30 Aug 2022
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5 Tech Trends That Will Shape The Future Of Intelligent Document Processing

What does the future hold for document automation? When is the moment to start introducing docs automation tools in a company? You’re in the right place if you’re curious about the answers. This article covers the top 5 trends of Intelligent Document Processing, with comments from Alphamoon experts.

It’s no secret that robots speed up monotonous jobs, allowing humans to perform more challenging tasks. Even if you’re not a tech geek, automated processing and robotic automation have likely appeared in your feed.

Now, there’s an apparent stigma regarding automation as a whole concept. Employees are often negligent towards changes, while the initial costs of introducing new tools can turn into a bucket of cold water. That’s why most SMBs believe that automation’s reserved for enterprises where money flows like rivers of honey.

However, that could not be farther from the truth.

Process automation lets smaller players benefit from its strengths too, and Intelligent Document Processing is a perfect example of such an approach. Every business generates, receives, and processes documents of many kinds, disregarding the size of the operation. Implementing an IDP tool saves time on manual processing and helps companies gain knowledge stored within the pages. Many IDP platforms – including Alphamoon – offer tiers that give SMBs a shot at speeding things up with robotization.

As IDP becomes more available to larger audiences, specific trends shape the future of document automation. This article covers 5 of them:

  • Growth of more advanced AI engines supporting document processing
  • Implementation of SaaS features for the maximized ease of use
  • New use cases that emerge thanks to the growing capabilities of IDP tools
  • A shift to no-code and low-code solutions
  • Exploration of the world beyond IDP

Can we actually tell the future?

If you are a little bit of a film buff, you probably recall how sci-fi movies from the past millennium depicted the future. Flying cars, highly-advanced androids used as everyday helpers, and monumental city designs, with skyscrapers and neon-soaked streets.

Full-on cyberpunk galore, with skyline railways and dooms protecting the modern cities!

Welcome to 2022, where almost none of these wild creations turned out to be true.

Curiously enough, past generations imagined automation long before they had the Internet. And while many of their visions were incorrect, the books by Stanisław Lem, Jules Verne, and Aldous Huxley inspired modern designers.

Take Roomba, the cleaning robot, as an example. The idea for a house chores helper emerged almost 100 years ago.

Source: interestingengineering.com

Most inventions that sneak into our lives make our lives easier. Roomba simplified house chores, and IDP makes it easier for businesses to process documents. Both mechanisms derive from the same need to save time, business-wise and personal life-wise.

Most improvements happened because people wanted to spend more time on things that mattered to them. That could be family gatherings, hanging out with friends, or taking pets for long strolls in the forest.

Whatever the reason, time has become the universal currency, which ties nicely with the first trend.

1. Growth of more advanced AI engines supporting document processing

Given the growing complexity of international business, markets are hungry for state-of-the-art algorithms that combine machine learning, computer vision, and natural language processing to lower document processing costs.

In that case, these costs are often expressed in the time spent processing a single document.

Although the technological development of Intelligent Document Processing is rapidly moving forward, IDP remains ahead of its full potential. And while the leading platforms offer up to 98% of data extraction, the cutting-edge technologies that continuously improve AI’s capability are also vital in the progress of Intelligent Document Processing.

While we’re at it, recent developments focus on tackling one of the most challenging problems of AI – the narrow focus. Quantitative reasoning, believed to be an insurmountable obstacle to overwhelming natural language processing models, will require significant enhancement of model architecture. But AI engineers are getting closer to building more robust engines that mimic the human ability to think independently in solving mathematical matters, including processing language-based information.

Minerva, a project led by Google, deploys mathematical notation and principles of chain of thought and scratchpad prompting to handle reasoning as humans do. The model generates numerous solutions to one question and then utilises majority voting on sampled solutions of a given problem, and chooses the most commonly appearing solution as the final answer. Here’s a visualisation of the methodology (source: Google).

While Minerva isn’t perfect, it is a significant step toward the future we’re describing.

Let’s go back to IDP.

Since IDP’s fuelled by Artificial Intelligence, new models will advance in terms of more complicated examples of documents – handwritten notes, for instance – or larger files where data extraction will need to be supported by a better understanding of the text context.

Adam Gonczarek, CTO of Alphamoon, explains:

Many improvements will also happen in non-written content – graphs, images, signatures, QR codes, etc. Conventional OCRs (so-called legacy OCRs) cannot fully process documents when they include more complex visual content, such as nested tables or graphs (a typical case in invoice processing). Leading IDP platforms are now equipped with AI OCR components to solve these complexities.

Note: Alphamoon deploys Machine Learning to enable the AI OCR feature to process damaged documents. You can read about the most common document scanning challenges here.

Intelligent automation will also deploy various techniques to detect the high-risk, prone-to-error cases and pass them to be verified by humans. Those methods will include novelty and out-of-distribution detection, AI model calibration, and uncertainty estimation.

That way, IDP tools will become more trustworthy.

Finally, the growth of AI will let IDP developers deploy multimodal deep learning models for recognizing document types and extracting relevant information. Multimodal means utilizing different kinds of information – also called modalities – which are textual, positional, and visual contexts. In other words, that’s the reflection of the perception of humans, who look at both the textual and visual aspects to understand documents fully. Judging by the look of a doc copy, you can tell it’s a contract or a loan agreement. The cloud-based IDP platform developed by Alphamoon will be able to recognize any document thanks to the feature of document classification, also backed by AI and ML.

2. Implementation of SaaS features for the maximized ease of use

Judging by the sheer popularity of various tools that bring process automation, IDP will be getting more accessible. But democratizing access to IDP requires a systematic shift from on-premises implementation to SaaS services with self-onboarding.

The development of a SaaS model – software as a service – has been one of the most era-defining parts of the technological progress of the contemporary business world. Cloud-based software is generally safer, can be updated, and freed of bugs much easier, and often requires little tech background from a user.

So despite the complexity of IDP, companies will likely follow the steps of most SaaS tools in terms of self-service and quick implementation. If we rewind the clocks a few years, developers offered Intelligent Document Processing technology to enterprises where document processing was the most complex. The on-premises implementation could take months of work on just one project – the typical software house nightmare.

Furthermore, you don’t need to employ thousands of employees to experience a bottleneck in your paperwork speed.

We’re used to adding extensions and integrations to existing tools, and rightfully so, many business owners refuse to wade through complicated implementations, even in the case of big companies. After all, introducing new tools usually happens to achieve a specific improvement, save time or reduce costs.

Hence there are several benefits stemming from that change:

  • SaaS deployments are easier to maintain and keep up to date. Any major update can be deployed for all clients instead of single implementations that are more time-consuming.
  • Seamless scalability and more straightforward dealing with demand peaks. Deployment of any tool should be easy – as easy as a few clicks. Along with your company’s scale, each integrated software needs to be easily adaptable too.
  • Existence and leveraging the highest security standards for data governance, protection, and privacy. How secure is it to keep sensitive data in the cloud? After all, clients upload their financial documents, contracts, and many other types of docs. Many major cloud providers employ the latest security standards for in-transit and at-rest data protection, data segmentation, and additional security best practices to achieve necessary trust, constantly monitoring for potential new threats.
  • Supporting business continuity plans with various cloud regions and geographies. Geo-redundancy is a great way to achieve a high level of protection against unexpected and potentially damaging events that can happen to physical servers. It can also help with business continuity plans, especially when access to infrastructure becomes limited, a situation we’ve experienced during the COVID pandemic.
  • Faster onboarding for a wider audience like mid-sized companies. The undeniable benefit of private cloud deployment is the easy extension of potential use cases. Instead of designing a very complex tool for one specific need, the platform-based solutions are quick to master and further adapt.
  • Easier and more global access for users. Another widely popular trend nowadays is making access to the product as easy as possible. Social sign-ups among SaaS businesses, guest purchases in e-commerce, supporting web apps with mobile apps.
  • Cost-effectiveness for clients without the need to maintain internal infrastructure and a supporting team. The best tools are easy to master, so you immediately begin noticing their positive impact. You don’t want to hire a new position to introduce and maintain software that’s supposed to simplify things.

3. New use cases that emerge thanks to the growing capabilities of IDP tools

Do you know that most IDP technologies get better as they learn?

Like humans, AI needs a bit of time to master a process. But learning one capability often means that a new use case can emerge soon enough with some work and the right set of documents to learn.

Therefore, the growing trend in AI technologies within document processing will increase ready-to-use workflows. Here’s an example.

Debt collection companies work with many pretty diverse documents, including loan agreements, contracts, bills, receipts, and more specific docs such as the bailiff’s advance payments. One company can benefit from several use cases that altogether optimize document processing. For instance, debt collection automation can include skip tracing – a process of locating a debtor by sifting through various databases and documents – but also extracting data and sourcing it to other systems – like in the case of excess payments processing.

Note: Looking for ways to improve your collections? With Alphamoon, you can automate document search, improve your skip tracing processes and avoid unnecessary costs thanks to data anonymization.

When the company uploads its documents to the cloud, the IDP platform can be used to classify documents, scan and parse them, extract data, and so on. Each component is also used to fit the use cases we’ve explained a few lines up.

Let’s not halt your imagination.

IDP will soon be powerful enough to deal with more complex, harder-to-read documents and files.

In the near future, ML experts from Alphamoon expect IDP to grow to cover more out-of-the-box use cases with more structured complexity. While the tools are already used to transform documents such as invoices into data sources, IDP will provide an end-to-end perspective, potentially extracting data from several types of documents and combining it within one closed process.

We should also see IDP used in e-mail processing (billions of e-mails are sent daily), official letters, medical documentation, etc.

Adam Gonczarek adds:

New use cases will not only empower IDP to help more companies with substantial parts of the processes to tackle but optimize them end-to-end. The goal for such products will be to automate each step of the document lifecycle, from acquiring through data extraction, understanding content, and finally, decision making or response generation. Once that happens, companies will have it much easier to send, receive and process documents between various parties without integrating new tools. Intelligent Document Processing should be able to adjust the extracted data from any form of document and make it useful for different, streamlined processes.

4. Shift to no-code and low-code solutions

When Wix made it happen – by it, I mean creating a website with zero dev skills – the life of an early start-up founder became much more straightforward. Instead of investing thousands in a website that might perish after a week, Wix makes it ridiculously easy to design and publish a page without such a hustle.

Why should it be different for other products then?

Implementing document automation technologies will be systematically shifting to low-code/no-code solutions.

What does it entail from the user’s perspective?

Well, creating document workflows will be an easy-peasy job – even for someone who never fiddled with coding at all. Training AI models for data classification and extraction will not require a separate team of IT experts; hence business users will have control over how the tool supports their needs. That’s how Alphamoon experts indicate the democratization of document automation in general – access to technology for business users without the necessary tech-savvy background.

That’ll also create new opportunities for companies that implement IDP at an early stage. Process owners will quickly add new workflows (document processes) to meet the unique needs associated with the changes in the organization. Combine this with cloud-based deployment, and then any business will benefit from the complete flexibility of IDP.

Finally, this trend also opens the gates of Intelligent Automation for smaller companies that wouldn’t otherwise afford such expensive implementations.

5. Exploration of the world beyond IDP

All of the above leads to the complete transformation of IDP into a fully autonomous document workflows platform with intelligent document processing, knowledge extraction, decision making, and response/outcome generation. What started with reading documents and simplifying one fraction of the process as traditional OCR needs to grow into a complex business growth support tool.

Norbert Raus, CPO of Alphamoon, adds:

In addition to automation, IDP will lean towards building structured knowledge bases from groups of various documents. Users often need to find relevant information quickly, and we see that across many use cases we’re working with today. Thanks to AI-based search engines within these sets, such structured knowledge bases can enable users to get the answers, even to the most complex queries.

Conclusion

As you can see, there’s a bright future lying ahead for Intelligent Document Processing. If you were unsure of whether document automation is but a gimmick, this article hopefully proved you otherwise.

Get in touch with Alphamoon’s team today and sign up for beta tests of our new IDP platform.

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