UiPath Document Understanding Reviews

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.

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.

We implemented UiPath Document Understanding for our first project with a pharmaceutical insurance company. They were receiving invoices from over 2,000 different vendors in a variety of formats on a daily basis, and they wanted to automate the process. We are receiving invoices in their email, and we are automating the download and processing of these invoices. If the confidence level of the automated data extraction is low, a user or client can correct the data according to the invoice and then submit it. The data will then be improved. We will be automating this project in two parts: first, reading specific emails and downloading the attachments; and second, checking if the attachments are normal documents or invoices.

We have implemented UiPath Document Understanding for two companies: one in the insurance industry and the other in the financial industry. We have completed the document creation process, which includes OCR and automatic signature imposition by different lawyers on the finalized documentation. We also use Document Understanding to read the document after analyzing it, and we then update the PDF with a front page signature and other components. This is a small process, but the first project was very large and we gained a lot of business from it. It was a very good project overall.

We process between 100 to 200 documents per day using Document Understanding.

The documents include checkboxes and barcodes. Some of our vendors only provide handwritten invoices, which Document Understanding could not read. These invoices had to be processed manually by the user.

UiPath Document Understanding can handle varying document formats including handwritten documents.

We have implemented a machine learning model to sort vendor names and important information related to those vendors into our system. When the model encounters a vendor that it has already seen, it automatically grabs the important information from the invoice. The model is continuously training on the new data that it receives, so it can become more accurate over time.

Machine learning was very good. We don't think we can implement without any ML model.

We integrated Document Understanding with Dynamic CRM so that the information extracted by Document Understanding is automatically input into CRM.

The amount of human validation required is based on the confidence level of the ML model. Each time human validation is required, the ML model learns and the need for human validation decreases. At the start, the ratio of documents requiring human validation was 50/50, but this ratio decreased with each iteration.

Document understanding helps reduce human errors. For example, if we receive 150 emails daily, we must analyze and process each email accordingly, such as sending invoices, checking invoice values, and investigating all relevant information. We must then read each invoice and enter the data into the system. This is a very active task that requires around 15 people to perform daily. Document understanding has reduced the need for human interaction by allowing us to automate this process. Now, only one person needs to analyze the email invoices. Once the invoices have been checked and analyzed, they are passed to a UiPath bot, which handles all the subsequent steps, such as reading the invoices and entering the data into the system.

Document understanding has helped free up staff time.

UiPath provides a useful feature that allows us to classify documents as invoices or not.

If the confidence level is low, we can check it and provide the product value to move forward. In this step, the user can sometimes skip or delete pages, especially if we receive a large PDF with the first two pages being invoices, followed by some relevant documents, and then more invoices in the same period. This is a very good feature of UiPath Document Understanding, as it allows the user to skip pages within the PDF document to move forward. For example, the user can specify that the first two pages and pages nine and ten are invoices.

UiPath Document Understanding's ability to read handwritten files has room for improvement.

The price of Document understanding is high, and we are constantly struggling to get our clients to use it because they find it to be expensive.

I have been using UiPath Document Understanding for one and a half years.

UiPath Document Understanding is stable. We have not encountered any downtime.

UiPath Document Understanding is scalable.

The technical support was helpful.

Positive

The initial deployment was straightforward.

Two people were required for deployment.

The implementation was completed in-house. We have a large team that includes technical consultants, architects, and developers.

The last time we implemented UiPath Document Understanding the price was high.

I would rate UiPath Document Understanding six out of ten.

Our client has PDF invoices and we use the solution to extract the details from them. We are using it in finance and health care. We have about 16 templates that we process now. The data is in semi-structured format and we mostly process things like signatures and tables. Out of the 16 templates, about 12 are completely processed automatically.

It has helped us automate finance statements and invoice billings.

Another benefit is that it has mostly helped reduce human error. We have a criteria of 75 percent matching. Out of 10 PDFs we have been getting eight PDFs with at least 75 percent matches. It has also helped free up staff time.

The taxonomy and Validation Station are among the most helpful features for us. If anything is extracted incorrectly, we can manually extract it there.

And we have included the AI Center for our customers to interact with PDFs to be extracted. Based on the approval or rejection feature, our customer can determine which kinds of PDFs they can automate.

I also like the table extraction feature. UiPath is very good with structured data.

Handwriting is more complex. We have not been able to get handwritten signatures correctly extracted in different languages. Our customer is in Dubai, and the solution cannot accurately process signatures in the local language. But it is a great tool for handling structured and semi-structured formats.

Another of the disadvantages is that we cannot include another tool. For example, with ABBYY extraction, we can integrate the process with any other product. We can integrate Document Understanding using JSON templates, but it is a bit of a complex model to extract the data from the JSON.

There is also room for improvement in long-running table extraction. If a table continues for more than 10 pages, in some cases, we have observed that it only extracts six or seven pages and skips the last pages.

I have been using UiPath for about 10 years.

Overall, the product is stable.

In our case, the use of Document Understanding is restricted to a particular group of users, around six or seven people.

The technical support from UiPath has been pretty good in the last year. It has been a very good experience. 

We used Azure DevOps for the deployment and we faced some issues regarding the deployment with UiPath and Orchestrator. We had a very good response from the UiPath technical team.

There is some room for them to improve the speed of the response because we often used to get late responses. But the resolutions are good.

Positive

We were using ABBYY, but it is more like a developer's tool with everything a developer needs for extracting fields. But we can train and retrain Document Understanding. In that way, I feel it's a better tool.

The pricing is reasonable.

As for additional costs, the solution is based on OCR, and sometimes the OCR cap is exceeded. It's not a major cost. Per month, we will have two or three scenarios like that. With ABBYY, once the cap was reached, we had to wait until the next day to use it again.

We did not evaluate other solutions. Using Document Understanding was a requirement from the client's side.

In terms of human validation for Document Understanding output, we have a limit of 75 percent correct scenarios. If it is below 75 percent, the user will be notified.

The solution doesn't require any maintenance unless the client requires more fields to be extracted. Only then are there changes that I need to make.

My advice is that if you are starting to learn about Document Understanding, you need to learn more about the taxonomy and what fields you are extracting. You need to have clarity on which position you are extracting, as it mostly depends on the position.

UiPath Document Understanding is a key tool we use to automate document processing for our clients, including tasks like invoice and sales order processing. We can create multiple workflows for different clients and even use it internally. To handle even more complex documents, we've also built custom models for specific data extraction needs.

UiPath Document Understanding helps our clients streamline data entry by accurately and consistently extracting information from both paper and digital documents. This extracted data can then be seamlessly integrated into their existing ERP or finance systems, eliminating the need for manual data input.

Document Understanding automates the processing of our invoices and sales orders, which are our most common tasks due to their semi-structured format. These documents share a typical organization with common fields, though we also handle custom documents like certificates and licenses across various states.

Document Understanding helps us process thousands of documents each day.

Thousands of documents are processed completely by Document Understanding each month.

Machine learning is the core of Document Understanding, where trained models extract data from documents. For simple forms, basic tools suffice. But in most cases, Document Understanding's built-in machine learning tackles complex documents. Generative AI features are new and basic for now but hold promise for the future.

The human validation required for Document Understanding outputs depends on the use case. We aim to get above 80 percent without human intervention. For some use cases, we're well above 90 percent. In just one minute, the human validation process can be completed for the small percentage of tasks, typically between 10 and 20 percent, that necessitate it.

While average handle time varied greatly before automation ranging from eight to ten minutes or even longer, data entry for sales orders with hundreds of line items was especially slow, taking up to 30 minutes per order. Automating the process with API integration significantly reduced this time to just one to two minutes.

Document Understanding helps significantly reduce human error, especially in crucial tasks like sales order entry for manufacturing clients. Mistyped entries can lead to incorrect production, rework, and unhappy customers. While the error reduction varies, estimates range from 18-20 percent to potentially as high as 40 percent in some cases.

Document Understanding significantly reduces manual data entry, freeing up staff time. For instance, one client eliminated a data entry role entirely, allowing that employee to focus on higher-value tasks. This is a consistent benefit – whenever we implement Document Understanding, the staff previously responsible for data entry can be redeployed to different teams, roles, or more strategic work.

UiPath's Document Understanding significantly reduces the effort needed to train a machine-learning model for our documents. Their pre-built models and tools for customizing them minimize the need for manual tasks like creating bounding boxes and training on uncommon examples. This allows us to achieve high accuracy and certainty in data extraction with minimal human intervention.

The rising annual licensing cost of UiPath's Document Understanding product is a major turnoff for users. This constant price fluctuation incentivizes companies to switch to competing solutions, potentially hurting UiPath's market competitiveness.

The technical support has significant room for improvement.

I have been using UiPath Document Understanding for three and a half years.

The technical support is bad.

Negative

Document understanding projects deliver a significant return on investment in two ways. First, by automating data entry tasks, they free up customer service agents to focus on client interaction, improving service quality. Second, this automation can eliminate the need for offshore data entry teams, potentially bringing those jobs back onshore and saving tens of thousands on overall costs.

UiPath's pricing model is complex and based on AI units, which are consumed during model training and use. This makes it difficult to predict costs upfront, unlike a simpler pay-as-you-go model offered by Microsoft. With UiPath, you purchase a bundle of AI units, and even if you don't use them all, you're still charged for the entire bundle. This can be less cost-effective compared to Microsoft's approach where you only pay for what you use.

I would rate UiPath Document Understanding nine out of ten.

We use UiPath Document Understanding to process purchase orders and order confirmations.

We implemented UiPath Document Understanding because we wanted a more efficient way to process the documents we were receiving.

We process purchase orders and order confirmations in PDF format. The documents we process are tables and standard data.

We process anywhere from 150 to 200 documents per day with UiPath Document Understanding. We process between 60 to 70 percent of the documents completely with UiPath Document Understanding.

We do not use Document Understanding for signatures or handwriting, but we do use it for various document formats, which it handles moderately well.

The AI and machine learning with UiPath do the job moderately well.

We leveraged API calls to seamlessly integrate UiPath Document Understanding with other systems and applications within our environment.

UiPath Document Understanding has helped save us a lot of time.

We required human validation between 30 to 40 percent of the time.

Prior to implementing UiPath Document Understanding, it took us one hour to process each document. With the implementation, processing time has been reduced to one minute, saving us 59 minutes per document.

UiPath Document Understanding has helped reduce human error.

UiPath Document Understanding has helped save our people time to focus on other projects. This time savings is equivalent to the productive output of a full-time employee. 

I like the clear and organized way in which UiPath has structured the labeling process, as well as the user-friendly development environment.

Several areas require improvement in UiPath. The licensing model poses a significant challenge due to the fee charged for posting a model, which impedes the development of productivity-enhancing models. Additionally, UiPath's pricing is substantially higher than that of its competitors, approximately three to four times higher.

UiPath's AI quality needs substantial improvement. Several issues have persisted for at least two years, such as the inability to handle breaks in tables. When data in a table extends from the first page to the second, UiPath fails to process it effectively. Additionally, UiPath struggles to handle multiple items.

I have been using UiPath Document Understanding for over two years.

I would rate the stability of UiPath Document Understandings six out of ten, because of the AI issues.

I would rate the scalability of UiPath Document Understanding eight out of ten.

The technical support is good.

Positive

The initial deployment is straightforward. We implemented a hybrid model of on-premises and cloud deployment throughout the organization. The robots are physically located on-premises, but their operations are managed and controlled through a cloud-based platform. One person was required for the deployment.

We have seen a return on investment with UiPath Document Understanding.

UiPath Document Understanding compared to the competitors is high. I would rate the price nine out of ten with ten being the most expensive.

I would rate UiPath Document Understanding seven out of ten.

We have seen time to value with UiPath Document Understanding.

UiPath Document Understanding requires constant maintenance because of the AI issues. One person can handle the maintenance.

I recommend thoroughly testing UiPath Document Understanding to verify that the organization is genuinely deriving value from the tool and to assess whether the AI model can effectively handle the capabilities advertised by UiPath.

I use the solution in different ways. There are different ways to deal with the documents and extract data using OCRs, et cetera. I focus on document understanding with machine learning capabilities baked into it. 

I have worked on different types of documents. Both structured and unstructured documents as well as purchase orders, invoices, and time sheets as well. Depending on the type of the document or form, the solution would be different. If we have a structured document to retrieve the known information, then that would be easy. We would just use regular methods and simple extraction methods to get the data. However, if the documents are unstructured and the formats of those documents are different, that's where we would be using understanding and machine learning capabilities.

I've used it in healthcare, finance, and investments.

We're dealing with multiple vendors right now. When we deal with multiple vendors, each vendor has different structures for documents, and some of them provide data within papers while some of them provide just data as paragraphs. So for each of those types of documents, we have to extend the data based on the need.

How much the process is automated depends on the use case. It depends on the scenarios of the different types of formats of users. Sometimes there are different functionalities that we have to use within UiPath. If we're using Action Center, we probably would be able to automate almost all documents and that's where we would need users' input to validate the right information. In any case, we would be able to automate the majority of the documents since the bot would be able to expand the data. In such cases, the process might be longer. That's where we have to spend more time. However, if the documents are structured, it would be very easy for us to identify the data in those documents and then build the workflows.

The ability of UiPath to handle variant document formats, including handwriting is decent. Extracting the data is fine. However, it depends on what solution you implement and how much time you are ready to spend to implement that solution. If we have a plan to involve the Action Center within the solution, then that's where we would need a few inputs from the users to make sure that the automation is working fine, and that's where you would be able to achieve the majority of the success rate. However, if it is something that we just want to automate and we don't want to involve humans in it, then that's where we might result in a few exceptions as the data might not be right. That's where we would face some challenges. 

The machine learning capabilities have been quite fine. We've been able to digitize and classify documents and use them in our processes. That said, when compared to AI fabric, that's where we need to spend more time creating our own packages for it and then deploying those packages into the workflows. We need to make sure that we have a handle on all the documents.

I have found that 70% to 80% human validation is needed if we are trying to deal with sensitive data or if we are trying to deal with some confidential data. In those cases, we need to make sure that all the data is right. So as long as the document is structured and is well defined, and well-formatted, we might leave it 100% to automation. If any of these details are confidential or if any of these details require evaluation, then we will need user interaction. 

The validation process can be pretty quick. A code document doesn't take much time. It depends on the data. In any use case, I need to extract more than ten or 12 fields. If we're dealing with that number of fields, I'd estimate we need between two to 22 minutes.

The Average Handle Time, the AHT, depends on the cases. If there's no human involvement, then it would definitely take less time. If there's human involvement, the product could at least reduce the effort. The human involvement may drop from 20 to 30 steps down to one and they are just needed for validation. That scenario shows UiPath as a great time saver. Initially, we used to take 15 to 20 minutes to work on a document. Now, with automation, it might only take two to three minutes. It saves 65% to 75% in terms of time. 

There's still a chance of human error or tasks taking a few minutes as users would need to input some data into the document in the Action Center. That said, there is definitely a reduction and less of a chance of human errors over there. 

It's helped us free up time for other staff projects. That's the intention of implementing automation. Users can reduce their time on tedious tasks and focus on more important business needs. 

Document understanding works fine, however, it depends on what information you are providing it with. If the data is right, the data is good, however, in cases where the data is not right, it gets a bit difficult.

They could work on the digitizing and classification of documents. That would play a major role in document understanding since that's where we need to make sure that bots are able to extract data from multiple formats or multiple structures of the documents. The better they get at data extraction, the better we can automate. 

I've used the solution for two and a half years. 

I have worked on different use cases. There was one use case where I just worked on a similar type of document that had data entries for more than 300 to 400 users. I have worked with more than 400 to 500 documents of different formats and different sources. That's where I had to use machine learning packages. Right now, we are working on documents, with set formats and different structures, however, the volume of the documents is about 400 to 500.

They have a good technical team that supports the needs of the developers. 

Positive

It could actually take one year if you consider the effort which we had put into building that solution as well. We have a few developers working on it. We wouldn't see a return on investment within eight to ten months as we would just be starting with building the processes. 

We have witnessed some ROI. For example, it reduces review work from 60% to 70%. That's where the full-time employees would definitely save their time and can then focus on much more important business needs, which could help them get more projects or increase revenue as well. That's where you would see the most ROI. 

It definitely reduces the hours of work. The bots have the potential to also cover offline hours. 

While it depends on the use cases, the document understanding is good. I'd rate it eight out of ten.

The product is very, very nice. The tool relieves the data entry team from manipulating data. It shortens the time of extracting data by 70 to 80%.

UiPath should localize the built-in features for supporting Arabic scripts so we do not rely on third-party tools. It will take some investment. Sometimes, when we use third-party tools, we need to collect multiple samples of the same document in Arabic. It creates some errors in understanding. 

Sometimes, we need to do multiple formats for the same document. It happens due to the nature of the Arabic language. For the same document in English, we only supply one template. The output of handwritten documents in Arabic is very poor, regardless of the solution. For computer-printed documents, we need to tune the system.

I am using the solution currently.

The product is stable.

The tool is scalable.

We were using our own intelligent business processing solution. It is more convenient to use because it's our own product, and we rely on Amazon Web Services. It provides big advantages for Arabic scripts. It is more understandable.

The product is easy to implement to an extent. If we are familiar with the input, processing, output, and how to declare variables, it's easy. If we are new to UiPath, it would be somewhat difficult to understand. UiPath Academy and certifications are very important.

The difficulties in implementation depend on the environments, document sources, document types, and customer understanding. Sometimes, customers think the product can pass anything without configuration, which is not true in some cases. We have to be very clear with the customer about the type of documents and layout they can expect. Two working days are sufficient for configuring, extracting, and linking the process. One person is enough to deploy the solution.

The pricing depends on the context of the project. UiPath is a pricey tool for small customers. If the customer agrees on the cloud product, we discuss the cost accordingly. In some cases, even though the customer sees value in the product, return on investment, productivity, and enhancements of their processes, they decide not to choose UiPath due to budget constraints. They choose a different vendor.

Sometimes, the customer is small, but we could see the potential for using the tool because they might have multiple processes. The price we offer is based on the context and the size of the opportunity we get for references. Document Understanding has helped us automate processes like contract management, reconciliation of invoices against purchase orders, claims management, and HR processes.

We process 100,000 to 500,000 documents in a year. We also had a project for Dubai Customs to reconcile the customs clearance, which involved 500,000 to 1,000,000 documents. These documents contain tables, bar codes, dates, and checkboxes. Tables might span over multiple pages, and we must capture it completely. It becomes challenging sometimes. If there is an invoice of three pages, everything must be captured, but sometimes the values are inaccurate.

Around 70% of our organization’s documents are completely processed automatically. We haven't used the product for signatures. Customers often see from a productivity point of view. If their employees process ten documents an hour, and the tool processes 50 documents an hour, using the tool is an advantage for them.

The tool does not work for integrations. It extracts data. When we extract data successfully, we rely on other business tools for integration. Document Understanding serves as a first milestone in the process. The tool at the second milestone would pick up the extracted data and post it according to the processes or applications used.

We need human validation of Document Understanding outputs three out of ten times. It does not take more than 20 to 30 seconds per document. An employee would take ten documents per minute. RPA could handle 30 to 40 documents per minute. Document Understanding has helped us reduce human errors by 70% to 80%.

Document Understanding has helped free up staff’s time for other projects. From a development point of view, it has freed up 70% of staff's time. From an employee's point of view, it has freed up 80 to 90% of their time. We saw the value of the product after a couple of projects, especially when we implemented it and saw the value for ourselves. As a customer, vendor, or implementer, when we see the execution, we see the value.

If someone wants to take advantage of this technology, they must think multiple times about different scenarios. They must centralize the capture or the source of data. They can use any product that is easy to use and configure and link it to the processes. They must sync the templates and the configuration multiple times and link it to the automation strategy.

Overall, I rate the solution a five out of ten.