Author: Craig Woolard

Companies are increasing their investments in AI due to the influence of ChatGPT. In this Fireside Chat, three industry experts led an in-depth discussion on the potential impact of AI on how we work and operate enterprises.

The panelists discussed the need to process unstructured data using enterprise-ready AI models, such as intelligent document processing (IDP), vs. popular generative AI tools known to compromise data security.

Jun 21, 2023 by Craig Woolard

If you are exploring enterprise-ready automation technologies for unstructured data, you will find this fireside chat a must-watch. The three participants from Automation Hero were:

• Stefan Groschupf, Founder and CEO of Automation Hero
• Max Michel, Chief Product Officer
• Mark Stripp, Head of Global Sales

You can read the full bios of the speakers at the end of the article, but in the meantime, here’s a quick summary recapping the discussion between these three industry veterans:

• Automation Hero CEO Stefan Groschupf emphasized the increasing importance and availability of AI tools for businesses. The panelists discussed the widespread global use of artificial intelligence and highlighted recent advancements in AI models like Generative Pre-trained Transformers (commonly known as GPT). 
• They also explored the applications of AI in document processing and decision-making, highlighting the value of technology that can read and process documents with above-human accuracy, including the ability to “unlock” the business intelligence stuck in contracts, historical data, and more to improve business performance. 
• Concerns are raised about truthfulness and security with popular pre-trained large language models (LLMs), such as ChatGPT. The panelists stressed the need for organizations to adopt enterprise-ready AI tools quickly to stay competitive.

Watch the full recording below for more details.

Below, we captured the best questions that stood out and highlighted the key insights from the webinar:

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What are the potential economic impacts of AI?

Now that we can overcome nearly all of the obstacles that plagued the Big Data era through the revolutions in transformer technology, the time for large language models has arrived. According to Mark Stripp, ChatGPT, the fastest-growing app of all time, and Nvidia’s emergence as a trillion-dollar company are signs that AI is having its iPhone moment.  

“ChatGPT, the fastest-growing app of all time, and Nvidia’s emergence as a trillion-dollar company are signs that AI is having its iPhone moment.” 

Automation Hero CEO Stefan Groschupf set the scene by weighing in on AI’s potential business impacts over the next 20 years. According to a well known University of Oxford study, AI could eliminate 47% of US jobs over the next 20 years.

“47% of US jobs could be eliminated by AI over the next 20 years.”

On this, Stefan elaborated further, noting the potential impact AI will have on an organization’s ability to remain competitive in the future:

“I do really think that we are in an incredible time right now, and the landscape in how we do business will be disrupted within the next few years. I think that very, very quickly, the world will be divided by those that are using AI tools and those who don’t.”

Stefan highlighted the potential opportunities AI can bring to the table. According to the same Oxford University study: 

“77% of global companies are either using or exploring AI in their businesses, and this is only going to accelerate.”

Stefan also called attention to the wide adoption of AI tools since ChatGPT’s 2022 introduction, noting that more business leaders are increasingly recognizing the importance of AI as a business strategy:

“45% of executives reported that the publicity of ChatGPT has prompted them to increase AI investments.”

Every time a new disruptive technology emerges, there are those who thrive and those who are disrupted. On this topic, Stefan reminded viewers of what happened when electricity eventually replaced the steam engine. By 1920, electricity had already replaced steam as the major source of automotive power, and in 1929, electric motors represented almost 78% of the total capacity for automotive machinery. Stefan emphasized the importance for business leaders to act by incorporating enterprise-ready AI technology now or be left behind:

“I really think that we really need to rethink how we run companies. The intelligent  enterprise is a thing that needs to be achieved very quickly because, right now, AI is a competitive advantage, but very quickly, AI will be a competitive disadvantage to those who do not adopt it early enough…If you don’t have your AI story, your AI platform, and your AI use case together, you will be disrupted.”

What is the business value of technology that reads documents?

The panelists emphasized the value of technology that can read and process documents. When addressing this question, Stefan cited research conducted by the IDC, raising an interesting data point about the intelligence trapped inside business documents: 

“By 2025, worldwide data will exceed 175 zettabytes. With most of this information locked inside emails, texts, PDFs, and scanned documents, it poses a real barrier to automation and digital transformation.”  

IDC, “Intelligent Document Processing Demonstrates How AI Can Make a Practical Difference to Business Right Now”

The panel also discussed how the ability to “unlock” the hidden value in contracts and historical data can improve executive decision-making and business performance. However, without the help of enterprise-ready AI, the data we need to process is growing rapidly, causing significant challenges for organizations worldwide: 

“The data we need to process is only getting bigger…the problem really here…is that 80 percent of your enterprise data is unstructured, and that is the massive treasure chest.”

Enterprise-ready AI that understands documents at the semantic level is needed to automate critical document-centric processes. Stefan explained how enterprise-ready intelligent document processing (IDP) technology helps organizations realize the full potential of their data:

“If you think about all your business intelligence [assets]…your ERP investments, your ETL data integration data, and your warehouse investments…all of that is only scratching the surface. You didn’t even get to the core yet because that’s only 20 percent structured data. So the big question is, really, how can you quickly turn these [unstructured data] assets that you have into structured data, but then what kind of business use cases are unlocked around that?”

What exactly is generative AI?

Automation Hero’s Chief Product Officer, Max Michel, gave a high-level definition of generative AI. 

“Generative AI is basically the technology behind the tools that everybody is talking about right now, so the Midjourneys and the ChatGPTs that everybody wants to use. It takes an input, for instance, a text or a prompt…and then it generates the output, so it generates a new image…or an explanation, or a poem, or whatever you ask it to do. The special thing is…it can also do things in different styles, so you can ask it for a portrait to be very photorealistic or very abstract, and that’s basically up to the user…but there’s always an input, and then an output is generated based on what the AI has learned before.”

Max also explained how generative AI tools, which generate outputs based on inputs, contrast with discriminative AI, which provides specific answers based on given parameters:

“There’s also this other way to work with AI, and this would be the discriminative AI approach. With discriminative AI, you basically give some guidelines, so it can only group these few specific classifications, or it can only give you the answer based on the input that you gave it…this is really what you want to do in an enterprise setting…so discriminative AI is safer, but where I see the real power of generative AI is in creative work, of course.”

What is a large language model? 

Max explained that language models are a way to make languages computable for machines to understand. On the topic of human and computer interactions, he says:

“In general, technology is really good at stuff that we are bad at, like multiplying big numbers, but technology is really bad at things that are easy for us, such as talking, picking up a glass of water, and stuff like that. This is really difficult for a robotic arm to do, so basically, all of the AI researchers are really focusing on closing this gap.”

To close the gap so that technology can become even better at performing tasks that humans traditionally excel at, Max explained the concept of large language models further:

“Large language models are the bigger version of it, so if the language model has over one billion parameters, we call it a ‘large language model.’ It can learn not only the syntax and grammar of language but also the semantic relationship between words, and especially the very large language models can go further than that and learn concepts about the world and how to work with longer inputs, so yeah, it’s basically a tool to make language computable.”

What does “GPT” stand for?

For the uninitiated, Mark Stripp asked Max to explain what “GPT” in ChatGPT means:

“Generative Pre-trained Transformers. Transformers are basically the technology for an AI to keep context over a long string of words. That was a big challenge before. Generative AI models that generate output based on text input and pre-trainers meant that ChatGPT was pre-trained on web-scale data to learn concepts about the world.”

Stefan provided an interesting metaphor on the topic, describing AI research as “simulating the human brain”:

“The human brain has neurons that fire when they get inputs and we’re just trying to find a mathematical representation for that…there are a lot of learnings that come from the AI research for understanding how the human brain works, but technically we are trying to simulate mathematically how the brain works.”

What are AI “hallucinations”? 

Mark Stripp asked Max to explain the concept of AI hallucinations to the audience: 

“When we think about hallucinations in general, it’s always your brain or your neural network creating a different output than the real world, so there’s a difference between the real world and [the output] you’re seeing, so that’s how I would define hallucination.”

What is the issue with generative AI for documents?

“I think it’s really interesting and creative work if the AI can come up with really creative things and hallucinate really great images and stuff like that, but for an enterprise setting where you want to know the truth about what’s actually inside your document, you don’t want the AI to make things up. This is a big danger that I saw very early on with generative text summarization models and stuff like that.”

Max described his own research into generative AI models and their limitations: 

“For instance, I was working with a legal text generator that was trained on court orders to generate summaries of court orders, and I just tried it out on contracts, and it was always trying to create a victim and somebody who did something wrong, even though it was just a contract. Of course, really big generative AI’s have some guardrails about that, but there’s still the danger that the generative AI can hallucinate such information on top of what is actually in the document or in the input that you give it.”

What is the relationship between automation and AI, specifically with documents? 

Responding to Mark’s question about the relationship between AI and automation, Stefan said:

“Automation is the killer use case for AI and Enterprises. I think the AI use cases in the consumer world are very clear, and that’s challenging Google’s dominance in the information retrieval arena. But clearly, the automation use case is the golden goose for AI in enterprises. If we think about how an enterprise works, there’s an opportunity here to really get what every enterprise strives for: economic efficiency with low-cost, high-efficiency rates.”

Stefan described self-driving cars and daily life in Silicon Valley as examples of autonomous business processes: 

“In the Bay Area, San Francisco has given licenses to multiple self-driving car companies. We do see self-driving cars every single day; it’s creepy; nobody’s in there, but they are real. When the error rate is so low that we can truly license them in global cities, it’s not a question of whether we will have self-driving cars or not. So if we have self-driving cars, we will also have self-driving companies or business processes, and we will have them very quickly.” 

When it comes to documents and the need for AI to streamline efficient document-centric business processes, Stefan said:

“Companies run on documents. If we’re talking about autonomous companies, companies interact with other entities—either other companies or their customers—through documents. So, if we think about self-driving businesses or business processes, we need to think about how we get there and what it needs.”  

On what companies need, Stefan emphasized that companies need an operating system:

“We need an operating system for autonomous business processes, and we need it really quickly. If you want to peel that onion further, you need an all-tolerant platform that’s highly scalable and self-healing. One that is optimized for AI workloads that are quite different compared to traditional ETL or data warehousing workloads, with different AI capabilities available for less technical users to point and click. The key here is to quickly find technology that is purpose-built for these AI workloads, has these AI capabilities built-in, and allows people to almost curate business processes to get as close—and every day a little closer—to that autonomous business process. Those that do this will significantly outcompete the competition at scale, and with lower costs and higher customer satisfaction.”

Finally, as someone who discusses AI-driven automation technology daily with business leaders worldwide, Mark Stripp offers some concluding thoughts on AI’s business value:

“We’re seeing those impacts coming in from the changing nature of the workforce to some of the bigger trends out there that they need to address to handle productivity and so on. This all stems from the same underlying need: to make better access to information easier so you can do something with it.”

 What should we learn from this AI moment?

Circling back to the original point on the potential economic impacts of AI, Stefan weighed in on a controversial topic with a sense of optimism:

“I’m not sure all jobs will be gone. If we look into the history books, there will be a redistribution of jobs. So there will certainly be a lot of jobs that will change in that sense. So should people on the call be afraid to lose their job? Well, maybe not a job, but maybe the job. The information worker will either scale up very quickly and become a person that is able to use a new generation of tools such as AI, or you might be pushed out or not receive that salary raise that you were looking for. That will be the reality—the reality is always that there will be people who can adapt to the new technology, and they will be more valuable to organizations. It will be people who once said, ‘Ah, who needs computers? I can do this all on a piece of paper much faster than typing it in this computer thing.’ We saw how that went, right?” 

Unlock the intelligence in your documents with our AI-driven automation today

Regardless of the scale of your enterprise or use case, we can help you get there. Here are a few ways to get started:

• Speak with an expert — tell us about your specific use case.
• Get a personalized demo — schedule a demo, and our Heroes will get in touch!

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Who are the AI experts?

Stefan Groschupf, CEO, Automation Hero

When he started his previous company, Datameer, Stefan Groschupf saw firsthand the challenges financial institutions confronted as they navigated “unstructured” data. As founder and CEO of Automation Hero, Stefan focuses his data analytics experience on processing the diversity of data hiding in unstructured documents faster than ever using the most advanced and complete IDP platform.

Max Michel, Chief Product Officer, Automation Hero

As Chief Product Officer, Max Michel is focused on scaling the product organization by further delivering world-class innovations that address and delight customer needs while aligning with the company’s strategic goals. He also leads research and development in AI, machine learning, and data science. Previously, he was a data scientist at Datameer, where he built smart AI tools for big data analytics and helped the company leverage the power of machine learning to optimize sales operations.

He is a Bauhaus alumnus and holds a master’s degree in computer science and media. He spent four years publishing research on natural language processing and information retrieval.

Mark Stripp, Head of Global Sales, Automation Hero

As Head of Global Sales at Automation Hero, Mark Stripp develops sales teams across all regions and leads our GTM strategy, worldwide. He is passionate about technology and has an eye for strategies that help organizations accelerate their digital transformations. Mark brings nearly two decades of experience in multiple industries across the UK and North America.

Mark started his career at Automation Hero as Regional Sales Director of EMEA following a year of substantial growth and key wins, Mark is now leading the global go-to-market strategy. Prior to Automation Hero, Mark helped strategic clients in multiple sectors achieve their goals and digitally transform through optimizing IoT, business process automation, and integration capabilities.

In today’s digital age, data has become the lifeblood of modern business. Access to quality data is driving innovation, improving insight, and enhancing decision-making within the most agile organizations.

As more global enterprises harvest the vast amounts of data available, it’s crucial to understand the different types of data and the documents that store it.

Jun 21, 2023 by Craig Woolard

Structured, semi-structured, and unstructured data each possess distinct characteristics that impact how businesses operate. Each type can also impact decision-making differently, so having a deeper understanding of the nuances and implications is critical for modern business leaders to grasp.

This blog will explore the distinctions between structured, semi-structured, and unstructured data. We will explore each data type’s characteristics, challenges, and unique opportunities for businesses operating in the data-driven era. Finally, we will discuss how enterprises can use modern AI-driven intelligent document processing (IDP) to navigate the growth of unstructured data and “unlock” the intelligence “stuck” in documents.

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Structured data vs. semi-structured data vs. unstructured data

In the world of big data, information can be grouped into two distinct categories: “structured” and “less structured.” Structured data is information that follows predefined “rules” or “guidelines,” such as data points organized into a database. “Less structured” information is essentially everything else. 

“Semi-structured” and “unstructured” data is information that can be grouped into the category of “less structured.” When information is locked away in documents, for example, these terms can often be confusing because all documents (structured, semi-structured, and unstructured) are generally considered “unstructured data.” Having said that, structured data (as in data in a database) is not considered “unstructured” data. 

Unstructured data requires processing with AI before you can store or query its contents in structured formats like a database table or JSON. However, the key understanding to have is that “data,” no matter where it is stored, is your organization’s most valuable asset. There is valuable information in customer emails, social media posts, chat transcripts, competitor websites, invoices, contracts, and many other types of business documents. But the value within remains unobtainable if you can’t unlock the intelligence trapped in document-centric processes. 

If you can’t unlock the intelligence trapped in document-centric processes, then the value within remains unobtainable. 

Critical data points in business documents—whether “stuck” in structured, semi-structured, or unstructured documents—are the valuable intelligence your organization must “unlock.” Eliminating the “friction” in the way of potential business intelligence is the key to unlocking enhanced employee and stakeholder decision-making. In the next section, we’ll take a closer look at the differences between structured, semi-structured, and unstructured data types and investigate some common business use cases for each one. 

The goal of intelligent document processing is to convert structured, semi-structured, and unstructured documents into structured data. Therefore, we will begin with the most accessible data type: structured data. This will deepen your understanding of the unique challenges and opportunities for each data type and the documents that store them.

What is structured data?

For all practical purposes, everything people try to extract from documents is “structured data.”

Structured data is any information organized into a format that’s easy for traditional machines to find. Documents with structured data typically follow a “fixed” layout and usually contain fields with text, values, and other organized data—such as fixed forms, spreadsheets, or databases.

Examples of documents with structured data

Structured data typically includes text and other values stored in tables or databases. For a comprehensive list of the most common examples of structured data in business documents, check out the table below:

Structured data

Relational database tables

Tables with predefined columns and rows representing attributes or fields containing individual data, such as text, numerical values, or code.

Spreadsheets (e.g., Excel)

Machine-printed text and numerical values are organized into cells, rows, and columns for easy sorting, filtering, and analysis.

SQL tables

Structured Query Language (SQL) tables are used in relational databases to organize and store structured data, enabling efficient querying and manipulation.

ERP system data

Structured data is captured and stored in Enterprise Resource Planning (ERP) systems and covers various business processes such as sales, inventory, finance, and human resources.

Inventory management systems

Data captured in Inventory Management Systems include product codes, quantities, prices, and physical warehouse locations that are structured and organized for efficient tracking and management of inventory.

Financial transaction records

Structured financial data that includes dates, amounts, account numbers, and transaction types, which are essential for financial analysis and reporting.

Advantages of structured data

The biggest advantage of documents with structured layouts is that the information is already designed and optimized for quick and easy processing by computer systems. This also makes the data easily searchable with traditional “rules-based” automation tools. 

Because the data is highly organized and “structured,” it’s easier for legacy automation software like robotic process automation (RPA) to find critical data points. This also makes it easier for widely accessible but outdated technologies, such as legacy Optical Character Recognition (OCR), to scan and capture the data faster than manual human effort. 

Simply put, structured documents require less advanced technology, which can benefit organizations that are reliant on older heritage document processes and legacy data systems. 

However, these are not the only organizations that reap the benefits. Because structured documents are easier to process, nearly every industry can benefit from the value they store.

Here are some more strategic advantages of structured data:

• Structured data enables efficient data retrieval and querying.
• Structured data is easy to store, organize, and access for further processing.
• Structured data facilitates consistency and accuracy to ensure good data quality.

What are the limits of structured data?

Researchers at the IDC discovered that more than half of the documents enterprises process have structured layouts. However, most organizations cannot rely on structured data alone. 

For example, what about handwriting? Fixed forms and other structured documents typically have additional fields reserved for signatures and handwritten checkmarks. 

These documents may be easier for traditional automation tools to process, but they provide little flexibility. Plus, if they do contain unstructured elements, such as handwriting, then traditional “rules-based” technologies will struggle, which leads to critical information being missing.

When dealing with modern omnichannel data sources that lack a well-defined structure, such as social media, email, or even handwritten content, it’s crucial to understand the limits of traditional rules-based extraction techniques. 

Understanding these limitations can help you make a more informed decision about the strategies that unlock the business value in structured and less structured documents. 

What is semi-structured data?

Semi-structured data is information that does not exist in a structured or fixed format per se, such as a database or spreadsheet, but may have some attributes that make it easy to find. Some examples include XML documents, JSON files, and NoSQL databases. It’s also worth mentioning that there are no documents that contain “semi-structured data,” but there are plenty of examples of semi-structured documents. 

Examples of documents with semi-structured data

When we talk about “semi-structured” in the context of intelligent document processing, we are referring to documents where the same pieces of information are present across a variable layout. 

Documents with semi-structured data conform to a template, but the information layout is flexible and likely varies from document to document. In the context of IDP, we are not talking about documents that contain XML or JSON data. We are talking about business documents made up of plain text, tables, and other elements that are based on evolving templates. 

Since semi-structured documents do not have “fixed” or standardized layouts, organizations handling them may need help predicting where the information of interest is located. Examples of semi-structured documents include invoices, purchase orders, bill-of-materials (BOM), receipts, and loan applications. 

Check out the table below for a comprehensive list of semi-structured data examples:

NoSQL Databases

Flexible, schema-less data storage. Accommodates semi-structured or unstructured data. NoSQL databases, such as MongoDB, Cassandra, and CouchDB, store data in a flexible, schema-less format. This means that the data in NoSQL databases can have varying structures or fields across different documents or records. Semi-structured data in NoSQL databases can include diverse datasets such as user profiles, product catalogs, sensor data, social media feeds, and unstructured text documents.

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is commonly used for storing and transmitting structured data, making it suitable for representing complex objects, arrays, and various data types. It is widely used in web APIs, configuration files, and data storage. Examples of semi-structured data in JSON include user profiles, product catalogs, and social media posts.

XML (eXtensible Markup Language) is a markup language used for storing and transporting structured data. It uses tags to define elements and attributes to provide additional information about those elements. XML is versatile and allows for custom data structures. Semi-structured data in XML can include documents such as invoices, scientific data, and electronic health records. It is widely used for data interchange, configuration files, and data representation in various domains.

HTML (Hypertext Markup Language) is the standard markup language for creating web pages. HTML documents consist of tags and attributes that structure and present content on the web. HTML is primarily used for defining the structure and layout of web pages, the content within HTML documents can vary in structure, formatting, and data representation. Examples of semi-structured data in HTML include web scraping results, online articles, blog posts, and forum threads.

Advantages of semi-structured data

Semi-structured data bridges the gap between structured and unstructured data types. Understanding the distinct advantages can help business leaders appreciate the value of incorporating semi-structured data into their document processing workflows. 

Here are some of the key benefits of semi-structured data:

1. Data analysis

Semi-structured data often contains more contextual information than traditional structured data. Examples of this include metadata or tags that provide additional context that can improve the accuracy and relevance of data analysis.

2. Flexibility 

Semi-structured data is more flexible in data storage and data management scenarios compared to rigidly structured documents. Since semi-structured documents do not follow a strict, predefined template, incorporating new data types into existing databases or data pipelines is easier.

3. Scalability 

Semi-structured data is more scalable than structured data. Semi-structured data can be stored and processed using distributed computing systems in a variety of locations, such as existing on-prem databases, data lakes, and cloud storage. This flexibility and scalability enable greater agility to handle massive amounts of data.

4. Integration 

Semi-structured data easily integrates with other types of data, such as unstructured data, making it faster to combine and compare data from multiple sources.

Challenges of semi-structured data

While semi-structured data offers significant advantages, it also presents unique challenges that business leaders must consider when working with diverse data types. Understanding these challenges is essential to effectively managing and leveraging the potential of this data type. 

Here are the key challenges with semi-structured data:

1. Data extraction and integration 

Semi-structured data can be more challenging to extract and integrate than structured data. It often requires specialized tools such as intelligent document processing (IDP) with custom Context-aware OCR data extraction data extraction processes to capture the relevant information from various formats and sources. Because semi-structured documents have varying templates, their improved flexibility can make data integration more complex. Additional efforts and sophisticated AI tools are needed to harmonize and align the different data elements.

2. Data quality and consistency 

Ensuring data quality is much more demanding in semi-structured data environments. The lack of strict data models and schemas can lead to inconsistencies, duplications, and discrepancies that compromise the quality of the data. Cleaning and standardizing semi-structured data requires either extra attention from humans or sophisticated AI technology to address variations in data formats, missing fields, and inconsistent datasets.

What is unstructured data?

Unstructured data is information that is not organized into any particular format and may be completely “free-form.” Examples of unstructured data might include photos, videos, emails, books, social media posts, health records, and legal contracts.

Examples of documents with unstructured data

Unstructured documents are “unfixed” and do not follow a templated design, a fixed layout, or “rules.”

Gartner defines unstructured data as machine-printed or handwritten content lacking predefined rules or guidelines that computers traditionally use to identify. Unstructured data could be free-form text, such as the body of an email, or non-textual, such as a photo containing handwriting—but it could also exist in a non-relational database—such as NoSQL. 

The table below lists some of the most common examples of unstructured data in business documents:

Data Type

Handwritten text or documents that lack a predefined structure. Unstructured data includes personal notes, letters, signatures, and any other text produced by hand. Examples: handwritten letters, personal diaries, meeting minutes, and signatures.

Images, photographs, or graphics that contain handwriting, machine-printed text or important symbols. These data points are unstructured as they don’t adhere to a predefined format. Examples: logos, illustrations, and digital photos of handwritten notes or scanned images of business documents.

Text Documents

Unstructured textual data such as articles, books, emails, and reports. This data lacks a predefined structure and can vary in length, language, and formatting. Examples: PDF news articles, research papers saved as a Word document, and email messages.

Free-form Notes

Unstructured notes, memos, or annotations created by individuals. They can contain text, diagrams, sketches, or any other form of personal record-keeping. Examples: personal journals, meeting notes, brainstorming sessions.

Speech Transcripts

Transcriptions of spoken language, such as interview recordings or speech-to-text conversions. These data points capture spoken words, including dialogues, speeches, and conversations. Examples: interview transcripts, meeting minutes, voice recordings, and podcast episodes.

Unstructured email messages and their attachments. Emails can contain text, images, and various file attachments. They often exhibit varying structures and content types. Examples: personal emails, business correspondence, newsletters.

Social Media Posts

Unstructured data shared on social media platforms, encompassing text, images, videos, or a combination thereof. It includes user-generated content, hashtags, and engagement metrics. Examples: tweets, Facebook posts, Instagram stories.

Competitor Websites

Websites contain both structured and unstructured data. While the overall structure, using HTML and CSS, is semi-structured, the unstructured data within websites includes text content, tables, images, videos, and other media elements. Examples: web pages, blog sites, and online forums. Unstructured data extracted from websites using web scraping techniques includes product listings, customer reviews, and news headlines.

Sensor Data

Unstructured data captured by sensors or IoT devices, including raw measurements, signals, or streams of data. It often represents real-world phenomena and lacks a predefined structure. Examples: temperature readings, accelerometer data, air quality measurements.

Advantages of unstructured data

Understanding the distinct advantages of unstructured data can help business leaders “unlock” the business value stuck in unconventional documents. 

Here are some of the key advantages you get when you leverage unstructured data:

1. Unlocked business value 

You have a lot of unstructured data. Unstructured data is a business’s most valuable asset. It is the raw conceptual material and intellectual property that are a goldmine of untapped information for advanced analytics. The research estimates that unstructured data accounts for a whopping 80-90% of all new enterprise data. Yet only 18% of businesses are actually able to take advantage of it. The other 82% need help unlocking the business intelligence in this valuable resource. 

2. Intent and sentiment analysis

Unstructured data opens the door to advanced analytics techniques such as sentiment and intent analysis. For example, customers use email for all kinds of requests, and sometimes they have more than one request. It’s interesting to know that every 24 hours, there are more than 3 billion business emails sent and received worldwide. Intent classification detects customer intent in emails, and then it can automatically categorize emails based on intent to help automate email processing and triaging. 

Sentiment analysis provides insights into customer opinions and emotions, allowing organizations to gauge brand sentiment, improve customer satisfaction, and address issues promptly.

3. A big competitive advantage

By leveraging advanced analytics techniques, businesses are able to uncover patterns, trends, and relationships that might go unnoticed. These invaluable insights enable organizations to make data-driven decisions that give them a competitive edge.

Challenges with unstructured data

Your unstructured data offers the most significant competitive advantages of all the data types. But it also presents the widest set of challenges to enterprise organizations. Understanding the challenges is essential to unlocking the business value stuck inside unstructured documents.

Here are the key challenges involved with unlocking unstructured data:

1. Data extraction 

Since the information in unstructured documents does not follow a predictable pattern, their contents are entirely “hidden” from traditional data extraction methods. 

2. Handwriting recognition

Standalone Optical Character Recognition (OCR) is a ‘rules-based” technology that recognizes machine-printed letters, numbers, symbols, and even some handwriting. However, traditional OCR technology still struggles to accurately process handwriting. If your OCR cannot “see” unstructured handwritten signatures, tables, and other nuances accurately, someone must manually review the documents to extract the information. This stops your automation. 

3. Data accuracy and quality

OCR best suits high-quality scanned images with higher contrasts between texts and backgrounds. But if the text is splotchy or the scan is low-quality, OCR’s accuracy drops dramatically. Even with the best scanners and the best document quality, you only get 60% accuracy with traditional OCR at best. For enterprises looking to improve data processing speed, accuracy, and agility in unstable digital markets, rules-based approaches simply don’t cut it.

4. Data privacy and security

Unstructured data is the lifeblood of your business. It should never appear on public platforms. Proprietary data, such as chat transcripts, emails, multimedia content, patent applications, legal contracts, and even programming languages, can contain sensitive information with valuable intellectual property. Protecting data from unauthorized access, implementing proper encryption methods, and complying with relevant data protection regulations are essential.

5. Scalable storage concerns

Unstructured data,  typically large in volume and diverse in formats, presents challenges in storing, processing, and retrieving the data efficiently. Traditional storage systems and analytical tools may need help to handle the scale and complexity of unstructured data, requiring businesses to invest in scalable storage solutions and advanced data processing technologies.

6. Integration challenges

Unstructured data often need more standardized formats and schemas, which may make integration with existing systems more challenging. Integrating unstructured data with structured or semi-structured data sources requires careful data mapping, transformation, and consolidation techniques to ensure seamless data flow and compatibility across systems.

Conclusion

Structured data facilitates efficient analysis and supports business intelligence by enabling organizations to derive crucial insights quickly. 

Semi-structured data requires a more nuanced approach since businesses need to extract relevant information from various formats and sources before they can uncover potential insights. 

Unstructured data presents both a challenge and an opportunity. Unlocking its potential provides the business intelligence that will give agile enterprises a real competitive advantage. 

Structured data facilitates efficient analysis and supports business intelligence by enabling organizations to derive crucial insights quickly. 

But how can enterprises truly know whether or not data has potential value if there’s too much “friction” to unlock it? The answer is Intelligent Document Processing (IDP).

Transform your unstructured documents into structured data with our AI-driven automation platform today

Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner — cutting down manual tasks from 10 minutes to just two minutes per claim.

• Speak with an expert — tell us about your specific use case.
• Get a personalized demo — schedule a demo, and our Heroes will get in touch!

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By 2025, data-sphere experts forecast that worldwide data will exceed 175 zettabytes—and this trend is growing so fast that our global storage capacity cannot physically keep up with the payload.

This raises important questions: how much of this information should we store and save as “data”? But most importantly, how do enterprises “know” what data has value?

Jun 14, 2023 by Craig Woolard

In today’s digital age, unstructured data has become the lifeblood of modern business. Access to quality data is driving innovation, improving insights, and enhancing decision-making within the most agile organizations. But with the rise of unstructured data, modern organizations are faced with a new challenge: how to make sense of this massive data deluge.

This blog will answer the question, “What is unstructured data?” Then, we will examine the global challenges that modern enterprises are facing as they navigate the rapid growth of unstructured data and its unique opportunities.

Finally, we will discuss how data-driven enterprises can use next-gen AI-driven intelligent document processing (IDP) to “unlock” the business intelligence stuck in unstructured documents.

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What is unstructured data?

Gartner defines the data in unstructured documents as machine-printed or handwritten content lacking predefined “rules” or guidelines. Traditionally, computers have had to be programmed to follow rules in order to locate information of interest in structured databases and related formats. 

However, unstructured data that lacks predefined rules is much more common in the real world. Unstructured data could be free-form text, such as the body of an email, or non-textual, such as a photo containing handwriting. 

Unstructured data is also challenging to capture. Traditional “rules-based” approaches, such as the ones used in standalone Optical Character Recognition (OCR) and Robotic Process Automation (RPA), are limited when it comes to extracting data that lacks structure

For example, RPA “robots” are limited in their ability to learn from experience; the bots can only do what they are programmed to do and do not improve or adapt over time without human intervention.

Consequently, since legacy technologies like RPA can only handle rules-based, repetitive tasks, they struggle to extract information from unstructured documents, which leads to critical information being missing.

The global challenges of unstructured data

The IDC Global DataSphere analyzes global data trends and reports on the quantity of data created worldwide. It also looks at how much data is stored across various storage media. In 2020, IDC researchers determined that the world produced more than 64 zettabytes of data.

Since then, this global uptick in growth has skyrocketed so quickly that the amount of data we produce over the next five years is projected to outpace our global capacity to store it, meaning we save less of the data we create each year.

In 2020, IDC researchers determined that the world produced more than 64 zettabytes of data.

By 2025, data-sphere experts have forecast that worldwide data will exceed 175 zettabytes. This trend is growing so fast that our global storage capacity, physically, cannot keep up with the payload. In this current reality, it raises an important question: how much of this information should we store and save as “data”? 

…this global uptick in growth has skyrocketed so quickly that the amount of data we produce over the next five years is projected to outpace our global capacity to store it, meaning we save less of the data we create each year.

Since the cloud increasingly determines how we store, consume, and engage with information, the rate at which enterprises produce data is double that of consumers. While not all data created is mission-critical or needs to be saved, how can enterprises determine what data has value?

Understanding the growth of unstructured data within organizations

Big data experts estimate that unstructured data accounts for 90% of all new enterprise data. This trend reveals that unstructured data is growing 55-65% every year—a rate three times faster than the growth of structured data. Yet only 18% of organizations are taking advantage of unstructured information.

The reality is that the bulk of an organization’s data today is “unstructured.” That’s because modern customer information comes in many forms:

• emails
• text messages
• social media posts
• PDFs
• handwritten scanned documents, and more.

Without a method to mine valuable data from these documents, an organization is simply unable to adapt to changing conditions. So what about the other 82%? These organizations lack the technology to “unlock” business intelligence stuck in their most valuable resource: unstructured documents.

If you can’t unlock the business intelligence trapped in legacy document processes, then the value within remains unobtainable. 

The capability to unlock valuable intelligence is severely blocked within organizations with highly regulated document-centric processes, such as banking institutions. Older heritage document processes are a real barrier to digital transformation. If you can’t unlock the business intelligence trapped in legacy document processes, then the value within remains unobtainable. 

So what can be done about it?

How IDP overcomes the challenges of unstructured data

Intelligent document processing (IDP) is a type of business workflow automation that uses next-gen artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to read documents like humans. 

IDP brings modern AI/cognitive capabilities to workflow automation software, enabling organizations to reduce errors and improve the accuracy of their data. Many tasks, including data entry, automated document classification, and data extraction, can be successfully automated with IDP.

The main goal of IDP is to transform your organization’s semi-structured and unstructured documents into useful, structured data.

IDP can also integrate data with existing SAPs, ERPs, accounting software, databases, and legacy RPA tools—creating the most advanced solution for the global challenges of rapidly growing unstructured data. 

The main goal of IDP is to transform your organization’s semi-structured and unstructured documents into useful, structured data. No matter what documents your organization handles, IDP scans content and interprets context—along with the author’s intent—to streamline the entire document workflow with above-human accuracy.  

Since IDP aims to convert unstructured and semi-structured documents into structured data, intelligent document processing offers organizations the modern AI-driven automation technology they need to unlock the intelligence stuck inside any document. This leads to a wide range of business use cases, including invoice processing, contract automation, and automated email classification and processing systems.

Organizations that leverage next-gen IDP technology to transform unstructured data into accessible data that is easy to use will come with many competitive advantages—but the immediate benefits are:

• faster processing speeds
• more efficient business processes
• streamlined document management workflows
• improved data quality,
• enhanced decision-making

For a complete understanding of intelligent document processing and the use cases that can help your organization, check out our Guide to Intelligent Document Processing for more.

Conclusion

Unstructured data presents both a challenge and an opportunity. Unlocking its potential provides the business intelligence that will give agile enterprises a real competitive advantage. But how can enterprises know whether or not data has potential value if there’s too much “friction” to unlock it? The answer is Intelligent Document Processing (IDP).

Transform unstructured data into business value with our AI-driven automation platform today

Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner — cutting down manual tasks from 10 minutes to just two minutes per claim.

• Speak with an expert — tell us about your specific use case.
• Get a personalized demo — schedule a demo, and our Heroes will get in touch!

Every 24 hours, more than 3 billion business emails are sent and received around the world. Just let that sink in… By 2025, global data experts forecast daily email traffic will grow to over 4.5 billion emails.

Modern enterprises need an automated email classification and processing system to manage the overload of incoming customer emails and triage messages more than ever.

Jun 13, 2023 by Craig Woolard

How many emails does your team receive each day? Hundreds? Maybe thousands? How many emails include attachments of unstructured documents, such as PDFs, or even images of handwritten text? If emails continue growing steadily at a 4% rate yearly, global data experts forecast daily email traffic will grow to over 4.5 billion emails in 2025.

Global data experts forecast daily email traffic will grow to over 4.5 billion emails in 2025.

For many businesses, the volume of emails to manually read and sort gums up internal systems and leads to losses in productivity and revenue. As a result, an effective email processing system to manage modern omni-channel customer communications is needed more than ever.

What is email processing automation?

Email processing automation reads and handles emails just like a human would. It uses advanced artificial intelligence (AI) algorithms to automatically process incoming and outgoing emails—helping companies respond quickly with the right message at the right time. 

This specific type of business process automation (BPA) leverages natural language processing (NLP) and other AI technologies to automate the routine tasks of email management. Some common examples of email processing automation include:

• Reading, classifying, and routing emails to the appropriate individual or team.
• Extracting data from the body of emails or their attachments.
• Automating responses and pulling in additional information if needed.
• Any combination of the above.

Automating email processing can help teams manage large volumes of incoming emails, streamlining communication and workflows. But what about the essential information “locked away” in attachments containing “hard-to-reach” unstructured data?

How does email processing automation work?

These days, email attachments run the gamut of unstructured documents. They include critical information stuck inside unstructured PDFs, semi-structured forms, attached spreadsheets, infographics, presentation slides, text files, and even photos of handwritten text. So how do businesses extract essential data from these unstructured email attachments?

How do businesses extract essential data from these unstructured email attachments?

A well-designed email processing platform can help companies completely automate these emails, so employees are free to focus on more mission-critical opportunities. Automated email processing consists of several parts:

1. Unlocks critical data in emails and attachments.
2. Understands intent and categorizes emails.
3. Automates email triaging to the right department.
4. Streamlines customer communication with the appropriate response.
5. Transforms unstructured data into action.

Why is automated email processing more efficient?

Let’s say you’re a shipping company receiving hundreds of customer emails daily with requests—from price quotes to package statuses. Those emails may come in with attached jpegs or even relevant information that could be related to multiple requests—making it hard to build rules that sort things reliably.

Figuring out who should see each type of information and responding with the right message takes hours of precious time from the shipping company’s staff.

Figuring out who should see each type of information and responding with the right message takes hours of precious time from the shipping company’s staff. Furthermore, it slows down response times when customers know their requests can be answered quickly by the competition with first-class round-the-clock, 24/7 support.

Streamlines internal processes

Email overload significantly reduces productivity, morale, and company revenue. Industries worldwide are dealing with new complicating factors that have sent email volumes soaring, and the rapid growth in email traffic is far-reaching. Freeing your employees from these distractions is critical to maintaining high productivity levels. Email processing automation and AI can help you streamline so everyone wins.

Email processing automation and AI can help you streamline so everyone wins.

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Efficiency boost equals more sales

Streamlined business processes directly impact your team’s ability to focus on higher-value tasks, such as generating more revenue. For example, a claims adjuster juggles dozens of insurance claims when a natural disaster strikes. Automatically classifying incoming emails with photos of receipts and other unstructured policyholder data boosts efficiency when time is critical. 

Streamlined business processes directly impact your team’s ability to focus on higher-value tasks, such as generating more revenue.

Likewise, sending incoming RFP emails to the correct departments during the initial stage of the Request For Proposal process cuts down the work of generating price quotes—improving competitiveness against new industry players who have digital-only processes. 

Enhances the customer experience

Customer service teams face an ever-increasing amount of pricing requests, address changes, complaints, and many other inquiries. Automating email classification with intent detection cuts down on the emails your team needs to manually review and reply to so customers get help faster. The reduced workload also saves time for higher-value tasks that improve the customer experience.  

…intent detection cuts down on the emails your team needs to manually review and reply to so customers get help faster.

How Automation Hero can help you with email processing

Automation Hero’s Hero Platform_ is an end-to-end intelligent document processing (IDP) platform with native, next-gen AI and enterprise-grade email processing built-in. Hero Platform_  uses advanced AI models for each step of the automated email process. 

Hero Platform_ understands, classifies, and manages any document—including incoming emails and attached files—such as scanned forms with handwritten text locked away inside invoices or pictures of receipts. 

Additionally, our platform can even triage emails and extract essential information from the email content directly, along with the data locked inside unstructured email attachments.

Say hello to Hero Platform_

When a new email comes in, Hero Platform_ uses a classification AI model to classify the intent of the message. Is the email a quote request, a shipment update, or a request for support? Hero Platform_ detects the intent and intelligently sorts the email into an appropriate category. 

Hero Platform_ then automatically collects key information from the message—such as the customer’s name, order details, or key dates mentioned, and generates a summary of the issue. 

The captured data is then used to verify the relevant customer account and order information stored in an internal database, saving your team time and effort. The extracted information could then go into a support ticket system that creates a new case—allowing your staff to focus on providing top-notch customer service instead of shuffling digital envelopes.

Industry-leading AI reads, classifies, & extracts data from emails 

Hero Platform_ has an advanced AI model that scans and understands the intent of emails. Our platform can automate fast responses or route emails to the proper departments based on intent. Hero Platform_ then automatically sorts and selects emails and can even send an automated response if additional clarification or supporting documents are needed. 

Email attachments come in various formats—from PDFs to printed receipts to scanned paper images of handwritten text and jpeg files. Our AI-powered Context-aware OCR extracts the critical information from these documents in seconds.

Powered by industry-leading AI, our all-in-one Hero Platform_ comes equipped with the most accurate OCR on the market that reads documents like a human—especially handwriting. 

End-to-end automation

Hero Platform_ is an end-to-end intelligent document processing platform that reads documents—even handwriting and tables—the same way as humans. It uses advanced AI to extract essential data locked away in emails and transforms it into business value. Once unlocked, Hero Platform_ can even read the data in multiple languages, allowing other departments operating in different countries to reuse it. 

Once unlocked, Hero Platform_ can even read the data in multiple languages, allowing other departments operating in different countries to reuse it. 

Likewise, IDP can enter the data into accounting software or an internal database—such as an ERP, ECM, or CRM system. With the data extracted, Automation Hero can look up information in another system, verify it, and automatically compose and send replies to customers with the appropriate responses. 

Accelerate responses—with a human touch

Plus, humans can interact with the automation as needed. Hero Platform_’s attended automation flags all remaining emails for employees to review using a human-in-the-loop interface for quick batch review. Our no-code environment is highly configurable and provides all relevant information employees need to make decisions quickly.

With Automation Hero, no more emails can slip through the cracks. So say goodbye to mishandled requests and hello to human-in-the-loop.

Industry-leading intent detection—built in

The faster your teams can detect and respond to leads that might signal an intent to purchase, the greater the odds of making a sale. Hero Platform_ automatically extracts key data points from the email—such as the customer’s name, order details, or dates—and generates a summary of the issue. Our flexible AI intent classifiers rapidly identify interested clients, equipping sales teams with the valuable business intelligence they need to close a deal.

The faster your teams can detect and respond to leads that might signal an intent to purchase, the greater the odds of making a sale.

Automatic email routing and triaging

Email triage—quickly sorting through a clogged inbox—is daunting no matter what business you’re in. What is the email’s main topic? What type of request is the customer making? Does the information need to be validated by an internal database or an employee? With an accurate understanding of the sender’s intent, these are instantly turned into actions.

Unclog the shared inbox and say goodbye to mishandled requests for good.

Unclog the shared inbox and say goodbye to mishandled requests for good. Our automated email classification system instantly triages emails with above-human accuracy, so employees aren’t constantly in over their heads.

Email processing automation case study 

A large global logistics and shipping company wanted to improve its customer experience when responding to price quote requests. For years, the company previously handled these requests manually, but as requests shifted to fax, email, and other digital communications, the company was swamped. 

Due to the variety of unstructured data contained in these requests, the company needed a way to keep up with the volume of inquiries from multiple channels. Using Hero Platform_, the company automated responses for 60% of the incoming inquiries, which reduced the workload by 80%—streamlining faster responses that increased customer satisfaction. 

Automation and AI can help you streamline too. For example, see how we helped this global logistics company streamline email processing down to mere seconds in response time—all with Hero Platform_.

Unclog the shared inbox with an AI-driven email classification and processing system today

Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner—cutting down manual tasks from 10 minutes to just two minutes per claim.

• Speak with an expert — tell us about your specific use case.
• Get a personalized demo — schedule a demo, and our Heroes will get in touch!

In the world of enterprise data management, business documents come in all shapes and sizes—each with its own characteristics and challenges.

But the differences between structured, semi-structured, and unstructured documents cause a world of confusion. This blog article unravels the distinctions between these document types.

Jun 01, 2023 by Craig Woolard

Understanding the differences between “structured,” “semi-structured,” and “unstructured” documents is crucial for effective information processing and decision-making.  

In this article, we delve into the distinctions among these document types, shedding light on their unique attributes and the implications they have for businesses. You will also learn that “less structured” documents require significantly more advanced technology.

That’s where IDP comes in. With Intelligent Document Processing (IDP), businesses can at last “unlock” the business value “stuck” in unstructured documents. Gain a clear understanding of the many types of documents and harness the power of structured data. Let’s dive in.

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“Structured” vs. “less structured” documents

Essential data can be organized in a variety of ways, and this presents all kinds of challenges. 

For one, the terminology and industry jargon cause a lot of confusion. To simplify it, there are two ways information can be organized in a document: “structured” or “less structured.” The table below breaks down the most common business documents by type:

Structured documents

Semi-structured documents

Unstructured documents

Printed forms

Social Media posts

Tax Documents

For example, there’s a lot of “unstructured data” packed away in business contracts.

You might be unable to stick a contract into a database and run queries against it. But you can use AI to extract data points—such as “supplier name,” “supplier address,” “contract termination date,” and other pieces of critical information—from those documents.

But how can enterprises know if documents have potential value or not? That’s the other challenge to address with business documents. 

The challenges with structured, semi-structured and unstructured documents

These terms can often be confusing because all documents (structured, semi-structured, and unstructured) are generally considered “unstructured data.”

They all require processing with AI before you can store or query their contents in structured formats. Let’s quickly examine the major challenges businesses confront with each document type:

1. Structured documents

With “structured” documents, such as a fixed form, you already know what you will find and where to find it within the document. 

You already know what you will find and where to find it within the document. 

2. Semi-structured documents

On the other hand, the layout is a lot more flexible and prone to change in a “semi-structured” document, such as an invoice. Even though you may already know what you will find in a semi-structured document, you will have no idea where to find it or how to predict the location of the essential data. 

Even though you may already know what you will find…you will have no idea where to find it or how to predict the location of the essential data. 

3. Unstructured documents

To make matters worse, “unstructured” documents, such as emails, social media posts, and contracts, are completely “free-form” and massive in volume. These documents are the most challenging to predict. You have no idea what you will find or where to find it. Just by sheer volume, unstructured documents need rich language understanding to “unlock” the potential business intelligence they likely have.  

You have no idea what you will find or where to find it.

How does intelligent document processing overcome these challenges?

In intelligent document processing (IDP), documents are categorized based on their structure (or lack thereof) and by the predictability of their content. 

Flexible documents require flexible AI models to understand and extract the data. Fixed forms are easy to predict and require less flexible AI. The more flexible documents you have, the more sophisticated AI you need.

No matter what type of document your organization processes, IDP can help you unlock the business value in any document. In the next section, we’ll take a closer look at the differences between structured, semi-structured, and unstructured documents.

1. Examples of “structured” documents

Nearly every industry heavily relies on structured documents in one form or another. 

These include documents containing machine-printed text designed to be scanned by computers, such as identification records, passports, ID cards, and driver’s licenses.  

Structured documents also typically involve fixed forms, including tax forms, questionnaires, surveys, tests, medical records, and insurance forms. For a comprehensive list of the most common examples of structured data in business documents, check out the table below:

Document type

Structured documents designed for collecting specific information, often used for applications or surveys.

Official identification cards issued to individuals, containing structured data such as name and photo.

Travel documents issued by governments, containing structured data including personal and travel details.

Documents used for filing taxes, containing structured fields for reporting income, deductions, and more.

Structured documents containing the patient’s medical history, diagnoses, treatments, and other healthcare data.

Simply put, structured documents require less advanced technology, which can benefit organizations that are reliant on older heritage document processes and legacy data systems. 

However, these are not the only organizations that reap the benefits. Because structured documents are easier to process, nearly every industry can benefit from the value they store.

It’s also worth mentioning that even with very structured documents, there are still many areas where AI can help. Those include detecting the fixed elements in a larger page (like finding the ID card scan as part of a larger page), image cleanup, and processing difficult input such as handwriting.

2. Examples of “semi-structured” documents

Since semi-structured documents do not have “fixed” or standardized layouts, organizations handling them may need help predicting where the information of interest is located. Examples of semi-structured documents include invoices, purchase orders, bill-of-materials (BOM), receipts, and loan applications. 

Check out the table below for a comprehensive list of common business documents with semi-structured data:

Document type

Invoices often contain structured fields along with unstructured information. They typically include structured data such as invoice number, date, and amount, while also incorporating unstructured information such as item descriptions and billing notes.

Purchase orders typically have structured fields for order number, quantity, and price, but may also include unstructured information like special instructions or terms and conditions.

Surveys may have predefined questionnaires but allow for open-ended responses. They provide structured data through predefined questions and response options, along with unstructured data through customers’ open-ended feedback and comments.

Financial statements consist of structured financial data, such as balance sheets and income statements, but may also include narrative sections with qualitative information that provide insights into the financial performance of a company.

Product Safety Data Sheets (SDS) provide information about the hazards, handling, storage, and emergency response measures for specific chemical products. While SDS documents have a defined structure, the content within each section can vary based on the specific chemical and regulatory requirements. This allows manufacturers to accommodate the varying properties and hazards of different chemical substances.

Formal documents providing detailed information about the quality and composition of a product or material, typically including structured data such as batch/lot numbers, test results, specifications, and analytical information.

Legal contracts have structured sections for parties, terms, and conditions, but may also contain unstructured clauses and appendices. Contracts include structured data with legal obligations and agreements between parties, while unstructured clauses and appendices provide specific details, exceptions, or additional terms that may vary from contract to contract.

3. Examples of “unstructured” documents

Unstructured documents are “unfixed” and do not follow a templated design, a fixed layout, or “rules.”

The table below lists some of the most common business documents with unstructured data:

Document type

Emails contain unstructured data such as free-form text conversations, attachments, and metadata.

Customer feedback documents, including surveys, comments, and reviews, contain unstructured data expressing opinions, suggestions, and experiences shared by customers.

Support tickets in customer service systems often contain unstructured data in the form of customer inquiries, problem descriptions, and support agent responses.

Social media messages, including direct and private messages, contain unstructured data consisting of customer inquiries, feedback, complaints, and other interactions with a business on social media platforms.

Voice transcripts provide unstructured data by converting recorded phone calls or voicemail messages into text, capturing customer inquiries, sales discussions, or support interactions in a text-based form.

How IDP transforms any document into structured data

Legacy technologies like robotic process automation (RPA) and optical character recognition (OCR) can find the same data points in the same place every time (structured documents), but they struggle to do anything else. 

When it comes to more flexible semi-structured and unstructured documents, RPA and OCR’s “brittle” interfaces are incapable of “seeing” the data points—so they simply break down, which stops your automation dead in its tracks. 

Intelligent document processing (IDP) is a type of workflow automation that goes beyond the limitations of traditional RPA and OCR. It uses next-gen artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to “read” documents like a human and transform unstructured documents into structured data.

Flexible documents require flexible AI models to understand and extract the data. The more flexible documents you have, the more sophisticated AI you need.

IDP can also integrate structured data with existing software, databases, and legacy RPA tools—creating the most advanced hyper-automation solution for the global challenges of rapidly growing unstructured data. 

Intelligent document processing to the rescue

No matter which business document your organization handles, IDP scans content and interprets context—along with the author’s intent—to streamline the entire document workflow with above-human accuracy. Here are the key IDP use cases to know about:

1. Different file formats

Business documents come in every format, including paper forms, PDFs, images, and emails. The AI deployed in intelligent document processing can read all of them—with a clear understanding of every word—and with greater accuracy and speed than traditional automation software can offer. 

2. Scanned documents

Companies around the globe struggle to extract information from scanned PDFs. This is especially the case with handwriting. However, IDP can intelligently classify, capture, and process stockpiles of business-critical data locked away in archives of scanned unstructured documents, regardless of the quality of the scan, file type, language, or handwriting legibility.

3. Handwriting & signatures

Optical character recognition (OCR) recognizes characters, letters, and numbers, regardless of the font. But as a standalone technology, OCR does not recognize handwriting accurately. But, when integrated with AI, cursive and signature detection improve significantly with IDP.

4. Invoice processing 

Every vendor uses a unique invoice, and companies receive thousands of invoices with different requirements—each with critical data that must be manually keyed into an AP system. An invoice automation platform with sophisticated AI, can analyze invoices, find key data points, and automatically update existing systems. 

5. Email processing

Emails have some fixed properties, so they could be considered semi-structured documents. But the valuable information is contained in the body of the email and in the attachments. This essential data is generally unstructured. An email automation platform with intent analysis can analyze incoming emails, detect the sender’s intent, extract the data, automatically update relevant systems, and notify the sender about the outcome. 

6. Contract automation

Contracts are data-packed documents with critical business intelligence. Automation Hero’s IDP unlocks the business value in contracts with a certified, secure environment driven by industry-leading AI. Only Automation Hero’s sophisticated AI recognizes styles of handwriting and goes beyond plain text to extract all critical data points stuck in contracts in just seconds.

Conclusion

Structured data facilitates efficient analysis and supports business intelligence by enabling organizations to derive crucial insights quickly.

The real challenge is extracting the unstructured information and making it actionable. This is the only way to discover the intelligence locked away in unstructured business documents.

Structured data facilitates efficient analysis and supports business intelligence by enabling organizations to derive crucial insights quickly. 

How can enterprises know whether or not data has potential value if there’s too much “friction” to unlock it? The answer is Intelligent Document Processing (IDP).

Unlock the business intelligence in your documents with an AI-driven automation platform now

Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner — cutting down manual tasks from 10 minutes to just two minutes per claim.

• Speak with an expert — tell us about your specific use case.
• Get a personalized demo — schedule a demo, and our Heroes will get in touch!

“Nothing is certain except death and taxes.”

In the business world, that phrase might as well include invoice processing. After all, the basis of every professional relationship is the transaction. If that gets bottlenecked, things can turn sour, potentially even damaging the company’s reputation.

Apr 06, 2023 by Craig Woolard

Invoice automation and modern AI have combined forces to revolutionize the accounts payable process. At Automation Hero, we help multinational enterprises free themselves from mountains of data-packed invoices so that they can pay their bills lightning-fast. After all, where would the most successful companies be without the support of vendors?

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What are the biggest problems in invoice processing?

Paying bills on time is often seen as a reflection of a brand’s image, and most brands want their invoices to be unique. However, the biggest problem in invoice processing is simple: everyone’s invoices look different. 

Before we get into how automated invoice processing works, let’s examine the holistic pain points enterprises have with the accounts payable process:

1. Invoices are semi-structured documents

Invoices are semi-structured documents, meaning they do not have a standardized layout. Invoices have the same fields — think date, the amount due, and invoice number — but layouts change vendor-to-vendor. Therefore, organizations handling these documents may need help predicting where the information of interest is located on each incoming invoice. 

2. Manual invoice processing is expensive.

Manually processing invoices takes a lot of time and staffing. Many organizations have seasonal increases in invoices that may even require additional staff. Each invoice requires someone to carefully read it, search through records to validate it’s a legitimate bill, confirm it’s for the correct amount, and input the data from the invoice. Additionally, a second person must usually be involved when approving and remitting payments. 

3. AP processes require people

The next problem in invoice processing is more complex: invoices need people to review, validate, and approve purchase orders. Every organization has decision-makers with the authority to approve or deny invoices. Procurement managers, accounting departments, CFOs, and VPs are often involved in this complicated workflow.

4. Complicated workflows

Many companies receive invoices with different requirements, values, and data that must be manually keyed into an ERP (Enterprise Resource Planning) or another accounting system and pass through four-eye or even six-eye review audits. Unfortunately, there’s no “one-size-fits-all” solution for processing invoices. As a complex workflow, accounts payable processes need key people involved in the auditing process with multiple touchpoints between different systems, databases, and departments. 

5. More room for human error

Processing each invoice requires full-time departments, but whenever humans have to perform detailed, repetitive work, there’s always the potential for costly mistakes. It can be easy to misread a number or miss a step in manual review processes, resulting in approvals that never should have been granted or misfiled documents that throw off an audit. Mistakes are expensive to fix. Any time a mistake is made, it takes additional time, staff, and expensive resources to correct it.

6. Bottlenecks delay critical payments

When human error and labor-intensive processes combine, the possibilities of delays are amplified, which might not be something your organization can afford. After all, the basis of every professional relationship is the transaction. When that gets bottlenecked, things can quickly turn sour. For example, with manual invoice processing, you risk losing vendors that offer great prices and services simply because of a bottleneck that prevented someone from paying a bill on time. Late invoices can incur penalties and impact corporate relationships, causing companies to lose discounts.

7. Poor visibility

When invoices are processed manually, records of every step are rarely stored in a central location (assuming they’re stored at all). Records management systems are expensive to maintain. Filing cabinet systems consume physical space, and the documents are difficult to retrieve, making it more challenging to know where mistakes occurred during an audit and who might be responsible for making them. Even more, it can be difficult to see opportunities for streamlining the process since it’s hard to track how long each step takes.

8. No centralized workflow

Incoming invoices come through various channels and in all kinds of different formats. Whether snail-mailed as paper documents, emailed as PDFs, or uploaded to an online vendor portal, incoming invoices come in various forms. With paper invoices still involved, additional staff must manually track and cross-check each one. 

Accounting is complicated, which makes the accounts payable process even more complex for large enterprises looking to pay their bills efficiently. All of the various touch points must work cohesively, and adhere to each type of invoice’s workflow requirement.

What are the different types of invoices?

Depending on your industry, the invoices you receive can vary widely depending on the goods or services you purchase. However, the following are the most common invoices that large multinational enterprises receive: 

• Standard Invoices: lists the goods or services provided, their prices, and the total amount due.

• Recurring Invoices: Many large enterprises have ongoing contracts or subscriptions for services that require recurring invoices.

• Purchase Order Invoices: This type of invoice is commonly used in B2B transactions when the buyer has issued a purchase order and received it from the seller.

• Progress Invoices: This invoice type is common in construction projects or other long-term contracts with ongoing projects. These show the amount due for work completed to date. 

• Credit Invoices: This type of invoice is issued when a customer returns goods or cancels services, and credit is due. Large enterprises may receive credit invoices from vendors due to their transaction volume.

• Debit invoices: A debit invoice increases the amount due from a customer, typically when additional goods or services are provided. Large enterprises may receive debit invoices if they request other services or products.

As we can see, there’s no “one-size-fits-all” approach to accounts payable processes. While there are many variations and types, in general, invoices can be grouped into two categories:

1. Invoices associated with a company’s internal request (PO order).

• These invoices have a purchase order number.

2. Invoices not associated with a request (non-PO).

• These may not have a PO order.

It’s common for companies to have one approach for PO invoices and a different approach for non-PO invoices. They can be sent via email, mail, fax, or EDI. Regardless of how incoming invoices come through, paying bills on time is fundamental to running almost every type of company. What if there was a way to eliminate the busy work? 

That’s where invoice process automation comes in, which we will cover in the next section.

What is automated invoice processing?

Invoice processing automation involves enterprise-grade technology and software to streamline the handling, processing, and management of incoming bills from arrival to payment. 

Invoice automation can help a company’s accounts payable department streamline AP processes and invoice management procedures by automatically paying supplier/vendor invoices on time. 

The technology behind invoice processing automation usually starts by extracting invoice data from incoming documents and inputting the data into an ERP or accounts payable system, enabling payments to be made in minutes. 

What is invoice automation software?

Invoice automation solutions may involve intelligent document processing (IDP), robotic process automation (RPA) software, and artificial intelligence (AI) to automate the manual data entry tasks associated with the bill processing cycle — including reviewing, approving, remitting, and matching invoice data with the data from purchase orders and receipts.

How does automated invoicing work?

The invoice submission and approval process typically follows a specific series of steps:

1. Document capture (the receipt of documents via email or scanned paper documents)

• Incoming invoices might arrive in a specific email folder as attachments (JPEG, PDF, EDI, PNG, etc).

2. Document classification (Is this document an invoice?)

• AI intent classification models can identify which emails have attached invoices, automatically sort those emails, triage them, or route invoices to the appropriate person for approval.  

3. Data extraction (Invoice processing)

• Invoice automation software extracts critical data points from attachments with Optical Character Recognition (OCR).

4. Analysis and validation (interpreting the invoice data according to business rules)

• If extracted values pass all validation rules, the invoice moves to “Verified.”
• If extracted values don’t pass validation rules, documents remain in “Review” unless manually reviewed and moved to “Verified” by a human in the loop.

5. Data transformation (automatic data entry into a database)

• Converts images into text data that can be used by other systems.
• A final report is generated containing all invoice data.
• Critical data is exported as .csv or integrated with the ERP system.

6. Invoice archiving (invoices are stored in a central repository for future use)

• The organization marks the invoice as “Paid” and archives the invoice in event of an audit, where it can serve as a valuable tool for detecting bookkeeping errors or embezzlements.

Watch this demo to see how our AI-driven platform can increase the accuracy and speed of your company’s processing workflow.

What are the limits of legacy automation solutions?

In the early 2000s, much of the “busy work” involved with invoice processing was taken over by robotic process automation (RPA), including data entry, price matching, and purchase orders. 

Since robotic process automation (RPA) can mimic the keyboarding and manual click-work performed by human knowledge workers, legacy RPA can handle some rote busywork. 

However, watch this demo to see where RPA falls short.

Legacy RPA is fragile and difficult to update

Since invoices are “semi-structured,” unfortunately, this is one area where traditional RPA falls short. Since legacy RPA uses a “rules-based” template for each invoice layout, there are real-world limitations regarding invoices. For example, since every vendor uses a unique layout, the “brittle” script-based technology breaks easily, making updating incredibly difficult. 

RPA is burdensome and inflexible to maintain

Whenever vendors change the invoice layout that your RPA template is built on top of, the entire automation breaks, and the workflow needs to be rebuilt all over again. Additionally, separate workflow automations must be created for each new invoice you receive, further driving the disillusionment about RPA as a “brittle” and unreliable technology. 

The only way to reach above-human accuracy is to go beyond the traditional template-based approaches. Modern invoice automation needs modern AI.

Invoice automation requires modern AI

In an automated invoice system that uses AI to process invoices, it doesn’t matter if the layout of invoices changes. This is because AI-powered automations can “learn” the structures of each invoice and process them no matter which vendor sends the invoice.

Intelligent document processing (IDP) is one example of artificial intelligence in invoice processing automation. IDP uses AI, machine learning, and computer vision to “read” invoices with above-human accuracy and extract critical data from them. 

Modern invoice processing needs more than AI

There are a lot of AI solutions offering a human-in-the-loop (HITL) component as a “verification” tool. Some of the AI solutions on the market only let you verify the data coming from the AI. These HITL components are solely focused on handing off the data to humans so they can correct it when the machine gets something wrong. 

Your AI could be perfect at doing this, especially if you have a PO system where you only need to validate three key fields. However, these HITL AI solutions offer a very limited role for human decision-makers to play in a workflow. What if additional decision-makers need to review an invoice? Depending on the size of the payment and your company’s size, you might also have accounting business rules requiring a four-eye or six-eye review process.

That’s why modern invoice automation needs more than AI. It needs key decision-makers in the loop. 

Especially in larger enterprise organizations, you need the capability to build automation workflows with dedicated human-in-the-loop (HitL) skills that are not review-focused for the accuracy of the AI. You also need the ability to send email alerts with critical information to these key decision-makers for an extra layer of security, with the capability to build it out iteratively. 

IDP: Agile workflow integration for the modern enterprise

When integrating any new technology, the failure to impact the core problem that needs resolution is a genuine concern. Errors in the implementation could carry real costs that ripple beyond your automation. For example, the time and money invested in projects that don’t move into production are at stake. To be agile, modern organizations need to build out iteratively, and the build-out must be flexible enough to continue iterating on the process. What you change today via automation may need more adjustment in a year. 

Automation Hero’s Hero Platform_ is the only IDP platform that automates the entire invoice automation workflow. It offers a full-service no-code environment, a flexible HITL skill-builder, and native AI/cognitive intelligence with workflow integration via API at the platform’s core, making it easy to build, test, refine, and deploy, or expand your automation with stability and scalability. 

Contrast this to traditional RPA, where the automation grows so complex that you throw it out and rebuild it all over again — instead of debugging and expanding the current implementation. 

Our easy-to-use and flexible HitL interface enables multi-party reviews and approvals for large invoices — turning randomized audit approvals and other AP compliance protocols into a streamlined, collaborative process that guarantees an extra layer of security. Most HitL interfaces are just rules-based user interfaces, but ours offers greater flexibility than that.

Additionally, Hero Platform_’s microservice architecture allows us to solve specific invoicing problems as we plug into existing workflows. For example, supercharge an ERP system with intelligent document understanding or augment your current RPA with AI capabilities. 

Our IDP technology is essential for enterprises looking to improve invoice processing speed, accuracy, and agility in the modern landscape.

Get started unlocking the intelligence in your invoices with AI-driven automation today

• Speak with an expert — tell us about your specific use case.
• Get a personalized demo — schedule a demo, and our Heroes will get in touch!