UiPath IXP Reviews

We use UiPath Document Understanding to extract insurance document data related to vehicle accidents in a pilot project to better understand who is at fault. We open the documents, connect with the insurance companies, and obtain the police reports describing the scene of the accident. We extract the information into a single spreadsheet to charge the third party.

Before implementing UiPath Document Understanding, humans required around 15 minutes to extract the required information per document. Now, using Document Understanding, we can process 11 documents per minute with 98 percent accuracy, saving a significant amount of time and money.

The data we extract using Document Understanding relates to vehicle accidents. The insurance company uses this data to charge third parties involved and recover most of their money with minimal human intervention.

We are extracting information from 50 insurance documents to prove the concept of showing coverage for those involved in the accident.

The information from the insurance company is in PDF format and is structured so that we can easily extract information from it. The other public policy documents vary in quality depending on the state and district in Brazil. We have 27 states, and these documents are unstructured, making it difficult to extract data from them. Sometimes they are in image format, and other times they are in PDF format.

The 50 structured PDF documents from the insurance company are all 100 percent automated using UiPath Document Understanding.

We are impressed with the machine learning and AI capabilities of Document Understanding. We can use APIs to identify whenever a vehicle accident is reported, which helps our business.

Using the AI we can access vehicle accident reports and train the machine learning to understand the majority of the documents we extract information from to help improve our business processes.

We use the UiPath platform orchestrator with an API to integrate with OpenAI.

UiPath Document Understanding has helped our organization demonstrate to potential customers how the solution, together with AI and machine learning, can benefit their businesses.

Before Document Understanding our process took 15 minutes per document and now it takes under one minute.

Document Understanding has helped free up our staff's time to work on other projects.

UiPath Document Understanding is easy to configure and user-friendly.

It is easy to insert a human into the loop.

Explaining the UiPath Document Understanding process to my customers is easy for them to understand.

UiPath Document Understanding produces a high accuracy rate of 98 percent.

The interface has room for improvement.

I have been using UiPath Document Understanding for ten months.

UiPath Document Understanding is stable.

We can easily scale UiPath Document Understanding by adding licenses.

We have only used the UiPath User Community site for support on issues or questions related to Document Understanding.

We previously used Microsoft solutions and switched to UiPath Document Understanding because it is the best. 

We can demonstrate a return on investment for our customers with the huge amount of time saved using UiPath Document Understanding.

UiPath Document Understanding is expensive, with the basic annual package including 6,000 documents and the next package including one million documents, a huge price difference.

We evaluated Automation Hero and a Microsoft platform. We chose UiPath Document Understanding because we are a UiPath partner in Brazil, and as a global leader, UiPath can support us on all of our projects.

I would rate UiPath Document Understanding nine out of ten.

We are currently conducting a proof-of-concept using UiPath Document Understanding, so we must use human validation for all processes to demonstrate the accuracy of the bots to the customer. Human validation takes five minutes per document.

Three professionals are required for the maintenance.

We primarily use it for invoice processing as well as receipt processing or expense processing.

It has advanced capabilities compared to other competitors, whether Blue Prism or Automation Anywhere. We can select the ML models based on the type of process that we are automating. 

It has helped process around two thousand documents per month, in formats including PDF, text, image, and even handwritten documents. This solution has played a significant role in drastically reducing human errors by ensuring that 95% to 98% of tasks are done through the system.

They can include some features in utilizing the product of assessment understanding, or more specifically, a better efficient handling of the ML skill, which right now is not that efficient. The integration could also be simplified as it's somewhat complex at present.

I have used the UiPath Document Understanding for one year.

They often ask us to go through the documentation first instead of directly explaining or addressing the root cause of issues.

Neutral

We did not use any previous solutions for document undertsnading. This is the first one we have used.

The initial setup is not straightforward. One needs to have knowledge to set it up.

The implementation team involved an architect, senior developers, and another architect.

Return on investment could be high if you are using the product for multiple processes. The more automation you achieve, the more ROI you will see.

It is expensive/ It's not easy to accommodate in the budget.

We didn't evaluate other options since we didn't have time to explore that much.

I would rate UiPath Document Understanding a seven out of ten, although the platform doesn't accommodate half ratings.

Our clients use UiPath Document Understanding for their purchase order creations.

We need to process invoices received from vendors. This involves posting the data to SAP and creating a virtual file. To extract data from the vendor's PDF documents, we utilize UiPath Document Understanding.

The documents we process using UiPath Document Understanding are invoices and purchase orders.

The documents are in PDF format. Some documents include handwriting and screenshots.

Around 80 percent of the documents processed are completely automated without any human intervention.

UiPath Document Understanding helps handle signatures.

The call center teams automated a process where they used to manually identify configuration items in service notifications submitted by users. This manual process required a team of more than three people to analyze over 70,000 records per month. To address this inefficiency, we implemented Forms AI to automate the process. This automation has directly benefited end users.

UiPath Document Understanding has streamlined invoice processing. Previously, processing invoices was a time-consuming manual process. Employees had to read each invoice, create corresponding entries in SAP and CRM systems, and then route them to accounts payable. This required multiple resources. UiPath Document Understanding automates these tasks, reducing processing time and errors.

In the past, a team of more than 10 people was required to manually process purchase orders. Now, thanks to UiPath Document Understanding, only a few people are needed to validate the complete information and resolve any issues.

Before UiPath Document Understanding, we used over eight resources to process documents. Each resource could only handle around 20 documents per day, limiting our total daily capacity to 160 documents. However, since implementing automation, we can now process over 600 invoices daily.

UiPath Document Understanding helps reduce human error by over 90 percent.

UiPath Document Understanding has freed up staff time to work on other projects.

Our clients are satisfied with the time to value.

The most valuable feature in UiPath Document Understanding is the identification of the fields column in the PDF documents.

UiPath Document Understanding has challenges with handwriting and screenshots.

I have been using UiPath Document Understanding for 2 years.

I would rate the stability of UiPath Document Understanding 8 out of 10.

I would rate the scalability of UiPath Document Understanding 8 out of 10.

We have a dedicated account manager as our primary point of contact for any support we require.

Positive

We faced some challenges with the initial deployment and had to get support from the product team.

Our clients saw a return on investment after the second year of use.

While Robotic Process Automation tools can be expensive, UiPath Document Understanding is no exception. However, the long-term benefits often outweigh the initial cost.

I would rate UiPath Document Understanding 8 out of 10.

The integration of AI in UiPath Document Understanding will enhance its ability to read screenshots and handwriting within PDFs in the future.

We're currently working with several internal clients across various industries, not just the financial sector. We're expanding our reach to assist them with both compliance and audit matters. By targeting a wider range of clients, we aim to help them implement effective tech ops practices.

Currently, we are using UiPath Document Understanding in our client's finance department.

We have a 4 person support team that monitors and maintains UiPath Document Understanding.

A recent use case was for an insurance company based in the United States. For that, we were recording or collecting the data from the insurance brokers who used to fill their documents. We had to find a few segments on the basis of them. We were collecting the data and confirming whether those brokers were coming from an authentic source. They had a stamp or a legal insurance number, and we were maintaining a few dictionaries containing the images of their signatures. Once we received a document from a broker, we passed the whole document into different segments, and then we just validated the signature part to see if it was coming from an authentic source. We validated that the signature and the image looked similar, and there was at least 80% similarity.

We were extracting the IPIN number from the Microsoft Intelligent OCR. We were able to extract almost 85% to 90% of the numbers. It contained digits that were being imposed on a stamp that we had provided to them, so there was less complexity because there was less human intervention. They were not manually writing those numbers where it could be a bit difficult for us to diagnose whether it was a four or a nine. With a digitized number imposed on the stamp, it was a bit easier for us to read it out. This is the use case that we just finished and deployed, and it is processing 150 to 230 requests on a daily basis.

I have mostly been automating banking, financial services, and insurance (BFSI) processes.

With Document Understanding, we have been able to process both structured and unstructured documents. It does not matter whether a document is structured or unstructured. The only thing is that data should be concise, and it should be constant. If we are getting 70% unstructured data and 30% structured data, we are good to go, but we should be aware of how much structured and unstructured data we are getting. If we get a picture, then based on that, we serialize them. It is either a standardized process, or we have to use some APIs or some logic to make it structured. We initially filter out based on the picture view. If the visibility of the data is less than 45% or 65%, it means that the data is not as structured. We then move it to a different folder to process it later. If it is standard and structured, we process it immediately. We do not need to worry about the chunks. There is a positive output in our hands when we have achieved 45% or 65% of our target. We can then work on the remaining part to make it more centralized, so it is a bit easier for us.

With Document Understanding, we are able to handle things like varying document formats, handwriting, and signatures. The approach we take depends on the nature of the data that we are getting. For example, a requirement from the insurance company was to mandatorily verify whether the source is authentic or not. They had metrics at their end to say who were the legal brokers and who were not legal brokers. It was not challenging for us there to extract that data from their backend because they already had all the information. We just used their APIs. We just read the data out and compared the data from there.

In terms of human validation required for Document Understanding output, we needed to finalize if the data coming from Document Understanding was correct or not. If it was not correct, we moved it to the process folder. As we marked it as incorrect, it asked us the exact location that we were looking for to get, for example, the grand total. We defined that, and then it got stored in its knowledge base system, and then it got processed. It can be processed as an attended bot or as an unattended bot. It totally depends on how much data or knowledge it has been gaining from humans, and day by day, with more knowledge, it becomes more capable of processing the data independently.

The average handle time depends on the number of cores that the operating system has. If you have 14 to 16 cores CPU in your machine, 3 minutes would be required to process a 3 MB file. It also depends on the number of pages or the complexity. If data visibility is clear and the page number is not more than five, it can process the file in 3 minutes.

After automating the process with Document Understanding, it takes two minutes to process a single PDF. I do not have the exact data of how much time humans used to take. They were probably putting in nine hours per day, and after automating the process with Document Understanding, they are putting in two hours per day, so they are saving seven hours per day. Monthly, there is a saving of 150 hours.

In terms of error reduction, in the beginning, we were getting a lot of machine errors, but as the process got smoother and the knowledge base system stabilized, the machine errors reduced, and the human errors also reduced.

Document Understanding helped free up the client's staff’s time for other projects. Before automation, they had seven people on their team, and after automating the process, they cut their budget and reduced the manpower from seven to four. They were able to free three staff members for other projects. They saved 35% to 45% of manpower.

Document Understanding has better machine learning or ML capabilities, and that is why I prefer Document Understanding.

It would be much easier if UiPath increased the count of pages. Currently, they are allowing one million pages for $10,000 per month. I would prefer to increase the page count or reduce the dollar count in terms of processing the documents. I would prefer $6,000 per month for processing 2 to 3 million pages per month. It will then be much easier for companies with a low budget to use this product.

I have been using UiPath Document Understanding for more than two years. 

It is stable. They always come up with a proper and stable approach. 

It is scalable. If they increase the page count or file count, our solution will not have any issues, and it will process them. The more you train the bots, the more the efficiency of the processes.

They were helpful. If you have a paid license key, they will help you a lot.

Neutral

I have worked with IQ Bot, but as Document Understanding got more stabilized and more well-known in the market, I started to move from IQ Bot to Document Understanding. I used IQ Bot when Document Understanding was not there. In 2021, when UiPath came out with the Document Understanding solution, I left IQ Bot behind and started developing my skills in Document Understanding. I have expertise in Document Understanding and IQ Bot. Document Understanding has better ML capabilities, so I prefer Document Understanding.

My whole six years of development experience is in the BFSI sector. I did only one retail sector project, but for that, we did not use UiPath Document Understanding. We used Magic OCR, which is not a Document Understanding or IQ Bot model. Those who are not willing to invest that much amount in UiPath or Automation Anywhere prefer to automate by using some open APIs. We used Magic OCR to scale the picture into a proper frame. We used to scale them as per our dimension or as per our frame, and then we used to perform all those activities that were required. If they came up with a cash memo, we had defined a few parameters for the grand total, discount, advance payment, overdue payments, and so on.

UiPath provides two options: the first one is a public cloud and the second one is on-premises. It is based on the package that you purchase from them. If you purchase the cloud version, then they will share with you the public cloud. If you go with the on-premises option, they will ask you to arrange a server. They deploy or install Orchestrator on the IIS server, and from there, we operate it.

We are using it on the cloud because AI fabric and lots of functionality are available on the cloud. Our cloud provider is Microsoft Azure.

The deployment process depends on the approach or SOPs of the company. The company I have been working with recently has its own DevOps team, but one of the companies I have worked with did not believe in the DevOps part. The developers were the ones gathering the data, developing the requirements, and fulfilling those requirements by doing the development and then deploying it on the production. It depends on the company model. I have worked on both scenarios, and there was not much issue with the deployment of the Document Understanding model. It is already based on the package. We added that package and then directly deployed it on Orchestrator. From Orchestrator, we operated them.

In terms of maintenance, it does not require any maintenance from our side.

The ROI is in terms of efficiency. There are time savings for humans and the accuracy of the results.

I would recommend Document Understanding. I prefer Document Understanding over IQ Bot as they have multiple flavors of machine learning models. If a person is capable, they can also easily achieve the same thing with programming.

I would rate Document Understanding an eight out of ten, but they can improve the costing part.

We primarily use UiPath Document Understanding for finance processes, covering both transactional procedures and reviews. One recent example involved streamlining the onboarding process, including pre-boarding, onboarding itself, and post-onboarding follow-up. The company typically requests various documents from applicants, which are then processed manually. However, due to variations in country-specific standards and requirements, HR personnel often spend significant time handling these documents.

Our solution involves creating a seamless online portal where applicants can upload their documents. These documents are automatically screened by the system and directly uploaded into the company's EFP system. This significantly reduces manual work for HR and finance teams. Similar automation applies to processing invoices from various suppliers in different formats. We leverage machine learning tools to train the system to read documents with varying complexity levels.

Essentially, the system mimics how an HR professional would process documents, capturing their knowledge and integrating it into the automated workflow. This reduces processing time and workload for both the company and its clients. Our focus lies on automating tasks within well-defined contexts, making us less involved in product development activities at this stage.

Initially, our clients were primarily interested in UiPath Document Understanding out of curiosity about its potential. Their main focus was on automation, but we also engaged in discussions about the broader benefits, such as time savings. We highlighted that a 30 percent time reduction allows them to focus on tasks with higher value. However, what I found even more crucial was the impact on lead times. Manual processes often lead to work stoppages, delays, and roadblocks. Automation, even partial, can significantly reduce lead times. For example, a task that previously took five weeks can now be completed in just a few days. While security concerns may necessitate some manual intervention, such as allowing the head of HR to retain some oversight, the overall process becomes more streamlined over time.

Most document processing is automated, improving efficiency and ease, especially in back-office transactions. However, areas like marketing, where business plans require creativity and flexibility, remain manual for now. Where documents are stored, and manipulated, and data needs to be extracted and distributed across various systems, the process is often cumbersome. Traditionally, someone would manually open each system, which is time-consuming, especially considering most companies have hundreds of them. This is where tools and systems come in, able to connect across platforms, read data from various sources, and make interpretations. The level of automation depends on the company's maturity. Sometimes we leverage their existing data, while other times we implement techniques to extract more insights. Ideally, we'd be able to predict and anticipate future needs, but for now, with clients, we're primarily focused on analyzing data and helping them automate their processes. This is the first step.

The volume and types of documents we process with UiPath Document Understanding vary depending on the client. For smaller companies with a few hundred employees, the needs are different than for large international corporations with thousands. These international clients often have diverse locations with varying processes and systems, making automation more challenging. In HR departments, for example, the sheer number of applicants and their associated documents can be immense. Ensuring accuracy is crucial, as mistakes can have significant consequences. Finance departments also present unique challenges, as data might be hidden or incomplete. This requires them to be at a certain level of maturity to benefit from automation effectively. The complexity of documents is another key factor. While machine learning can handle many documents, it has limitations. Some documents might be too time-consuming to train on, making the investment in automation impractical. This can leave a portion of documents requiring manual processing. Overall, UiPath Document Understanding automates the processing of the majority of documents we handle, around 80 percent. However, for the remaining 20 percent, manual intervention is still necessary due to document complexity, data limitations, or training time constraints.

UiPath Document Understanding helps us extract data from various document formats, including tables, handwritten content, checkboxes, and barcodes. However, poorly legible documents present a challenge. Automating 100 percent of documents is currently impossible due to diverse languages and handwritten sections. Our current approach categorizes documents into easy, medium, and complex based on difficulty. We prioritize easy documents as complex ones require significant time investment with uncertain results. Unfortunately, machine learning for document processing can be time-consuming. We prioritize documents based on return on investment. For example, if we have 10,000 documents, we might skip two unique ones, even if theoretically similar to others. If only two or three data points are needed, but the structure drastically varies, processing might not be worthwhile. Imagine a 10-page phone bill invoice with a minimal value of €10. Investing time in such documents offers a minimal return. Therefore, we focus on documents offering greater value.

Around 70 percent of the documents are processed automatically using UiPath Document Understanding.

UiPath excels at connecting with various systems compared to some competitors. This is crucial when promoting it to clients, as in our case with our UiPath partnership. All our developers have UiPath training, and we strongly believe in its capabilities. However, internal legacy systems within companies can pose challenges. For example, a client with an EFP system they plan to replace might hesitate to automate now. Integrating UiPath with basic IT infrastructure is essential, and frequent system changes demand flexible solutions. While UiPath is adaptable, we need to demonstrate its compatibility with various systems to gain client buy-in. This will make them more open to automation. It's important to remember that company maturity levels influence their automation openness. While UiPath has no control over that, adapting to ever-changing environments requires flexible systems. By showcasing UiPath's ability to work with different systems, we can overcome client hesitation and secure their trust in our proposed automation solutions.

It typically takes clients about a month to see the benefits of UiPath Document Understanding. We start by showing a demo. We often use the UiPath website itself for inspiration, and we also consult with UiPath staff to see if they have any pre-built demos for specific areas, such as onboarding. We create short, simple videos tailored to their needs and showcase them to both HR and IT personnel, giving them a glimpse of the solution before implementation. While deployment ultimately requires its timeline, we can typically craft a process description within a couple of weeks, allowing for a swift rollout. The tools themselves are relatively quick to use. In my experience, the main bottleneck usually lies within the client organization itself. Functional teams are often busy, have competing priorities, and sometimes change their decisions. Navigating these internal dynamics can be time-consuming. The actual development time for tasks like process mapping, decision-making, and technical implementation is relatively short, typically measured between 10 to 20 days. However, building consensus, convincing stakeholders, and developing a compelling business case can take considerably longer. Internally, clients often encounter both promoters and detractors – individuals who welcome or resist change. These internal dynamics are often the biggest hurdle. However, once the decision is made, we can quickly create a targeted demo showcasing the added value UiPath Document Understanding can bring.

On average, human validation takes just a few minutes. Additionally, the number of full-time equivalents was reduced by 30 percent - that's a significant achievement. Lead time has also decreased dramatically, much more than the FTE reduction. A small department of three people can now do the same work with two, freeing up one person for other tasks. It's important to note that lead time reduction depends on the specific case. Theoretically, in a perfect scenario with seamless workflow, automation tools operating 24/7, and no disruptions, a five-fold decrease in lead time is possible. However, real-world scenarios often involve unforeseen issues requiring manual intervention, limiting the maximum achievable reduction. Still, significant lead time reductions are attainable through consistent improvement efforts.

When it is done well we can reduce and improve the accuracy through automation helping to reduce human error.  

For me, the most valuable aspects of UiPath Document Understanding are its time efficiency and minimal human intervention.

UiPath Document Understanding's ability to handle diverse document formats, including scans and signatures, needs improvement. While it can be learned from various examples, the accuracy suffers when presented with poorly scanned, multi-generation photocopies. Companies often struggle with repeated scanning and photocopying, leading to documents illegible even for humans. While the software can be trained on various signatures and handwriting styles, it requires a significant number of high-quality samples for optimal performance. This training process necessitates time and effort, and human verification often remains necessary. Initial excitement about the automation potential can be dampened by the reality of data quality limitations. Collaboration is key. While the tool has limitations, companies must also invest in providing high-quality training data to optimize results. Simply expecting the software to adapt without proper resources is unrealistic. Improvements in both tool capabilities and data quality are needed for truly reliable document understanding.

I have been using UiPath Document Understanding for one year.

UiPath Document Understanding is stable.

Up to this point, we have not encountered any scalability issues for UiPath Document Understanding.

Both technical support and the commercial team need to actively listen to clients. Simply pushing products onto them is ineffective and often unwelcome. We frequently find ourselves caught in the middle, mediating between UiPath and clients with differing priorities. This lack of unified communication creates the impression that neither side is truly listening to the other.

It's crucial to pay close attention to clients' specific concerns, as their needs often extend beyond a single product. They may have broader goals and considerations that we are unaware of. By actively listening, we can gain valuable insights and build stronger relationships.

Neutral

We implement the solution for our clients.

One of the biggest challenges we face with UiPath is the pricing structure. It's often opaque and difficult to understand the true cost involved. This makes it hard to have transparent conversations with clients, as any lack of clarity can raise concerns about hidden fees or manipulation. Our goal is simply to understand the pricing ourselves, but the complex structure creates an unnecessary obstacle.

Thankfully, the UiPath team recognizes this issue and is actively working with partners to improve communication and transparency. We've seen initiatives from their Chief Marketing Officer aimed at strengthening partner relationships, specifically addressing the pricing concerns. While they often propose pre-defined packages designed to sell bundled functionalities, these aren't always appropriate for every client's needs.

We've experienced situations where clients express interest in a specific solution but decline the complete package. When we relay this feedback to UiPath, they sometimes counter with larger, multi-year contracts that significantly exceed the client's budget and desire for a trial period. This makes it challenging to demonstrate the value of UiPath in a way that aligns with the client's initial request.

Ultimately, what we need is a more flexible and transparent pricing structure that allows clients to start small, experiment with specific solutions, and scale up as needed. This would significantly improve our ability to have open and honest conversations with clients and build trust in the UiPath platform.

We should pay closer attention to listening to our clients. In my experience, I've observed conversations between UiPath and clients where they clearly explain their needs. While UiPath naturally wants to sell larger deals, they should prioritize active listening. The client may not always be 100 percent accurate, but pushing big deals is counterproductive.

UiPath, of course, wants to secure larger deals with longer contracts. This is understandable, as automating for only 3-6 months wouldn't be ideal. However, clients often want to pilot tools first. They need to justify the investment to internal stakeholders and prove the added value. Selling them pre-packaged solutions designed for other clients, particularly those in different regions or industries, often proves ineffective.

Clients seek adaptable solutions that fit their specific context. Large companies with thousands of employees have access to numerous competitors. We can't assume they won't explore other options. While polite on the surface, they're actively seeking the best solution for their needs.

While UiPath offers excellent solutions, they sometimes fall on the higher-priced end compared to alternatives like Microsoft, which might appear more affordable on the surface. Clients who already have established contracts with Microsoft might be more inclined to choose their products unless we can effectively demonstrate the unique value proposition UiPath offers. This goes beyond mere cost and includes aspects like security, which is paramount in Switzerland. Clients often require data control and prefer on-premise or regulated cloud storage options.

Data security is a major concern for many companies. Cloud solutions, while attractive, aren't always universally accepted. Factors like industry regulations and legal requirements often dictate data storage options. Defense, oil and gas, and other sensitive sectors have stricter constraints imposed by their legal departments.

In conclusion, while larger deals are desirable, focusing on active listening and adapting solutions to each client's specific needs is crucial. Highlighting unique value propositions beyond cost, such as robust security and data control options, will differentiate UiPath from competitors and win over clients.

I rate UiPath Document Understanding eight out of ten. In my experience, UiPath Document Understanding stands out as a superior solution compared to other document processing tools I've encountered.

The future lies in leveraging artificial intelligence or machine learning to accelerate progress across various landscapes. Recently, we encountered a situation where technicians presented a series of documents with a medium-high level of complexity. They proposed running a machine for a month to process them, but this was unrealistic for management. The lead time for new document processing needs to be appropriate. While processing in a day is acceptable, dedicating a team for a month to a single document type is impractical. Scaling up operations requires flexibility and adaptability. For example, testing tools in one country and then scaling to another presents challenges due to different environments and document types. This necessitates a more powerful machine with faster processing and the ability to handle diverse document formats. Ultimately, such advancements will significantly improve the system's efficiency.

The amount of human validation required for UiPath Document Understanding outputs varies based on the client. While some clients may hesitate to trust complete automation, others recognize its potential. However, for sensitive tasks like contract reviews, they wouldn't send documents to external candidates without human verification. Therefore, the initial steps involve clarifying expectations with the client. During implementation, adjustments might be needed, and even after the tool is operational, some human involvement is typically built into the process for added confidence. Over time, as trust in the system grows, these checks can be gradually reduced. However, eliminating all checks could be risky.

Most of our clients prefer on-premise deployments and for any Cloud deployments, the servers must be located in Switzerland.

Many organizations fall into the trap of automation neglect. They implement new tools or processes, only to abandon them later due to lack of maintenance. While initial implementation may bring a sense of accomplishment, this approach ultimately fails to deliver business value. Beyond simply implementing technology, user adoption, and ongoing maintenance are crucial. IT systems should be seen as part of a continuous improvement journey, not one-time solutions. Analyzing processes, strategy, and people allows for ongoing optimization, where digital tools empower improvement instead of creating isolated interventions. To avoid the common pitfall of neglected automation, consider establishing a Center of Excellence. This central team can provide support, guidance, and expertise to local users, ensuring the system functions effectively and delivers lasting value.

Before organizations implement UiPath Document Understanding, they need to clearly define their desired outcomes and understand that successful implementation requires both adapting their documents and refining their processes. While it's tempting to see automation as a magic bullet for fixing dysfunctional processes, it's crucial to address underlying issues beforehand. This involves simultaneous work on process improvement and document optimization. For example, when I consider the HR department I worked with. The key was to first understand their existing workflow through process mapping. Then, we identified bottlenecks and potential improvement areas based on their feedback. While developing the automation, we also reviewed their document structure and eliminated unnecessary documents. This combined approach ensured that the implemented process and tools were efficient and streamlined. Simply speeding up a flawed process with automation often proves ineffective, leading to user dissatisfaction and a perception of failure. The problem doesn't lie with the tool itself, but rather with the lack of skilled staff who understand the processes they manage, their purpose, and the specific complexities of the company and its unique environment.

We use UiPath Document Understanding for P2P processes to extract document information for ingestion, processing, and classification.

The key problem our clients faced, which we were trying to solve by implementing UiPath Document Understanding, was the large amount of unstructured data in the events. They want a solution that can solve this problem right from the beginning, from the document ingestion phase to the document classification and streamlining the document for the data taken right inside the documents. So driving all those analytics and the ROI in the end is a major key asked by most of our clients.

Our clients deploy UiPath Document Understanding both on-premises for our banking clients and also on the AWS cloud for others.

UiPath Document Understanding has helped us automate a large number of accounts payable processes for our clients such as P2P and O2C. 

It helps us process many types of file formats primarily PDF. We are able to process a large volume of documents using UiPath Document Understanding.

In our P2P process, we have encountered some handwritten invoices. The handwriting text recognition feature offered by UiPath is good, and it has been very helpful in converting these handwritten documents to a more structured format. Apart from handwritten invoices, there are other documents that require extensive merging and sorting, which has always been a concern for many of our clients. I believe that UiPath has effectively solved this problem.

Our clients process over 90% of documents using UiPath Document Understanding are processed straight through without human validation.

When we use Document Understanding to analyze data, the AI works in the background to process the document seamlessly.

The ability to integrate with other systems and applications is really great. I would rate it a nine out of ten.

It has improved our clients' cost savings and time savings, in turn improving productivity and providing a better ROI.

The time required to manually validate information depends on the type of document. A handwritten document takes longer than a PDF file and can take up to half an hour.

The average handling time has improved and is now under ten minutes.

It is very effective at reducing human error in identifying incorrect fields in documents. This is where I think it excels. We have seen a reduction in human errors by up to 90 percent.

UiPath Document Understanding has helped free up staff time for other projects.

We typically see a time to value after four to five days from starting the process, but again, this depends on the process.

Document classification is very good. We have received great feedback from customers who use it to classify bank documents, sort them, and generate formal documents. I think the overall presentation of the final document is amazing.

UiPath Document Understanding can improve its handwriting and signature recognition. We have also been engaging with other intelligent document processing companies such as ABBYY and Kofax, which have superior features for handwritten text recognition. UiPath offers a good solution, but ABBYY has far more support for handwritten text recognition, especially in the latest version.

It is still in its infancy and has room for more advanced AI features.

They need to strengthen their relationships with IDP partnerships.

They should expand its library.

I have been using UiPath Document Understanding for almost six months.

UiPath Document Understanding is a stable solution that our clients are comfortable using.

UiPath Document Understanding is highly scalable if I want to extend support to the maximum number of subprocesses within a single process. Therefore, I believe there is no scalability issue.

The support is good but sometimes the response time is slow.

Neutral

The initial deployment complexity depends on the document. Therefore, we must be cautious when integrating with third-party vendors. I believe it takes more time to deploy critical documents with sensitive data. We must be very careful when choosing a vendor, such as AWS or Azure, to ensure that we can integrate with them successfully.

We use a team of three to four people for Document Understanding deployments.

UiPath is more expensive than ABBYY and Kofax.

Our clients are concerned about the volume-based pricing model, as UiPath charges more than other vendors in the market.

I would rate UiPath Document Understanding seven out of ten.

UiPath Document Understanding requires maintenance from time to time, and we are currently experiencing a slowdown in the oral solution. Therefore, I believe that maintenance is required. Perhaps they need to develop a newer, more intelligent, and more efficient version, as Kofax and ABBYY have done. The same team of people that deploy UiPath Document Understanding also handles the maintenance.

There are other vendors who are excelling further in the intelligent document automation space. They offer more advanced capabilities and AI intelligence than Document Understanding, which is still an evolving solution. When we read customer reviews and have first-time conversations with clients, we notice that they often start by naming vendors like ABBYY, which are known for their technical expertise in the IDA space.

In a medical healthcare department, when we need to retrieve digital documents, we need to classify them. The first step is to use AI to understand what type of documents we're dealing with. Once we've identified the template, we can extract information using specific OCR tools. Depending on the confidence of the extracted results, we may need to apply additional OCR, use a more active tool, or pass the document to an agent for review if the AI doesn't recognize a specific element like the "person page of the commission." Finally, the extracted fields are classified within the system and organized into different folders. This is the process I'm using with UiPath Document Understanding.

Document Understanding can complete each document within one second.

It can be applied to the healthcare industry to streamline the processing of medical documents. This includes scanning and applying OCR to convert physical documents into digital formats.

We can tune the AI component to improve the quality and accuracy of the documents being processed.

Typically, the AI process involves several steps. Firstly, it recognizes the template, which essentially identifies the input format being used. Secondly, it applies rules configured in a JSON file. This file specifies details like the expected fields for the recognized template, such as name, age, date of birth, and security address. The AI then reads and analyzes data from the specified location based on the recognized template. It applies the predefined rules to extract relevant information and search for the required fields. If the input doesn't match any known template, it employs more general search methods to locate the desired information. This is the core functionality of the internal AI component.

Of the 1,000 documents we process, 90 percent are completely automated.

My three OCR tools each incorporate three AI components. These components work in tandem, with the activity determining which AI component takes the lead. For example, if the first AI requires a minimum accuracy of 86 percent and encounters text with 85 percent accuracy, it passes the task to the next AI component. This next component employs a different OCR tool in an attempt to achieve the required accuracy. If it still falls short, the task is then routed to a human agent.

Our integrations leverage robust API connection services. A single, secure authentication method protects access to JSON files. Requests are sent and product responses are seamlessly handled. This API-based approach provides faster and more efficient integration compared to manual interface interactions.

UiPath now includes a document understanding AI components, eliminating the need for third-party solutions like ABBYY. This allows for quick and automated extraction, analysis, and template recognition of information from various documents. By training the system with diverse examples, the AI component can become highly efficient, similar to ABBYY's global OCR capabilities. This is a significant improvement, as it eliminates the need for additional integrations like ABBYY within UiPath projects.

I found UiPath Document Understandings' ability to understand varying document formats to be good. I had no issues with the templates I was using.

Using AI and machine learning can significantly speed up the recognition of new formats, templates, customers, or entities introduced into our process. It is particularly beneficial when dealing with low-quality documents, which often require manual intervention. By implementing a machine learning model at the beginning of the process, the system can learn from successful agent solutions and incorporate them into future scenarios. Clear feedback, including agent ID and task details, further enhances this learning process. As a result, machine learning can help save time, reduce costs, and improve overall process accuracy. This makes it a valuable tool within UiPath.

Less than ten percent of processed documents require human validation. However, when customers provide input that falls outside pre-defined templates the usual 90 percent of cases, the system cannot recognize it and fails to notify agents. This means a new template will be implemented to include human-agent collaboration when training AI models.

The validation process depends on the specific template and the data being acquired. If all data is extracted from the entire template, the validation process can take less than one minute.

The manual document process took us around ten minutes and now with UiPath Document Understanding, the process is within seconds.

Since implementation, human error has been reduced by 30%.

UiPath Document Understanding has helped save 50% of our time in instances when no human validation is required.  

The quality of the input documents is crucial because sometimes healthcare providers prefer automated processing rather than human review. However, this preference depends on the complexity of the resolution required and the document type e.g., JPEG, TIFF. I find the quality of the input documents as the most valuable part of the automation.

At the end of the process, we classify documents in our external application, similar to a CRM system. This classification is based on the documents stored in the new system. The results of classifying patient documents within UiPath Document Understanding need to be more accurate.

I have been using UiPath Document Understanding for three years.

UiPath offers excellent technical support due to its high-tech nature and the complex needs of its customers. This support is crucial for several reasons. One such reason is the customer success plan, which provides dedicated API support and a specialist focused on existing customers. This fosters close communication between the customer and UiPath, facilitated by two individuals who actively monitor and manage the customer's needs every week.

Positive

Previously, we used manual processes for all our tasks. We transitioned to UiPath Document Understanding due to its integration of AI components. It is more flexible to our needs.

We saw a return on investment within three months of deploying UiPath Document Understanding.

The pricing structure is based on the number of robots installed. While a single robot may suffice for some customers, others may require more depending on their processing capacity needs and desired turnaround times.

The cost per license is significant, approaching ten thousand dollars. While not inexpensive, for high transaction volumes, the potential savings can be substantial.

I rate UiPath Document Understanding an eight out of ten.

The primary use case revolves around processing invoices. In Israel, where the solution is region-oriented, the invoices typically involve multiple languages within a single document and may also include various currencies. The capability of handling such diverse linguistic and currency elements is a notable strength of UiPath Document Understanding in this context. Through its implementation, our goal was to minimize manual tasks significantly and reduce the time required for invoice processing.

Up to this point, Document Understanding has been applied primarily to automate invoice processing in our implementations. For the customers for whom we have implemented it, the emphasis has predominantly been on invoice processing. This is because, within the customer's value chain, these processes are perceived to deliver the most significant value.

In terms of the types and volumes of documents processed with Document Understanding, the volumes are measured per page rather than per invoice. We typically handle a range of 50,000 to 100,000 pages. It's important to note that invoices, which occasionally consist of more than two or three pages, are encompassed within these volume metrics.

Typically, the document format comprises a header, a table, and often a summary, along with occasional total figures. This basic structure is effectively handled by Document Understanding, excelling in processing both headers and tables seamlessly.

Approximately seventy to eighty percent of our customers' organizational documents undergo complete and automatic processing.

The benefits are straightforward– it eliminates the need for physical forms on the table. This simplicity instills a high level of confidence in the model, and I foresee a promising future for it. It stands out as an excellent solution for companies, particularly those dealing with a substantial volume of invoices and vendors from diverse sources.

It has liberated time for other projects. Previously, we needed three to four people for validating invoices. Now, we have scaled down to one part-time person, who, for the most part, is engaged in other responsibilities. Invoicing tasks occupy only around five percent of their work time, handled intermittently.

The most valuable aspect is the AI training model, which distinguishes itself by offering a more transparent and controllable approach compared to other products on the market. Unlike some alternatives, this model allows precise retraining of machine learning instances. It provides visibility into the training process, enabling control and the option to retrain multiple times as necessary. In contrast to comparable products, this transparency and control contribute to enhancing the precision of the training model.

Forms AI performs admirably, posing as a strong competitor to Microsoft's PowerApps and other similar products in the market. It is straightforward and versatile, yet there is room for enhancement in certain design features that could improve user experience.

Document Understanding seamlessly integrates with other systems and applications within the environment it operates. Its integration capabilities extend beyond RPA modules, ensuring smooth and trouble-free connections with various components.

Human validation is required for Document Understanding at the beginning of Document automation journey, constituting around thirty percent of the overall process, while the tool handles the remaining seventy percent and document straight through processing improver further with model retraining. Notably, the retraining feature is a crucial and valuable aspect of the platform. This feature allows for retraining based on the validation actions performed by human validators. This is particularly significant because it enables refinement of the model in cases where documents are validated with low confidence. Some of the platforms lack the capability to provide confidence levels for field and data recognition, making this retraining feature a valuable asset for businesses seeking precision and efficiency in document processing. The human validation process for each document typically takes only a couple of seconds. The validation requirements are easily identifiable, allowing you to point to the specific area. Typically, pointing to it triggers a quick refocus of recognition to a different part, making the validation process efficient and straightforward.

The average handle time before implementing Document Understanding was approximately between three to five minutes, but after automation, it has significantly reduced to less than a minute, possibly even just a couple of seconds. This improvement covers the entire process, including validation, data exchange, mailing approvals, and more, all seamlessly happening in the background. Beyond the time savings, the automation also substantially reduces rework caused by human errors, enhancing the overall efficiency and accuracy of the process. As per the customer, errors do occur at times, and the associated risk is considerably high. However, the implementation of Document Understanding effectively mitigates this risk, eliminating the potential for errors.

I wish to have more pre-trained modules available in various languages. For instance, while Document Understanding currently supports Hebrew for Israel, I would appreciate the addition of pre-trained modules specifically tailored for different Hebrew-related forms. This enhancement could prove to be quite beneficial.

I have been working with it for three months.

The system is highly stable, especially since it operates on the cloud. We haven't encountered any disruptions or issues.

When discussing Document Understanding and RPA processes, it's essential to highlight that it's a scalable solution on the cloud. The scalability it offers is truly exceptional, making it arguably the best in the market.

The technical support is outstanding. In Israel, we have a local UiPath office, and they are incredibly helpful. Their responsiveness is remarkable, and if there's ever a need for assistance, they promptly provide valuable support. I would rate it nine out of ten.

Positive

The initial setup falls in the middle ground – not overly complex but not entirely straightforward either. It requires an understanding of how to retrain the model and fine-tune both the OCR and the application.

Deployment time is a matter of minutes. The deployment process is straightforward as it involves a cloud solution. You order the environment, set up both the robotic and Document Understanding environments, and start working. It's a simple and quick process. Typically, the deployment involves one representative from our team and relevant subject matter experts from the customer's side. These experts are individuals directly engaged in the process, and often a reinsurance manager, functioning as a project manager, is crucial from the customer's side. It is imperative to have a subject matter expert from the customer's side because our team usually lacks visibility into their business processes and requirements.

Maintenance typically involves one person responsible for document validation. The specifics may vary based on the document type; for instance, if it's invoices, it's generally handled by a single person specializing in invoice processing. While I would assume similar patterns for other platforms, variations might occur with different document types, requiring different subject matter experts for each form. However, from the technical side, it usually entails the responsibility of one person.

In terms of Return on Investment, while we haven't quantified it precisely, the notable reduction in personnel from three or four full-time roles to one person handling the task part-time signifies a significant cost avoidance. Instead of letting people go, the approach involves reallocating them to other tasks, essentially avoiding around ninety-five percent of the previous budget dedicated to this particular process. The benefits in terms of cost-effectiveness and time efficiency are substantial. In the context of time to value, I'd estimate around two months to establish a production process, yielding impressive results ranging from seventy to eighty percent.

I think this timeframe needs to be considered with the multitude of invoices and vendors involved. We're dealing with processing invoices from over two thousand different vendors, spanning two different languages, including instances where both languages are mixed within a single invoice. The complexity is heightened by the inclusion of both right-to-left and left-to-right languages. Despite these intricate challenges, achieving the high complexity production process within two months is not only sufficient but also a commendable outcome.

For those interested, I would recommend undergoing a POC to truly experience and be pleasantly surprised by the outcomes within a couple of days. In an overall comparison with other solutions in the local market, I would confidently rate this as a robust nine out of ten.

In Italy, one of the most prevalent use cases involves automating the processing of invoicing cycles. The issue we aimed to address through the integration of this solution is essentially the manual input of data into systems by humans and the need for checks and balances between invoicing and other physical documents. Our organization is in the manufacturing realm. We primarily use Document Understanding to process invoices, specifically a common document in Italy known as the BDT. Regarding the document format, it includes structural elements like tables, checkboxes, and headers. Some documents may feature large tables, and the header contains essential information that needs to be extracted. In terms of volume, for a medium-sized or small company, we handle approximately ten thousand of these documents annually.

The advantage stems from the seamless integration of this solution with the UiPath platform. If a customer already has the standard, robust UiPath platform operating within their systems, adding these smaller modules is all that's required to enable Document Understanding. It functions as an integrated ecosystem.

It facilitated the automation of our data entry processes.

Approximately twenty to thirty percent of our customer's documents undergo full automation in processing.

In our scenario, Document Understanding operates independently as a standalone module, not integrated with any other systems. The robots, however, interact with the systems.

The average processing time, before and after automating with Document Understanding, improved in speed for a minute.

Human errors have been reduced by seventy percent.

Document Understanding has contributed to freeing up approximately seventy percent of people's time for other projects.

I believe the most valuable feature is the prebuilt algorithm for extracting information from foreign invoices. This efficient algorithm eliminates the need to create one from scratch.

It has the capacity to manage diverse document formats, including handwriting and signatures.

Leveraging artificial intelligence or machine learning capabilities is beneficial. These technologies excel in field identification tasks, even when adjustments such as moving or rotating the identified fields may be necessary. The primary benefit of artificial intelligence lies in its ability to handle various layouts.

Around 20 to 30 percent of cases necessitate human validation for Document Understanding outputs. The human validation process typically takes less than one minute per document.

There is room for improvement in handwriting processes. It should enhance the user interface for constructing extraction logic. It is not as user-friendly as other parts of the platform. An additional feature that could be considered is the integration with generative AI. The deployment process should be more user-friendly and streamlined. Scalability capabilities should be improved, as well.

I have been using it for two years now.

It offers good stability. The need for maintenance decreases with the highest level of stability.

Scalability is limited as it relies on the document layout. Integrating generative AI could potentially address this aspect. Moving an algorithm to another project without making significant changes can be quite challenging.

Our experience with its technical support is quite satisfactory. I would rate it nine out of ten.

Positive

The deployment process is not as straightforward as a seamless deployment, such as with App Studio. The number of people required for a project depends on its nature. Typically, one or two individuals are sufficient for most deployment cases.
Maintenance requirements vary depending on the projects. The team size can range from one person to five, six, or seven people. The deployment of this solution required one month.

I believe a six-month payback period is reasonable for the time-to-value. A shorter duration would be more favorable for customers.

I find the pricing to be somewhat on the higher side. User decisions are impacted by the pricing structure.

Overall, I would rate it nine out of ten.

We use UiPath Document Understanding for two purposes: extracting information from medical certificates issued by a prominent university in Singapore and processing invoices for a client in the logistics industry within their ERP systems.

We implemented UiPath Document Understanding to significantly reduce the substantial mailout effort. Approximately 20 full-time employees were previously dedicated to these processes, but after implementation, we were able to halve the number of full-time employees required.

We are capturing the header line items, which include the account number, invoice number, invoice date, and the line items: quantity, line item description, unit price, taxes, item number, and ZIP codes. This is a sales sector document. The medical certificate is an untested document, and we need to capture specific dates, the doctor's medical certificate number, and the student's name. We also need to check whether a checkbox is checked. There are no handwritten documents to extract.

Around 80 percent of our documents are processed 100 percent automatically.

Before implementing Document Understanding, the average time per invoice for manual processing, including invoice scanning and data extraction, was 15 minutes. Following automation, the processing time has been reduced to six minutes, with the specific duration varying based on the number of features on each invoice.

Document Understanding has helped reduce human error by 70 to 80 percent.

Document Understanding has reduced staff time by nearly 50 percent.

The best feature is pre-labeling, as it eliminates the need to manually label each data point. This saves a significant amount of time and effort. Additionally, the generated text is integrated seamlessly into the tool, making it easy to use. The documentation is also very clear and concise, making it easy to get started with the tool.

Over the past few years, I have observed that the invoice model consistently improves with each new UiPath release. There is still room for enhancement in capturing line items from invoices. This is one of the areas where I believe we can achieve near-perfect data capture. Unfortunately, the current accuracy rate for capturing line items is between 50 and 60 percent. This necessitates manual two-way matching, which is time-consuming and inefficient. I believe UiPath Document Understanding can still improve in this area, but overall, it is moving in the right direction.

Despite advancements in artificial intelligence and machine learning, there are lingering concerns about data privacy and security. These concerns can have a significant impact on users, particularly in terms of geographic restrictions and data policies.

The accuracy level we receive does not justify the price, as many competitors are offering much lower prices.

I have been using UiPath Document Understanding for three years.

While the stability is improving, it still needs to be enhanced in terms of model learning.

UiPath Document Understanding is scalable, but there is an aspect of training that requires attention. The model should be trained with a specific type of invoice to ensure optimal accuracy. For instance, if the invoices are in multiple languages and formats, the post-model training results may not be as effective as compared to training the model with invoices in a single language or two languages at most.

The technical support is good.

Positive

In the past, we used IQ Bot from Automation Anywhere; however, its output fell short of UiPath Document Understanding's capabilities. This discrepancy stems from the sheer size of the model we currently employ in the collection. UiPath Document Understanding's effectiveness is attributable to this factor. Additionally, UiPath offers superior analytical reporting capabilities, whereas Automation Anywhere falters in this regard. With Automation Anywhere, we were required to create multiple models, whereas UiPath allows us to utilize a single model for a collection of invoices with similar structures.

The deployment itself was straightforward. However, the deployment of the automation may have been more complex. In terms of the Document Understanding skills required for deployment, the process is straightforward. It doesn't require a lot of effort and can be completed in a day or two. For an experienced or certified individual, the deployment can likely be completed within a few hours.

To complete the deployment, a team of three people is required to work together.

The return on investment is seen within the first year of using the solution. 

UiPath Document Understanding is priced high compared to its competition.

I would rate UiPath Document Understanding nine out of ten.

Our clients experience time to value after approximately four months of usage because, initially, it takes some time to become familiar with the models and begin to see results.

The number of people we have using the solution is specific to the AP team or data finance team. Currently, we have two teams working on the solution.

Document Understanding requires ongoing maintenance in the form of model retraining. In the event of any encryptions, we may need to provide validation to the user. Additionally, we need to ensure that our models are regularly retrained.

Organizations need to carefully evaluate the scope and requirements of their Document Understanding initiatives. While existing Document Understanding models have demonstrated capabilities in specific invoice formats, it is crucial to test their performance across a broader range of invoice types. I recommend conducting a pilot test using a sample of 20 diverse but similar invoices to assess the models' accuracy and applicability.