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If you are a business leader looking to streamline document processes for your organization, intelligent document processing (IDP) may be the automation solution your team needs.

However, sometimes it’s unclear which vendor can deliver the results your organization needs. So, how do you choose the right vendor?

Mar 14, 2023 by Craig Woolard

In a recent fireside chat, three Automation Hero leaders discussed the best strategies to evaluate IDP vendors. They covered everything from vendor experience and performance to use cases, technical requirements, implementation strategies, scalability, and cost-effectiveness.

This blog highlights the top seven questions to ask when evaluating and selecting an IDP vendor. Your current automation might not be optimal for processing documents or unstructured data, so interviewing vendors and asking key questions is essential to finding the best approach for a return on investment (ROI).

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By following the insights from these IDP experts, you can make a more informed decision that optimizes your document processing capabilities and takes your organization to the next level.

The three participants from Automation Hero were:

• Cynthia Almonte, Director of Product Management
• Kevin Shepherd, Director of Product Solutions
• David Danushevsky, Sales Director

You can read full bios of the speakers at the end of the article, but in the meantime, let’s hear their advice.

Top 7 questions to ask when evaluating IDP vendors

1. What is the potential impact of the problem?

On beginning the automation journey, Cindy Almonte leads the conversation with the following:

“I like to start by understanding the potential impact of the problem…for example, are we solving a problem for a line of business that has limited impact and probably a smaller issue for a group of people, or is this a problem that affects a lot of people organization-wide?”

Addressing the question from a product management perspective, Cindy explains how organizations should begin their automation journeys by identifying problems with the highest ROI.

“Finding something which has a good ROI calculation, whether it’s a small problem in a line of business or a big problem enterprise-wide, is always a really good initial approach because…once you’ve been impressed with automation’s ability to solve these bigger problems and shown the ROI-based calculation of what you’ve saved, it is much easier internally to get buy-in to solve other problems.”

From her experience guiding organizations through the automation journey, she explains that starting with a minor problem for a particular team may be a good approach. However, in her experience, she concludes that beginning with a more comprehensive enterprise-wide solution frequently impresses stakeholders and increases buy-in for future automation projects.

2. Are we looking for a short-term or long-term solution?

We all agree on the ultimate goal of automation: saving money and repurposing resources so humans can focus on work that requires actual thought.

On getting started, Kevin weighs in with a time-driven approach and considers the short-term and long-term problems that must be addressed before introducing automation into an organization.

“…as well as the small and big problems…is it just something you know is short-term because of a business strategy change…[or]…is it a really good time to revisit the whole process?”

Kevin also highlighted the importance of choosing a vendor to collaborate with your team and help you.

“Is the vendor the type of vendor that can…help you take it back to the whiteboard and say, ‘Okay, just because you’ve been doing it like this for 20 years doesn’t mean that’s the right way.’ How can we utilize the new technology we now have…to the best of its ability rather than just ripping and replacing what you’ve got?”

Choosing a vendor to help you with this process in the beginning phases is essential. Kevin advises organizations to revisit their strategies holistically before deploying automation that attempts to improve them. He also highlights the importance of internal resourcing and identifying the right team members in your organization who can help drive the project forward:

“From your business team to your IT team, who are the people who can really help you understand how reimagined processes will have the biggest impact? Identify those people on your team who can help drive automation forward and onboard them in the initial phase of the project. Get them speaking to the vendor and start building up that relationship early on.”

3. What is the most efficient way to get from A to B?

From input to output, there are multiple ways to achieve a goal. For example, there are many ways to achieve your automation goals, but we seek the most efficient approach with the best ROI.

When evaluating vendors, the focus should be finding the best and most efficient way to achieve your desired outcome. Automation Hero Solutions Expert David Danushevsky weighs in on the question with years of experience working with automation clients:

“With so many vendors out there, there are now multiple ways to get from A to B…but I will say, when you’re asking these questions, what you’re looking for is the best way, so that’s something that absolutely needs to be considered…your current way is probably not great, which is why you’re interviewing vendors, so a lot of these questions are definitely key to finding the most efficient way to grab that coveted ROI.”

On the most efficient way to get from A to B, Cindy says:

“I think it’s also really important to understand the capabilities of your resources internally are…[For example,] there are times when it’s just better to engage with services to get a process launched and running within an organization and then have your team supported on an ongoing basis, but not actually build it.”

Leveraging years of product management experience in the tech industry, Cindy explains the value of accurate personnel assessment and collaboration across departments when helping clients build out automation projects:

“Every organization is different. Some organizations have deep technical resources and can throw people at the problems and collaborate well with us. But, on the other hand, some organizations aren’t capable of that, and I think it’s beneficial for an organization just to do that assessment upfront.”

In addition to identifying the resources available, Cindy shares her A to B strategy for efficient implementation:

“The JavaScript coder who runs your Ops Team might be a good person to run automation on an ongoing basis and consult in the development, but it might be a lot faster to engage with services with the platform that you’re bringing on board and have them do the primary development of the core Automation and just get your bootstrapped to it that much faster.”

4. What is your organization’s current automation landscape?

In the automation world, many claims are being made, and it’s not always clear which vendor can deliver the results your organization needs.

Therefore, when evaluating automation tools, it’s critical to define the success criteria, such as the metrics you will use for measuring the automation’s success, the use cases you might want to automate, and implementation strategies as you navigate the current technology landscape.

Automation Hero’s David Danushevsky shares his perspective on the current automation landscape:

“A lot of the prospects I speak to already have automation in-house, but some of these technologies can be dated, or they’re just not doing what the user is necessarily looking for, or again, it’s not the most efficient way to get from A to B.”

Furthermore, David advocates focusing on a few projects first and observing their ROI:

“What I try to preach early on is… let’s focus on a project or two and let’s see how it works, and then once that ROI is really starting to take shape…then we have a plethora of opportunities to explore other use cases within the business, and that’s when it gets exciting.”

5. Who is involved in the decision-making process?

Whether it’s business people or IT people, it’s essential to understand the technical requirements of your automation and who it will affect. On who should be involved in the decision-making process, Kevin says:

“It’s crucial to involve everyone in the process and ensure they understand that the goal is not to replace their jobs but to build upon their important contributions. This early involvement is important for successful integration points, smooth implementation, and User Acceptance Testing. Early involvement from the team also helps reduce fear and resistance to the new process.”

Another action item to consider in your implementation strategy is defining the success criteria early on in the initial phases of a project. Failing to define success criteria that everyone aligns with is a common issue that derails a project.

This occurs when someone looks at the project and decides that the success criteria need to be increased or changed, even if they were agreed upon earlier. This change can affect the project’s implementation and ROI. Kevin says:

“If I shave time off here or if I manage to process these things two days faster and achieve the ROI goal that was initially defined, and then all of a sudden someone else looks at it and says ‘no, that needs to be twice as much,’ then that can really derail a project and the implementation, so I think getting both your requirements and your success criteria understood really early and getting everyone in alignment with them is pretty key.”

While redefining an organization’s success criteria comes up frequently, it can be tackled early to mitigate some of the headaches that slow down progress later. Therefore, it’s crucial to clearly understand the requirements and success criteria from the start of a project. Ensuring everyone’s agreement as early as possible will help avoid any issues.

6. Who will be implementing the solution? Are you using internal resources, or is an external vendor handling it?

If your company is considering multiple automations with long-term strategies, adding a partner from the vendor’s ecosystem can also be a tremendous value-add in decision-making.

Whether it’s from the vendor’s network or a partner that you already engage with, Kevin Shepherd explains how adding an experienced partner can push the project forward into production:

“Does the vendor already work with a partner base? If you don’t have an in-house team that can manage that, then it’s quite likely that having a partner in the ecosystem will help you really push that project forward.”

On the topic of who will be implementing the solution, Cindy emphasizes the critical role that project managers play within large-scale enterprise projects:

“I think for these large-scale enterprise projects, the most unsung hero on successful teams is the project manager…ultimately someone who can manage all aspects of a project.”

However, Cindy cautions against the assumption that project management is a simple task anyone can handle. Instead, she emphasizes the crucial role experienced project managers play in effectively managing complex projects:

“A project manager, not just somebody from accounting who runs a spreadsheet with all of the tasks on it, but someone who can literally say this is the definition of phase one based on the requirements from the business and then manage the entire team’s work effort, is worth their weight in gold internally…having someone who is aggressively managing the project is just hugely critical in moving these things into production.”

Doubling down on the critical role internal project managers play in the implementation strategy, Kevin Shepherd stresses the importance of collaboration between internal and external resources for successful enterprise-scale projects:

“As we go back to the topic of picking the right vendor…they have project managers, and they have customer success managers…but you need one on the customer side as well…so totally agree, Cynthia, that’s essential.”

On implementation, David Danushevsky also agrees with the essential role of the internal project manager:

“I want to echo the project manager’s piece from the rooftops. A project manager that’s calculated, efficient, organized, and has a process to check boxes as they go…is essential in moving the process along, and I’m so glad that you both touched on that.”

7. What security considerations do we have?

Security is a crucial concern for nearly every customer these days. Therefore, we recommend defining your security criteria from day one. Addressing security concerns early in the process helps reduce unnecessary hold-ups during your implementation.

On security topics to consider, Kevin Shepherd emphasizes the importance of defining your security criteria from day one with the following:

“From the actual implementation side, spinning up cloud environments takes minutes, so we often see customers get environments up and running really quick and then say, ’Actually, we need to tick all these security checkboxes with our security chief or our CISO.’ Again, it could be something really simple like email integration, but because it’s touching the cloud platform, suddenly, you’ve got lots of hoops to jump through, so ideally, you want to start those sorts of questions as early as you know them,m before you’ve even signed the paperwork.”

Furthermore, vendors are usually willing to address your security concerns and sign NDAs or master agreements before implementation. Doing this as early as possible helps reduce the number of unplanned concerns that tend to arise and hold up the process later. Kevin says:

“Any vendor out there, including Automation Hero, will be more than happy to run over security problems and make sure that the NDAs are signed at the right levels…because it’s a group business, and that helps everyone expand later…the great thing is you can get those conversations going really early in the process by working with the right vendor.”

Get started with IDP today

In conclusion, evaluating automation vendors requires thoroughly analyzing vendor capabilities, technical requirements, and cost-effectiveness.

The seven questions highlighted in this blog provide a framework for assessing potential automation vendors. As a result, you can select the best automation solution, platform, or technology for your use case.

We hope the insights from Automation Hero’s AI experts help your organization optimize its document processing workflows, increase efficiency, and drive ROI goals to success.

We recommend watching the whole discussion in the webinar link to gain further knowledge and insights from our AI experts. Then, with a comprehensive evaluation process, your business can make an informed decision that aligns with your unique goals, large or small.

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!

Who are the AI experts?

Cynthia Almonte, Director of Product Management, Automation Hero

Cindy Almonte is a tech veteran with over 20 years of product leadership experience. She has worked in various leadership roles in big data, search, marketing, and product management at major tech companies. As Automation Hero’s Director of Product Management, Cindy wears many hats and collaborates within and across teams to define, design, develop, and deliver first-class software solutions. Cindy consistently pushes herself and her team to deliver the best possible work. Cindy’s leadership and guidance are a big part of what makes Automation Hero’s work unique. She is an incredible inspiration for our team.

Kevin Shepherd, Director of Product Solutions, Automation Hero

Kevin Shepherd brings nearly three decades of tech experience to Automation Hero. Recently promoted from Head of Customer Success to Product Solutions Director, Kevin’s technical acumen in network engineering, consulting, communication, and product go-to-market strategy are critical assets to Automation Hero’s product success. In addition, Kevin says he enjoys making people’s work more enjoyable, productive, efficient, and precise by augmenting human intelligence with intelligent document processing automation.

David Danushevsky, Sales Director, Automation Hero

As a skilled sales professional with a diverse background in startups, including SaaS, artificial intelligence, automation, real estate, and hospitality, David has developed a unique perspective on business development’s technical and strategic aspects. Additionally, he brings a passion for mentorship and cultivates sales talent with an ability to think “outside the box” to deliver maximum success.

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AI transcends traditional robotic process automation. But why should insurance companies adopt new technology now? Because your competitors most likely already have.

Mar 10, 2023 by Craig Woolard

The future of insurance automation is transforming the way insurers operate, and it’s becoming essential for providers to remain competitive. By automating time-consuming and repetitive tasks, insurers can improve operational efficiency, reduce costs, and provide faster and more accurate customer service. 

Implementing automation with AI can help. AI transcends traditional robotic process automation insurance use cases. But why adopt new technology now? A closer inspection reveals that most competitors may already have.

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Why is technology important in the insurance industry?

Technology has become increasingly important in almost every industry today. 

The insurance industry is no exception. Technology can transform insurance companies’ operations, from policy underwriting and claims processing to customer service and communications. 

Why should insurance companies adopt new technology now? Because your competitors most likely already have.

By 2030, McKinsey predicts manual processes, such as manual underwriting, will no longer exist for personal and small-business products across life, property, and casualty insurance. Instead, according to the research, the future of insurance underwriting will be reduced to a few seconds…

Of course, that doesn’t mean humans will no longer be needed or in the loop. On the contrary, automation creates more opportunities for humans to weigh in and provide higher-level thinking. The future of insurance is bright, and the use of smart technologies to automate repetitive tasks will also enhance decision-making.

What is insurance automation?

In short, automation is the use of technology to streamline better customer communication and improve data quality throughout a company’s business processes. There are many opportunities for automation in the insurance industry, and we will examine a few examples.

But what can be automated in insurance? Insurance automation can include everything — from extracting critical data points inside policy underwriting and claims documents — to automating customer service responses. 

Insurance automation can range from simple rule-based technology to more complex systems that utilize artificial intelligence (AI) to remove barriers and augment leadership decision-making. 

What is robotic process automation in insurance?

When most people think of insurance automation, they think of robotic process automation (RPA). 

In the early 2000s, screen scraping software was one of the first use cases of RPA in the insurance industry. RPA involves building a series of steps or rules (called a “script”) to accomplish complex screen tasks. 

Today, RPA in the insurance industry still allows repetitive and rule-based tasks to be quickly automated, but these are generally limited to simple “swivel chair” copy/paste functions. RPA for insurance helps automate data entry, data processing, and triggering responses in other software. 

In insurance, RPA can help with insurance claims processing, life insurance administration, and benefits automation, but there are limits to where RPA can help providers.

Unfortunately, whenever there is a change in the software’s graphic user interface (GUI) or when the document layout changes that an RPA template is built on top of — the entire RPA automation breaks. This means automation engineers have to rebuild the process automation all over again. 

However, this defeats the point of automation. Automation is meant to save insurance companies time and increase efficiency; not easily break when it can’t adapt to change. This can be a major issue in insurance as policies and regulations are constantly changing. 

As outdated tech, it’s the biggest reason robotic process automation needs an AI upgrade. 

How can AI help the insurance industry?

RPA is going on 20 years old, so it’s no longer the shiny, new cutting-edge tech it once was. It’s also severely limited. For example, RPA cannot handle more complex tasks that require human judgment or decision-making — such as underwriting or claims assessment. 

Additionally, RPA needs help with unstructured data. For example, extracting free-form text-based content from emails or handwriting is nearly impossible to process with legacy rules-based automation technology. 

To handle the incoming unstructured data, insurance companies need a new type of automation technology with AI and cognitive intelligence more than ever. There are many examples of AI in the insurance industry; however, there is one clear leader: intelligent document processing.

What is intelligent document processing in insurance?

Intelligent document processing (IDP) is a new type of automation that directly solves the insurance industry’s most unique challenges with unstructured data.

IDP has many native applications of deep learning built in that can help the insurance industry transform unstructured data into business value. Using machine learning (ML), natural language processing (NLP), and deep learning (DL) algorithms, insurance companies have many more opportunities for automation compared to legacy RPA. For example, IDP can automate claims processing, analyze customer data, improve underwriting accuracy, and detect and prevent fraud. 

Here are the five use cases and benefits of using intelligent document processing in insurance:

Uses of automation in the insurance industry

1. Policy Underwriting: Automating underwriting processes reduces mistakes and the manual re-work involved to resolve the errors. Automation can help insurers quickly and accurately evaluate risks, speed up the underwriting process, and make higher-quality decisions on policy applications faster.  
2. Claims Processing: Automating claims processing helps insurers streamline the entire claims handling process, from initial reporting to the final settlement. A more efficient policyholder management process end-to-end can reduce costs, improve turnaround times, and provide faster and more accurate customer service.
3. Customer Service: Automated email responses and routing can help insurers provide 24/7 customer service and support to customer inquiries. By leveraging natural language processing (NLP) and machine learning (ML), Automated Email Classification and Processing systems, such as Automation Hero’s Hero Platform_ can answer common customer questions, resolve simple issues, and even escalate more complex inquiries to the appropriate department or agent whenever the “human touch” is needed.
4. Fraud Detection: Automated fraud detection systems can help insurers identify and prevent false claims, identity theft, and premium evasion. Automation can help insurers proactively detect and mitigate risks by analyzing large volumes of documents. Some automation platforms, such as Automation Hero’s Hero Platform_ use an API that allows existing fraud models to flag the claims needing human intervention for quick batch review before payout.
5. Data Analytics: Automated data analytics systems can help insurers control data quality for compliance and gain deeper insights into customer behavior or preferences, market trends, and risk factors. By analyzing vast amounts of structured and unstructured data, insurers could make more informed business decisions and deliver better products and services to policyholders. For example, Hero Platform can help identify cross-selling and upselling opportunities for existing policyholders. Our platform assesses policyholder information, compares it to others with similar data, and alerts a sales rep or a broker of the cross-sell opportunity.  

Insurance automation ideas for optimizing your business

Insurance companies can increase efficiency, reduce costs, and improve customer experience by implementing these automation ideas.

1. Use AI to classify insurance documents.

Automation Hero’s patent-pending Context-aware OCR scans and understands the intent of any human message. Then, based on the intent, our intelligent document processing platform can automatically classify the attached documents and even automate a response to the request or route the email to the proper department — reducing manual tasks in claims processing so policyholders get service faster.

2. Data extraction and data input 

Automation Heros’ Context-aware OCR can also handle text extraction better than the leading competitor. In fact, our intelligent OCR is 281% more accurate at handwriting recognition than the leading competitor’s — and more than 90% accurate at cursive recognition. 

When you focus this much attention on improving handwriting recognition, performance and accuracy go up for all machine-typed documents too. With industry-leading Context-aware OCR, claims adjusters can turn the unstructured data in handwritten notes, email requests, and receipts into usable business data that automatically gets entered into an internal database. 

3. Compare documents instantly

The best CRMs, document management software, and policy administration platforms need help managing the data points stuck in unstructured documents. However, Hero Platform_’s IDP is adept at comparing data points from multiple sources and analyzing them with above-human accuracy. For example, data quality teams typically check policies against outside sources to verify insurance company records are correct — such as property records and other databases. Hero Platform_ validates all of these data points in one go, reducing the amount of human error in the process.

4. Fraud prevention

Underwriters can’t assess the risks of new customers if the data they have on them (or their assets) remain stuck inside inaccessible documents. Likewise, claims adjusters mitigate similar risks to prevent paying out money on fraudulent claims. 

Hero Platform_ can look up account information stored in other places and compare the historical data with documents customers submit — speeding up data input and output throughout the underwriting and claims processes.

Our platform can even incorporate existing fraud models triggered by high claim amounts or fatalities. In addition, our human-in-the-loop attended automation will flag the claims that need human intervention for quick batch review before payout.

5. Optimizing Sales Outreach

Hero Platform_ can assess policyholder information, compare it to others with similar data, and alert a sales rep or a broker of a cross-sell opportunity. For example, our intelligent document processing platform can help your marketing team reuse the existing data in your company’s CRM and policy management platform to show sales agents all relevant details of a customer’s record, such as age, past purchase history, and the next best product that might interest them.

6. Process proof of prior insurance documents

One of the most common steps in onboarding new insurance customers is checking for proof of prior insurance. For example, customers interested in new car insurance might submit several policy declaration documents to prove they had insurance through another carrier. These might include ID cards, current carrier reports, renewal billing notices, or cancel/non-renewal statements.

7. Canceling or renewing an insurance policy automatically

Since Hero Platform_’s AI detects intent, it can determine if the email request is a renewal or a cancellation request. This means using our Context-aware OCR to extract relevant information from the email or attachment for essential details like the policy number, customer’s name, send date, and sender name. 

Hero Platform_ then performs a series of checks verifying that the sender’s identity matches the policy. If everything lines up, Hero Platform_ alerts your ticketing system to close or renew the policy and even sends a billing notification with the appropriate reply.

Conclusion

Insurance is highly regulated, and even the most prominent providers hesitate to adopt new technology. But this results in an industry buried in outdated document processes that gum up an organization’s ability to adapt.

For example, processing claims fast is one of the most critical parts of policyholder satisfaction and insurer reputation. But, if your adjusters are buried in documents during a natural disaster, they can’t focus on the most sensitive cases. 

Hero Platform_’s AI reduces the time dedicated to processing each claim, freeing up adjusters to bring their A-games to the highest-risk policies. Automation Hero can help you too.

Get started with insurance automation today

Learn how we helped, Markerstudy reduce their 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!

Dec 07, 2022 by Craig Woolard

What is intelligent document processing (IDP)?

Intelligent document processing (IDP) is a next-gen technology that helps organizations resolve problems that need hard-to-reach data. IDP reads, extracts, categorizes, organizes, converts, and outputs information from any document and turns it into practical business formats. 

IDP increases speed, decreases cost, and unlocks the valuable insights stuck inside structured, semi-structured, and unstructured documents from a wide variety of sources.

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How does IDP compare to RPA?

What truly sets intelligent document processing apart from legacy automation technologies is that it can perform practical and valuable tasks with the information it reads without tacking additional automation solutions onto it. IDP can function as a completely standalone technology or integrate with existing workflow automation tools to augment legacy RPA tasks.

With built-in AI intelligence, IDP reads the data within any document — even handwriting — and turns it into a usable format the same way a human would. 

Even though intelligent document processing can streamline an entire business automation workflow end-to-end (without needing anything else), IDP plays nicely with existing workflow automation software — including Robotic Process Automation (RPA). For more, check out our other article on the differences between RPA vs. IDP.

When combined with legacy automation tools such as RPA, intelligent document processing brings next-level AI/cognitive capabilities to these critical business processes — creating the most advanced solution for the global challenges of rapidly growing unstructured data.

Some IDP solutions include automation features (also called hyper-automation) to manage the output into a workflow, while other IDP vendors only focus on the intelligent data extraction step. With these more limited IDP products, automation engineers and designers must take the output elsewhere and manage it themselves.

How does document processing work?

Simply put, IDP has AI that can “read” documents the same way as humans. 

IDP uses Optical Character Recognition (OCR), machine learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) — to capture the valuable data locked away inside documents and their related document workflows.

As a next-gen technology, intelligent document processing consists of fundamental elements (classification, extraction, and validation).

It’s important to mention not all IDP vendors approach these elements the same way, nor do they solve the same problems. For example, some IDP solutions include automation to manage the output into a workflow, while others only focus on intelligent data extraction — forcing the user to configure or manage the output.

1. Classification

The classification stage in intelligent document processing uses OCR technology to identify, capture, convert and organize information streams. Some IDP platforms combine Al, machine learning, and OCR to convert text more accurately. However, Automation Hero is the only end-to-end IDP platform with a special patent-pending Context-aware OCR that converts text — even handwriting — significantly more accurately than other IDP vendors.

2. Extraction

Extraction applies trained AI models to target specific elements of interest. For example — addresses, tax details, monetary values, technical specifications about products, or hard-to-find clauses in legal contracts. Unlike legacy approaches, intelligent document processing uses AI to extract these elements of interest without building complex, fragile templates for each document.

3. Validation

The validation stage in IDP performs data analysis on the extracted information. For example, applicant addresses extracted from application forms could be compared with addresses on utility bills and bank statements. Another example could be validating the accuracy of invoice totals by comparing details in purchase orders. Attended automation can exist as “human-in-the-loop” to provide an extra layer of validation assurance for employees to approve decisions.

4. Human-in-the-loop

Some IDP vendors like Automation Hero provide a human-in-the-loop environment with a no-code interface, allowing users to approve validations and correct errors before they occur. Automation Hero’s attended automation goes several steps further. It allows automation designers to have dedicated audit interfaces in-between tasks based on business rules — which helps close the gap between people and automation. 

As a bonus, Automation Hero’s human-in-the-loop also fine-tunes the training of AI algorithms used in the data capture, extraction, and classification stages.

5. Workflow integration

After an IDP platform captures the information from documents, it processes it as valuable data that can be used by other software applications, microservices, and even third-party digital workflow services (via API) — helping organizations streamline the data intake process across different departments. Most automation vendors charge extra for orchestration capabilities that add intelligence to their automation solutions.

However, workflow integration and orchestration come built-in with Automation Hero — so it scans documents and brings the hard-to-reach data into critical business processes — at no extra charge.

Where IDP can help

IDP captures all the essential data locked away in emails, text documents, scanned paper PDFs, and photos containing text used in business processes. No matter what documents your organization handles, cutting-edge AI models in IDP can process any document with above-human accuracy.

1. “Reads” all file formats

Documents aren’t just paper forms and PDFs. Business documents come in all formats. This includes images, emails, text messages, online form submissions, and even code. Intelligent document processing can read all formats — with a clear understanding of every word — faster and more accurately than traditional automation software. 

2. “Unlocks” unstructured documents

Essential data can be stored in many ways, which presents several challenges. There are documents with structure, and there are documents with less structure. Structured documents, such as forms, require less advanced technology. Semi-structured and unstructured documents, such as long-form content or images of blurry text, require more sophisticated AI. 

Unstructured documents are completely “unfixed,” so they don’t follow rules or fixed layouts that computer software applications typically use to identify them. No matter which type of document your organization handles, intelligent document processing uses next-gen AI technology to scan content and interpret context from any structured, semi-structured, or unstructured document.

3. Extracts handwriting and signatures

Handwriting has always been tricky. OCR is the technology that will recognize the characters, letters, and numbers in images of text — regardless of font. OCR also recognizes cursive in multiple languages.

As a legacy technology, OCR does not recognize handwriting accurately. However, handwriting and signature recognition significantly improve when OCR and AI are combined within IDP. 

4. Reads scanned paper documents

Low-quality documents are more common as people rely on smartphone cameras instead of scanners to digitize paper. Humans can use context clues to read and understand muddy text, but legacy OCR and traditional automation technologies do not have this reasoning capability.

If a letter or word is too blurry for the software to recognize, legacy OCR will skip it over without realizing it missed anything. This is especially true with handwriting. However, IDP can classify, extract, convert, and process business-critical information locked away in archives of unstructured documents — even if the scan quality is blurry or the handwriting is illegible.

Why is intelligent document processing important?

Document processing is one of the trickiest problems in business. Over half of enterprise knowledge workers say their business applications fail to automate their document-based processes — or they report that their different systems and software do not talk to each other.

Legacy automation technologies aren’t enough 

Until now, most automation software has been robotic process automation (RPA) — which has helped streamline mundane, non-document-centric tasks performed on computer screens. Unfortunately, RPA’s capabilities are limited to tasks that do not involve high-level decision-making. Legacy approaches have to build complex, fragile templates for each document. For example, the entire automation breaks when there is a change in either the GUI or a document that an RPA template is built on.

Unstructured data is proliferating

As new customer information comes through emails, text messages, social media, photos, PDFs, and scanned documents with handwritten text — most new data businesses collect and produce is “unstructured.” Companies need a fast, efficient, and reliable way to transform the unstructured information hiding in documents into structured data that they can easily use. IDP provides the essential document understanding of unstructured data that frees people to focus on the more critical aspects of their business.

IDP in action — industry use cases

Any industry reliant on document processes can achieve digital transformation using IDP. However, industries such as — finance, healthcare, and insurance — handling thousands of structured or unstructured forms from a wide variety of different institutions are the industries that will benefit the most.

For example, insurance companies deal with various incoming documents to support an insurance claim from multiple institutions. These could be any documents or paperwork from institutions doing business with their customers. These industries are not the direct customers of those companies, so they don’t have the power to standardize those documents.

Here is a short list of top industry use cases for intelligent document processing.

Top industries for intelligent document processing (IDP)

• Insurance 
• Healthcare
• Banking
• Legal
• Logistics
• Retail & e-commerce
• Government & Public Sector

IDP for Insurance

Insurance claims typically consist of paper forms signed by claimants, and there can be a lot of supporting documentation. They might be handwritten doctor’s notes, photos of sick pets, or property damages. The increased volume of claims — plus supporting documents such as invoices and receipts — can be daunting. 

With a wide range of documents, from life insurance applications to auto accident claims, disability forms, change of beneficiary forms, and annuity account forms, IDP replaces this labor-intensive and error-prone process with an automated workflow. 

IDP for Banking & Financial Services

Financial institutions must perform KYC and customer due diligence checks (CDD). But, financial institutions can’t know their customers if most of the data they have about them is stuck in inaccessible documents.

For example, loan officers must navigate large volumes of KYC documents to verify signatures before clearing payments. Delays in these KYC processes undermine underwriters’ abilities to mitigate risks and react quickly.

Automation Hero’s IDP will make historical data readily available for KYC review. It also makes this data accessible to different departments and third-party Fintech services — fast-tracking the customer journey. 

IDP for Logistics

Shipping and logistics companies can benefit from IDP too. Let’s say you’re a shipping company with business in 35 countries. You need tools that provide real-time updates when government agencies or regulatory entities change their policies around flights, freight restrictions, customs requirements, taxes, or tariffs.

For example, suppose Mexico raises its freight limits on imported goods. In that case, an IDP automation instantly re-calculates how this affects your operations across the board — and automatically updates all affected systems.

Shipping companies handle all kinds of documents. These include — identifying shipments with hazardous material indicators on shipping documents, capturing annotations about damages from delivery receipts, processing contracts for volume discounts or making reasonable clauses, and cross-referencing with billing and incident systems to identify points for renegotiation with suppliers.

The benefits of intelligent document processing

Increased efficiency isn’t the only reason to implement intelligent document processing. Any enterprise with business processes stuck under a heap of documents will immediately see the benefits of IDP. Here are a few of the most common benefits:

1. IDP is highly scalable

Enterprises need a flexible, straightforward path to scale products and services. Automating these document-intensive processes allows your business to scale your response without needing to scale your human team to the same degree.

For example, if you get a seasonal surge of claims or applications, you need a seasonal team of workers to handle the additional load. That is a management task all by itself. It gets even worse if it is unpredictable (like trying to predict where to assign your claims adjusters during natural disaster seasons).

Automation gives your skilled workers more leverage to handle an unexpected increase in volume — giving you more time to assign additional personnel after seeing where they will have the most impact. Whether scaling up or down, the platform your business depends on needs to be built for rock-solid, stable performance. Automation Hero’s Hero Platform_ is the only product in the IDP and automation space with built-in fault tolerance at the platform’s core.

2. IDP integrates with existing solutions

Intelligent document processing is incredibly flexible and integrates seamlessly with internal and external databases, ERP systems, invoice processing platforms, and other accounts receivable (AR) software.

Many popular data systems don’t have AI/cognitive capabilities — so adding IDP dramatically improves accuracy and production efficiency. IDP can also integrate with robotic process automation to augment RPA tasks as sub-processes with AI capabilities.

3. IDP’s ultra-precise accuracy ensures compliance

Data extraction eliminates typos and other human errors from manual data entry tasks. However, IDP extracts data from documents with above-human accuracy, eliminating the need for laborious review processes.

Data extraction in IDP formats critical information into forms other computer systems can use, so the information is immediately available for analysis. For example, IDP is particularly adept at comparing information from different sources to assess whether you’re staying in compliance. 

4. IDP lowers costs and increases ROI

Any time automation achieves greater efficiency, it cuts down on costs. Because automation allows organizations to make more money with less time and labor, it improves the bottom line. 

IDP’s native AI decreases the bottom line even more while increasing profitability. Since standalone OCR and other legacy automation tools lack AI, they make many mistakes — increasing the work needed to manually review the errors. 

However, IDP substantially reduces the workload caused by human errors, allowing companies to expand their bandwidth for new leads, customers, sales, and other revenue-generating opportunities that further improve profitability. 

5. IDP cuts down on paper waste

Enterprise runs on documents, but automation and AI need data. Companies need an easy way to extract essential data from paper documents to improve the efficiency of their document processes. IDP platforms like Automation Hero have native AI that captures information from paper documents. Blurry scans and chicken scratch handwriting won’t be a problem for our IDP.

Get started with IDP

If you are shopping for an intelligent document processing solution, choosing the right IDP platform that will fit your organization’s needs is important.

We offer a few strategies to get started:

• Read our IDP guide — our complete guide to IDP will guide you every step of the way.
• See how it works — our IDP platform has dozens of use cases. Filter by industry and see what it can do.
• 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!

start new section

These days, there is an acronym for nearly everything. In the automation world, “IDP” falls under the umbrella of “Intelligent Process Automation” (IPA). In the artificial intelligence field, IDP means “Intelligent Document Processing.” However, this type of intelligent document processing automation has native AI intelligence built-in — so it processes any kind of document with above-human accuracy.

Nov 09, 2022 by Craig Woolard

What is intelligent document processing?

Intelligent document processing is a type of business workflow automation that uses artificial intelligence (AI) to read documents the same way that humans do. IDP is a next-generation automation technology that brings AI/cognitive capabilities to workflow automation software — creating the most advanced solution for the global challenges of rapidly growing unstructured data.

How does intelligent document processing automation work?

IDP uses AI technologies to capture, categorize, extract and validate essential data in emails, text documents, scanned documents, and even photos that can be turned into workflows for data analysis and automation software.

Intelligent document processing leverages AI, machine learning (ML), Deep Learning (DL), Optical Character Recognition (OCR), and natural language processing (NLP) to read and understand the context of the information it captures from documents.

IDP scans the contents of these documents and interprets context — along with the intent — to help organizations efficiently manage documents and streamline a substantially more intelligent document processing automation.

Understanding intelligent document processing

The first step to understanding intelligent document processing is to understand the big picture. 

As new customer information increasingly comes through email, text messages, social media, photos, PDFs, and handwritten scanned documents — most of the new data we collect and produce today is “unstructured.” 

In fact, data researchers at the Everest Group surveying the intelligent document processing market found that unstructured data grows between 55-65% every year. These researchers also determined the growth of unstructured data is growing three times faster than structured data. 

But what is unstructured data, and why is it growing so fast?

Documents with structured vs unstructured data

Essential data can be organized in a variety of ways and this presents all kinds of challenges. In the world of big data, there are two ways information is organized: structured and less structured. 

Structured documents require less advanced technology. Less structured documents require sophisticated AI.  Less structured documents could be semi-structured — or they might be completely free-form — with no structure at all. In either case, documents with less structure are messy and traditionally require the help of human experts to interpret their contents to work with them.

What is unstructured data?

Unstructured documents are “unfixed” and do not follow a template, a fixed layout or a set of rules.

Gartner defines unstructured data as any content — machine-printed or handwritten — that lacks predefined rules or guidelines that computers typically use to classify and identify it. Unstructured data could be textual or non-textual — such as a photo containing handwritten text — but it could also exist in a non-relational database such as NoSQL. 

Some common documents with unstructured data examples include:

• images 
• articles 
• memos 
• emails
• websites 
• legal contracts
• simple text files 
• customer chats
• discussion forums 
• scientific research
• social media posts
• SMS text messages
• handwritten letters
• call center transcripts
• internal Slack message threads 
• some programming/scripting languages

Research estimates unstructured data accounts for a whopping 80-90% of all new enterprise data — yet only 18% of organizations are taking advantage of it. The other 82% are not even utilizing their most valuable resource.

Information contained in these documents does not follow a set of rules, templates, or structures, so their contents are “hidden” from computers. Traditionally, unstructured data could only be read by humans, and therefore required repetitive manual data entry before it could be used for data analysis.

What is semi structured data?

Documents with semi-structured data conform to a template, but the layout of information is not rigid and likely varies from document to document. 

Since the layout of semi-structured documents is not standardized, organizations receiving these documents may not be able to accurately predict where the information of interest is located in a variety of incoming documents. 
Examples of semi-structured data include invoices, purchase orders, bill-of-materials (BOM), receipts, and loan applications. Since emails have some fixed properties, some sources categorize emails as semi-structured documents, however, the body of emails is where valuable information is contained, and this essential data is generally unstructured.

What is structured data?

Structured documents conform to a rigid template that does not vary. 

Examples of structured documents include identification documents, such as passports, identity cards, and driver’s licenses — plus fixed forms — such as tax forms, surveys, questionnaires, tests, and insurance claims forms that were designed to be scanned by computers.
Researchers at the IDC recently discovered more than half of the documents enterprises process have structured layouts.

What can intelligent document processing do for your business?

Businesses are under constant market pressure to deliver more efficiently and offer better customer experiences than the competition. 

Nearly all business transactions and customer interactions are taking place on some kind of digital platform. Today, interactions between customers and businesses are happening online more than ever before. As more Gen Zers embrace wearable devices, augmented reality, and even the metaverse, their expectations will continue to evolve as well. 

With the bulk of new customer information coming from emails, text messages, social media posts, PDFs, and even handwritten scanned documents — not having a method to mine the most valuable data hiding in unstructured documents will limit an organization’s ability to adapt quickly to changed market conditions. 

In highly regulated organizations where latent value remains untapped inside older, heritage document processes — inaccessibility to essential business data poses a real barrier to digital transformation.

Adding to the friction, unstructured documents require repetitive manual data entry and even translation if the text is in another language. These mind-numbing tasks deflate employee morale and leave room for typos and other human errors that are costly to fix. 

As a result, companies need a fast, efficient, and accurate way to transform the unstructured data hiding in documents into structured data that businesses can use. IDP provides the essential document understanding of unstructured data — freeing people to focus on the more critical aspects of their business.

Intelligent document processing use cases

No matter which type your organization handles, intelligent document processing uses cutting-edge AI models to scan the content and interpret the context — along with the intent — from any document. Here are a few use cases of what intelligent document processing can do.

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 clear understanding of every word — and with greater accuracy and speed than traditional automation software can offer. 

Scanned documents

Companies around the globe struggle to extract information from scanned PDFs. This is especially the case with handwriting. IDP can intelligently classify, extract 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.

Handwriting and signatures

Object or Optical Character Recognition (OCR) is the technology that recognizes characters, letters, and numbers — regardless of font — it also recognizes cursive. As a standalone technology, OCR does not recognize handwriting accurately. But, when integrated with AI, handwriting & signature recognition significantly improves with IDP.

IDP vs. optical character recognition (OCR)

Even though intelligent document processing is AI-focused, optical character recognition (OCR) plays an important role in IDP’s ability to provide end-to-end document processing. OCR is one of many architectural components of intelligent document processing. Interestingly, not all OCR solutions on the market are the same.

All sorts of companies have sprung up around the technology capable of recognizing machine-printed characters and handwriting. Legacy vendors started tackling Optical Character Recognition in the 1980s. Even after decades of research, most off-the-shelf OCR technologies still lack the sophistication to accurately recognize handwriting today. Even with the best quality scanners and document quality, you only get 60% accuracy with OCR before it eventually hits a wall. 

Intelligent document processing is significantly more advanced than standalone OCR technology. IDP delivers a full stack of AI models that process and manage entire collections of machine-printed documents and handwritten texts. OCR is just one of the tactical steps IDP takes to accomplish this high-level task. 

Within the IDP stack, OCR specifically recognizes letters, numbers, and symbols in sources. Since OCR specializes in recognizing both handwriting and machine-printed texts from images, PDFs, and scanned paper documents, OCR is traditionally one of the first steps to digital transformation. 

For example, OCR is the main technology IDP employs to classify documents into appropriate categories. OCR works by analyzing the light and dark areas in the source material looking for clues about the kind of document it is scanning. 

If one of the AI models in intelligent document processing classifies the contents of a PDF as data relevant to “invoices” — or even the contents of specific pages containing signature in a longer PDF, OCR will work to identify the light areas as backgrounds and the dark areas as either handwriting or machine-printed characters. OCR then turns the binary data it scans from the source into glyphs, characters, numbers, and symbols that IDP can use for further processing. 

As the final step, OCR converts the characters it detects from either the handwriting or printed texts into a data format IDP can use in document extraction software for the next processing phase. Even though OCR is decades-old technology, it plays an essential role in IDP.

IDP vs RPA vs OCR

Intelligent document processing is extremely different compared to older automated data processing systems. IDP can read and understand the context of the information it captures from documents — allowing organizations to automate a much deeper “stack” of document-related tasks than previously possible. 

Until now, most automation software has been robotic process automation (RPA). RPA works by recording simple point-and-click mouse movements to automate repeatable tasks using the UI. RPA requires a series of steps (called a “template”) to accomplish its more complex screen tasks. In the early 2000s, screen scraping software was one of the first use cases of RPA as a “band-aid” to create a “bridge” in between newer systems and incompatible legacy systems.

Today, RPA helps streamline some mundane, non-document-centric tasks performed on computer screens. Unfortunately, RPA tools and OCR are both limited to tasks that do not involve high-level decision-making. The biggest differences between RPA, OCR, and IDP are native AI intelligence and essential document understanding with expert-level context awareness. IDP is next-gen automation technology that has evolved from the need to go beyond RPA’s limited capabilities.

Both RPA and OCR are rules-based technologies that rely on traditional template approaches. Templates are useful for tasks and documents with well-defined structures, but once a design element of the user interface changes in either the software or the documents that a template is built on top of, the template breaks, and the automation has to be re-designed all over again. 

Since IDP is based on AI, data extraction is not limited by a rules-based approach. Intelligent document processing reads the contents of documents and learns from their contents just like humans, so it improves with every use. IDP can complement legacy point-and-click RPA tools for tedious screen tasks — but with an AI engine at its core, IDP is powerful enough to replace your RPA robots, entirely.

Key benefits of intelligent document processing

There are several reasons organizations should incorporate IDP. Here are some of the key benefits of intelligent document processing within modern workflow automation.

• RPA technology still relies on artificial intelligence, screen scraping, and workflow automation to accomplish anything greater than simple, screen-related tasks. Intelligent document processing has native AI built-in, so it learns and improves with every use.
• IDP will capture, classify, and extract essential data from structured, semi-structured, and unstructured documents. It then intelligently processes data with above-human accuracy.
• Intelligent document processing can integrate with traditional RPA as a sub-process to augment RPA tasks. When dealing with vast amounts of essential information hiding in documents, IDP is the ideal choice to manage it all.
• Legacy automation systems like RPA can’t keep up with the growth of unstructured data, nor can they accurately handle the hidden context of such data. Intelligent document processing unlocks all of the essential data hiding in these unstructured documents.
• Having access to unstructured data provides valuable feedback about customers and the experience of doing business with your organization. It also removes barriers in the way of critical document processes that create friction in the customer experience.
• Within a related workflow or automated task, IDP is non-invasive and easily integrates with internal applications, systems, and automation platforms.
• With IDP, organizations can begin speeding up their document-driven processes without compromising the quality of their service. With intelligent document processing, everyone wins.

The 7 steps of document processing automation

Data ingestion

1. The first step in intelligent document processing is intaking data from different sources. One way IDP ingests data is through a process called “data capture.”
2. For example, if your original document was a paper document, you might digitally preserve documents as binary image data in a few different ways. You could use a scanner to capture your data as a PDF file — or you could use your camera to capture the document as an image.
3. Regardless of the source, these digital copies can serve as legal original copies, making the data in digital records management systems even more valuable.
4. In document management systems augmented by intelligent document processing, data capture involves more than just scanning paper documents and taking photos. In an IDP workflow, document capture can automatically process collections of electronic documents in multiple versions and formats — allowing your records management system to become source agnostic.
5. For example, you might want to store the original Word document and have access to a PDF version for future reference. Integrating Intelligent document processing in digital archives gives immediate access to important information in documents from the very moment of data capture.

Data classification

1. The second step in IDP figures out the type of data being processed.
2. Document classification begins by identifying the beginning and the ending of the source material — and then analyzes the content in between. In intelligent document processing, this involves classifying document types — such as invoices, purchase orders, identification documents, contracts, bills, resumés, letters, etc.
3. If the source is a PDF document or a scanned image of a document, an OCR algorithm trained in nearly 190 languages interprets the data by capturing characters, numbers, and symbols from the data it scans.

Data extraction

1. Once IDP has classified the file type and analyzed the format of the data source, the most important step in intelligent document processing is text extraction.
2. Intelligent document processing applies trained AI models — using natural language processing (NLP), machine learning (ML), and Deep Learning (DL) algorithms — to extract valuable context from the source. Document extraction targets specific elements of interest — such as addresses, tax details, monetary values, technical specifications about products, or hard-to-find clauses in legal contracts.
3. Intelligent document processing then enters the data it captures into a database or stores it for future use.
4. From here, the data could be translated into another language, processed in a different format, or automatically entered into any number of enterprise application databases.
   • E.g., spreadsheets, accounting systems, Enterprise Resource Planning (ERP) systems, Enterprise Content Management (ECM) systems, Customer Relationship Management (CRM) systems
   • Complementary technologies — such as Robotic Process Automation (RPA), and cloud SaaS services to augment your current business workflow automation.

Intelligent search

• This process uses inferred logic in the user’s search criteria rather than exacting search terms to retrieve essential details from a collection of documents that share similar characteristics.
  • For example, if searching for “early-payment discounting clauses, ” intelligent document processing will find all of the early-payment discounting clauses across all of the relevant contracts.

Document validation

• This important step takes specific elements within documents and compares those details with other documents.
  • For example, applicant addresses extracted from an application form can be compared to all addresses on utility bills and bank statements. Another example might be validating that invoice totals are accurate by comparing details in matching purchase orders.
  • Some IDP vendors like Automation Hero may offer a human-in-the-loop environment with a no/low-code interface to let users approve validations and correct errors before they occur — closing the gap between people and the automation. As a bonus, this will also fine-tune the training of AI algorithms used in the data capture, extraction, and classification stages.

Data analysis

1. This process performs data analysis on documents by breaking down the information and even document processes into separate component parts.
2. An AI model then looks at the relationships between parts and analyzes how they are interconnected to provide a deeper insight into their contents and related document processing workflows.

Automation Hero’s Hero Platform_ provides a beautiful dashboard to display these document workflow metrics.

Workflow integration

1. The final step of intelligent document processing is exporting the information to internal data systems and integrating other business process workflows.
2. After the data is released to your internal data systems, the information is immediately available to be accessed by other departments — freeing up your entire organization to focus on taking quick action and providing efficient service to customers.
3. From here, intelligent document processing could be integrated with RPA systems as a sub-process to augment RPA tasks with AI/cognitive capabilities.
4. Automation Hero’s API will serve as a “fabric” connecting all workflow services together with other business process automation systems. And once you have created your automation, it can be offered via an application programming interface (API) as a service for other departments to use. 

With Automation Hero as the IDP fabric integrating multiple APIs into a unique business process — you will have an infrastructure that open-banking APIs, FinTechs, and other third parties can use.

Getting started with IDP

When introducing new technology into your business workflow automation, it makes sense to introduce IDP technology on a limited basis so you can test how it works, first. 

If you are shopping for an intelligent document processing solution, it’s important to choose the right IDP technology that can fit all of the needs your organization and your people will have. 

We offer a few suggestions to get started:

1. Research — get exclusive access to ebooks, guides & more in our free resource center.
2. Read our blog — our AI experts have written articles to guide you every step of the way.
3. Learn about Automation Hero — our IDP platform has dozens of use cases. Filter by industry to see what it can do for your organization.
4. Try Automation Hero — our free trial is the first step in your digital transformation.
5. Speak with an expert — tell a sales expert about your use case now.
6. Get a personalized demo — schedule a demonstration and our Heroes will get in touch!

In the meantime, start planning how you might use Automation Hero’s built-in intelligence to address multiple use cases across your entire company. Our Human-in-the-loop attended automation provides security for employees to approve any automation that also trains your AI model. Just imagine training your AI model with the wisdom of your company’s top experts!

These days, there is an acronym for nearly everything. In the automation world, “IDP” falls under the umbrella of “Intelligent Process Automation” (IPA). In the artificial intelligence field, IDP means “Intelligent Document Processing.”

However, this type of intelligent document processing automation has native AI intelligence built-in — so it processes any kind of document with above-human accuracy.

Nov 09, 2022 by Craig Woolard

What is intelligent document processing?

Intelligent document processing is a type of business workflow automation that uses artificial intelligence (AI) to read documents the same way that humans do. IDP is a next-generation automation technology that brings AI/cognitive capabilities to workflow automation software — creating the most advanced solution for the global challenges of rapidly growing unstructured data.

Keep in touch

How does intelligent document processing automation work?

IDP uses AI technologies to capture, categorize, extract and validate essential data in emails, text documents, scanned documents, and even photos that can be turned into workflows for data analysis and automation software.

Intelligent document processing leverages AI, machine learning (ML), Deep Learning (DL), Optical Character Recognition (OCR), and natural language processing (NLP) to read and understand the context of the information it captures from documents.

IDP scans the contents of these documents and interprets context — along with the intent — to help organizations efficiently manage documents and streamline a substantially more intelligent document processing automation.

Understanding intelligent document processing

The first step to understanding intelligent document processing is to understand the big picture. 

As new customer information increasingly comes through email, text messages, social media, photos, PDFs, and handwritten scanned documents — most of the new data we collect and produce today is “unstructured.” 

In fact, data researchers at the Everest Group surveying the intelligent document processing market found that unstructured data grows between 55-65% every year. These researchers also determined the growth of unstructured data is growing three times faster than structured data. 

But what is unstructured data, and why is it growing so fast?

Documents with structured vs unstructured data

Essential data can be organized in a variety of ways and this presents all kinds of challenges. In the world of big data, there are two ways information is organized: structured and less structured. 

Structured documents require less advanced technology. Less structured documents require sophisticated AI.  Less structured documents could be semi-structured — or they might be completely free-form — with no structure at all. In either case, documents with less structure are messy and traditionally require the help of human experts to interpret their contents to work with them.

What is unstructured data?

Unstructured documents are “unfixed” and do not follow a template, a fixed layout or a set of rules.

Gartner defines unstructured data as machine-printed or handwritten content lacking predefined rules or guidelines that computers typically use to classify and identify. Unstructured data could be textual or non-textual — such as a photo containing handwritten text — but it could also exist in a non-relational database such as NoSQL. 

Common documents with unstructured data examples include:

• images 
• articles 
• memos 
• emails
• websites 
• legal contracts
• simple text files 
• customer chats
• discussion forums 
• scientific research
• social media posts
• SMS text messages
• handwritten letters
• call center transcripts
• internal Slack message threads 
• some programming/scripting languages

Research estimates unstructured data accounts for a whopping 80-90% of all new enterprise data — yet only 18% of organizations are taking advantage of it.

The other 82% are not even utilizing their most valuable resource.

Information contained in these documents does not follow a set of rules, templates, or structures, so their contents are “hidden” from computers. Traditionally, unstructured data could only be read by humans, and therefore required repetitive manual data entry before it could be used for data analysis.

What is semi structured data?

Documents with semi-structured data conform to a template, but the layout of information is not rigid and likely varies from document to document. 

Because semi-structured documents do not have a standardized layout, organizations handling them may need help predicting where the information of interest is located. Examples of semi-structured data include — invoices, purchase orders, bill-of-materials (BOM), receipts, and loan applications.

Emails have some fixed properties, so they could be considered semi-structured documents; however, the body of emails is where valuable information is contained, and this essential data is generally unstructured.

What is structured data?

Structured documents conform to a rigid template that does not vary. 

Examples of structured documents include identification documents, such as passports, identity cards, and driver’s licenses — plus fixed forms — such as tax forms, surveys, questionnaires, tests, and insurance claims forms that were designed to be scanned by computers.
Researchers at the IDC recently discovered more than half of the documents enterprises process have structured layouts.

What can intelligent document processing do for your business?

Businesses are under constant market pressure to deliver more efficiently and offer better customer experiences than the competition. 

Nearly all business transactions and customer interactions are taking place on some kind of digital platform. Today, interactions between customers and businesses are happening online more than ever before. As more Gen Zers embrace wearable devices, augmented reality, and even the metaverse, their expectations will continue to evolve as well. 

With the bulk of new customer information coming from emails, text messages, social media posts, PDFs, and even handwritten scanned documents — not having a method to mine the most valuable data hiding in unstructured documents will limit an organization’s ability to adapt quickly to changed market conditions. 

In highly regulated organizations where latent value remains untapped inside older, heritage document processes — inaccessibility to essential business data poses a real barrier to digital transformation.

Adding to the friction, unstructured documents require repetitive manual data entry and even translation if the text is in another language. These mind-numbing tasks deflate employee morale and leave room for typos and other human errors that are costly to fix. 

As a result, companies need a fast, efficient, and accurate way to transform the unstructured data hiding in documents into structured data that businesses can use. IDP provides the essential document understanding of unstructured data — freeing people to focus on the more critical aspects of their business.

Intelligent document processing use cases

No matter which type your organization handles, intelligent document processing uses cutting-edge AI models to scan the content and interpret the context — along with the intent — from any document. Here are a few use cases of what intelligent document processing can do.

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 clear understanding of every word — and with greater accuracy and speed than traditional automation software can offer. 

Scanned documents

Companies around the globe struggle to extract information from scanned PDFs. This is especially the case with handwriting. IDP can intelligently classify, extract 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.

Handwriting and signatures

Object or Optical Character Recognition (OCR) is the technology that recognizes characters, letters, and numbers — regardless of font — it also recognizes cursive. As a standalone technology, OCR does not recognize handwriting accurately. But, when integrated with AI, handwriting & signature recognition significantly improves with IDP.

IDP vs. optical character recognition (OCR)

Even though intelligent document processing is AI-focused, optical character recognition (OCR) plays an important role in IDP’s ability to provide end-to-end document processing. OCR is one of many architectural components of intelligent document processing. Interestingly, not all OCR solutions on the market are the same.

All sorts of companies have sprung up around the technology capable of recognizing machine-printed characters and handwriting. Legacy vendors started tackling Optical Character Recognition in the 1980s. Even after decades of research, most off-the-shelf OCR technologies still lack the sophistication to accurately recognize handwriting today. Even with the best quality scanners and document quality, you only get 60% accuracy with OCR before it eventually hits a wall. 

Intelligent document processing is significantly more advanced than standalone OCR technology. IDP delivers a full stack of AI models that process and manage entire collections of machine-printed documents and handwritten texts. OCR is just one of the tactical steps IDP takes to accomplish this high-level task. 

Within the IDP stack, OCR specifically recognizes letters, numbers, and symbols in sources. Since OCR specializes in recognizing both handwriting and machine-printed texts from images, PDFs, and scanned paper documents, OCR is traditionally one of the first steps to digital transformation. 

For example, OCR is the main technology IDP employs to classify documents into appropriate categories. OCR works by analyzing the light and dark areas in the source material looking for clues about the kind of document it is scanning. 

If one of the AI models in intelligent document processing classifies the contents of a PDF as data relevant to “invoices” — or even the contents of specific pages containing signature in a longer PDF, OCR will work to identify the light areas as backgrounds and the dark areas as either handwriting or machine-printed characters. OCR then turns the binary data it scans from the source into glyphs, characters, numbers, and symbols that IDP can use for further processing. 

As the final step, OCR converts the characters it detects from either the handwriting or printed texts into a data format IDP can use in document extraction software for the next processing phase. Even though OCR is decades-old technology, it plays an essential role in IDP.

IDP vs RPA vs OCR

Intelligent document processing is extremely different compared to older automated data processing systems. IDP can read and understand the context of the information it captures from documents — allowing organizations to automate a much deeper “stack” of document-related tasks than previously possible. 

Until now, most automation software has been robotic process automation (RPA). RPA works by recording simple point-and-click mouse movements to automate repeatable tasks using the UI. RPA requires a series of steps (called a “template”) to accomplish its more complex screen tasks. In the early 2000s, screen scraping software was one of the first use cases of RPA as a “band-aid” to create a “bridge” in between newer systems and incompatible legacy systems.

Today, RPA helps streamline some mundane, non-document-centric tasks performed on computer screens. Unfortunately, RPA tools and OCR are both limited to tasks that do not involve high-level decision-making. The biggest differences between RPA, OCR, and IDP are native AI intelligence and essential document understanding with expert-level context awareness. IDP is next-gen automation technology that has evolved from the need to go beyond RPA’s limited capabilities.

Both RPA and OCR are rules-based technologies that rely on traditional template approaches. Templates are useful for tasks and documents with well-defined structures, but once a design element of the user interface changes in either the software or the documents that a template is built on top of, the template breaks, and the automation has to be re-designed all over again. 

Since IDP is based on AI, data extraction is not limited by a rules-based approach. Intelligent document processing reads the contents of documents and learns from their contents just like humans, so it improves with every use. IDP can complement legacy point-and-click RPA tools for tedious screen tasks — but with an AI engine at its core, IDP is powerful enough to replace your RPA robots, entirely.

Key benefits of intelligent document processing

There are several reasons organizations should incorporate IDP. Here are some of the key benefits of intelligent document processing within modern workflow automation.

• RPA technology still relies on artificial intelligence, screen scraping, and workflow automation to accomplish anything greater than simple, screen-related tasks. Intelligent document processing has native AI built-in, so it learns and improves with every use.
• IDP will capture, classify, and extract essential data from structured, semi-structured, and unstructured documents. It then intelligently processes data with above-human accuracy.
• Intelligent document processing can integrate with traditional RPA as a sub-process to augment RPA tasks. When dealing with vast amounts of essential information hiding in documents, IDP is the ideal choice to manage it all.
• Legacy automation systems like RPA can’t keep up with the growth of unstructured data, nor can they accurately handle the hidden context of such data. Intelligent document processing unlocks all of the essential data hiding in these unstructured documents.
• Having access to unstructured data provides valuable feedback about customers and the experience of doing business with your organization. It also removes barriers in the way of critical document processes that create friction in the customer experience.
• Within a related workflow or automated task, IDP is non-invasive and easily integrates with internal applications, systems, and automation platforms.
• With IDP, organizations can begin speeding up their document-driven processes without compromising the quality of their service. With intelligent document processing, everyone wins.

The 7 steps of document processing automation

Data ingestion

1. The first step in intelligent document processing is intaking data from different sources. One way IDP ingests data is through a process called “data capture.”
2. For example, if your original document was a paper document, you might digitally preserve documents as binary image data in a few different ways. You could use a scanner to capture your data as a PDF file — or you could use your camera to capture the document as an image.
3. Regardless of the source, these digital copies can serve as legal original copies, making the data in digital records management systems even more valuable.
4. In document management systems augmented by intelligent document processing, data capture involves more than just scanning paper documents and taking photos. In an IDP workflow, document capture can automatically process collections of electronic documents in multiple versions and formats — allowing your records management system to become source agnostic.
5. For example, you might want to store the original Word document and have access to a PDF version for future reference. Integrating Intelligent document processing in digital archives gives immediate access to important information in documents from the very moment of data capture.

Data classification

1. The second step in IDP figures out the type of data being processed.
2. Document classification begins by identifying the beginning and the ending of the source material — and then analyzes the content in between. In intelligent document processing, this involves classifying document types — such as invoices, purchase orders, identification documents, contracts, bills, resumés, letters, etc.
3. If the source is a PDF document or a scanned image of a document, an OCR algorithm trained in nearly 190 languages interprets the data by capturing characters, numbers, and symbols from the data it scans.

Data extraction

1. Once IDP has classified the file type and analyzed the format of the data source, the most important step in intelligent document processing is text extraction.
2. Intelligent document processing applies trained AI models — using natural language processing (NLP), machine learning (ML), and Deep Learning (DL) algorithms — to extract valuable context from the source. Document extraction targets specific elements of interest — such as addresses, tax details, monetary values, technical specifications about products, or hard-to-find clauses in legal contracts.
3. Intelligent document processing then enters the data it captures into a database or stores it for future use.
4. From here, the data could be translated into another language, processed in a different format, or automatically entered into any number of enterprise application databases.
   • E.g., spreadsheets, accounting systems, Enterprise Resource Planning (ERP) systems, Enterprise Content Management (ECM) systems, Customer Relationship Management (CRM) systems
   • Complementary technologies — such as Robotic Process Automation (RPA), and cloud SaaS services to augment your current business workflow automation.

Intelligent search

• This process uses inferred logic in the user’s search criteria rather than exacting search terms to retrieve essential details from a collection of documents that share similar characteristics.
  • For example, if searching for “early-payment discounting clauses, ” intelligent document processing will find all of the early-payment discounting clauses across all of the relevant contracts.

Document validation

• This important step takes specific elements within documents and compares those details with other documents.
  • For example, applicant addresses extracted from an application form can be compared to all addresses on utility bills and bank statements. Another example might be validating that invoice totals are accurate by comparing details in matching purchase orders.
  • Some IDP vendors like Automation Hero may offer a human-in-the-loop environment with a no/low-code interface to let users approve validations and correct errors before they occur — closing the gap between people and the automation. As a bonus, this will also fine-tune the training of AI algorithms used in the data capture, extraction, and classification stages.

Data analysis

1. This process performs data analysis on documents by breaking down the information and even document processes into separate component parts.
2. An AI model then looks at the relationships between parts and analyzes how they are interconnected to provide a deeper insight into their contents and related document processing workflows.

Automation Hero’s Hero Platform_ provides a beautiful dashboard to display these document workflow metrics.

Workflow integration

1. The final step of intelligent document processing is exporting the information to internal data systems and integrating other business process workflows.
2. After the data is released to your internal data systems, the information is immediately available to be accessed by other departments — freeing up your entire organization to focus on taking quick action and providing efficient service to customers.
3. From here, intelligent document processing could be integrated with RPA systems as a sub-process to augment RPA tasks with AI/cognitive capabilities.
4. Automation Hero’s API will serve as a “fabric” connecting all workflow services together with other business process automation systems. And once you have created your automation, it can be offered via an application programming interface (API) as a service for other departments to use. 

With Automation Hero as the IDP fabric integrating multiple APIs into a unique business process — you will have an infrastructure that open-banking APIs, FinTechs, and other third parties can use.

Getting started with IDP

When introducing new technology into your business workflow automation, it makes sense to introduce IDP technology on a limited basis so you can test how it works, first. 

If you are shopping for an intelligent document processing solution, it’s important to choose the right IDP technology that can fit all of the needs your organization and your people will have. 

We offer a few suggestions to get started:

1. Research — get exclusive access to ebooks, guides & more in our free resource center.
2. Read our blog — our AI experts have written articles to guide you every step of the way.
3. Learn about Automation Hero — our IDP platform has dozens of use cases. Filter by industry to see what it can do for your organization.
4. Speak with an expert — tell a sales expert about your use case now.
5. Get a personalized demo — schedule a demonstration and our Heroes will get in touch!

In the meantime, start planning how you might use Automation Hero’s built-in intelligence to address multiple use cases across your entire company. Our Human-in-the-loop attended automation provides security for employees to approve any automation that also trains your AI model. Just imagine training your AI model with the wisdom of your company’s top experts!

When working on process improvement, it is essential to always have your key success metrics in place. But what are these metrics, and what should the focus be?

Nov 09, 2022 by Kevin Shepherd

When working on process improvement, it is essential to always have your key success metrics in place. But what are these metrics, and what should the focus be?

For example, do you focus on the technology you are implementing? Or should the focus be on the process and how to automate each element? You might also want to consider the people who will see the change you implement into a given process.

Finally, you will want to look at the overall bigger picture and the efficiencies everyone involved will gain from any of the controls you add.

Keep in touch

Use case: automate invoice processing 

Of course, all of this will depend on the process you are automating — so let’s imagine an accounts payable team with thousands of invoices to process each month.

Streamline your document processing automation

In terms of efficiency, automation engineers have the following to address in this use case:

• Faster invoice entry to the ERP with automated data extraction.
• Immediate account matching and coding.
• Near-instantaneous approval processes with automated document routing and minimal steps for end-users to follow.

Control the entire workflow automation

On the control side, automation engineers will have to manage where invoices are stored, so that Accounts Payable can see all approval statuses for each invoice at any given time — without introducing human errors into the approver allocation process.

The technology behind this automation will require advanced intelligence that accurately recognizes incoming documents as “invoices” — and we will want the technology involved to also extract critical information of interest as soon as it identifies the documents as such.

Our optical character recognition (OCR) has context-awareness

Automation Hero’s built-in AI intelligence applies custom context-aware OCR technology with native intelligent document processing (IDP) to instantly analyze, classify, extract, and sort documents by type into the appropriate categories.

The data extracted from these invoices could then be automatically entered into the fields for the proper financial software — which greatly speeds up tedious, manual data processes.

But is there more to consider than just these benefits?

Validate invoices with above-human accuracy

Absolutely! Once our automation has extracted the essential information from the invoice, we need it to analyze and validate this data with expert document understanding.

For example, we may want the technology in this automation to intelligently look up account information stored in other places and compare it against historical purchase order data for comparative analysis and quality assurance purposes.

Some IDP vendors like Automation Hero may offer a human-in-the-loop environment that allows users to approve validations and correct errors before they occur.

As a bonus, this will also fine-tune the training of AI algorithms used in the data capture, extraction, and classification stages of your invoice workflow automation.

Workflow integration, seamlessly

Finally, your automation could run through some approval logic that sends the document — or just the essential data extracted — to your current business workflow automation — such as a Robotic Process Automation (RPA), a cloud SaaS service, or another complementary technology for further processing.

From here, intelligent document processing (IDP) could be integrated with RPA systems as a sub-process to augment your current RPA tasks with AI/cognitive capabilities. Your workflow automation could even send the invoices through different approvers and departments depending on its coding.

Guarantee a layer of security with our attended automation

For this, we could use a human-in-the-loop element again for an extra layer of security and quality assurance. Automation Hero offers a human-in-the-loop skill builder with a no/low-code environment that closes the gap between people and your automation.

The human element in our platform dynamically manages all aspects of the automation — which allows you to implement dedicated approval or audit stages in-between tasks that are based on rules you define. This enables users interacting with your automation to correct errors before they occur. Human-in-the-loop also fine-tunes AI algorithms used in the capture, extraction, and document classifications.

After the data is released to your internal data systems, the information is immediately available to your other departments — freeing up your entire organization to focus on taking quick action and providing efficient service to customers. Imagine making your invoice entry and approval processes 5x more efficient while knowing all of the approval stages for every invoice at every step of the QA process!

Doesn’t that sound nice?

Case study: how human-in-the-loop improved the customer journey 

Now, you could argue that some data-driven processes don’t actually need any human element, interaction, or knowledge — and that intelligent automation is all about the technology and whether it can solve a particular problem or not. However, we recommend considering this argument from an Efficiency and Control strategy point of view.

A client using our Hero Platform_ to replace older, legacy middleware recently reduced their per-record throughput from around 20 minutes to less than 2 seconds!

This huge efficiency boost allowed the client to process more invoices in record time with a return on investment that paid off in dividends. Their record-breaking boost in performance accelerated the data to their Order Management system — which streamlined not only a better document organization process — but the entire customer experience of ordering and doing business with them as well. Customers were happier and employees could finally focus on higher-level business initiatives rather than on tedious, mind-numbing tasks. 

With intelligent document processing in your automation, your organization could begin speeding up its document-driven processes for any type of document without compromising the quality or the security of its services — so everyone wins!

At Automation Hero, we recommend first looking at the bigger picture across your entire organization. Start by planning how you might use Automation Hero’s built-in intelligence to address multiple use cases across your company. Next, take inventory of where your data sits, the skilled workers using it, and the tasks they must complete with the data. Finally, can you add Efficiency and Control to any of these processes?

Just imagine teaching your AI model with the wisdom of your company’s top experts!

Next steps

We offer a few ways for you to get started:

• Speak with an Expert — Tell a sales expert about your specific use case now.
• Get a Personalized Demo — Schedule a demo & our Heroes will get in touch!