted nature. Take invoices, for example. You think an invoice is just a list of items and prices, right? Ummm…No. That’s not how GenAI sees it.

If you get 50 invoices from your vendors, no two might look the same. They’ll have their own schemas. What’s more, every invoice has various types of discounts. Some are for buying a specific quantity, others for being a long-time customer, or even special deals for specific products.

These aren’t just a simple “Discount: 10%.” The generative engine needs to understand why there’s a discount and how the vendor has calculated it. This often involves reading the surrounding text and understanding the context.

So, automatically pulling out these insights becomes one hell of a task than just looking for a simple “total.”

You’ll need to consistently and accurately map them to a common schema (most notably JSON schema) every single time. In other words, you have to enter very detailed instructions—sometimes hundreds of lines long.

That’s a lot of business logic and human expertise.

Still In Its Early Days

GenAI bridges the gap between messy data and the neat, structured systems businesses use. But let’s not double down on it. Right now, it’s still slow, not worth the money, and only handles small data chunks at a time. This is a challenge, even for small businesses, which generate data in terabytes (TB).

Plus, GenAI has much smaller context windows than the enormous volumes organizations need to sift through regularly.

Time to Dig Into The Underutilized Asset

Data is the fuel that drives AI engines. But that tank has mostly been empty because an enormous portion of enterprise data is unstructured—and for too long, it has sat largely untapped. Most businesses couldn’t figure out how to “fill the tank.”

Thanks to GenAI, an unexploited treasure trove is now up for grabs, with more and more organizations making it a crucial component of innovative and analytical enterprise applications. Tap into this incredible opportunity before that ship sails!

That said, unstructured data boasts varied complexity—a wide-ranging spectrum. At its lower end, traditional AI solutions work pretty accurately and without hurting the bank balance. GenAI is yet to tick that checkbox.

But, for workflows that demand human expertise due to the complexity and the unlimited data variance, GenAI is a promising technology to automate such agentic systems and free humans.

So, the question is: are you ready to harness more of your enterprise data, and especially that rich human-generated unstructured goldmine, as you implement AI-enabled systems? The answer should be a big fat “Yes.”