UiPath IXP Reviews

The primary use case for UiPath Document Understanding is to identify and classify documents, extract metadata, and use this data in automation workflows. This can be particularly useful in HR processes where various documents need to be submitted during hiring, such as graduation certificates, IDs, etc. UiPath Document Understanding helps classify these documents and extract the necessary data to process internally or initiate workflows.

UiPath Document Understanding is a tool that assists with processing documents containing various formats, including tables, handwritten text, and checkboxes.

We leverage machine learning and artificial intelligence to train UiPath Document Understanding on various documents. This integrated capability significantly simplifies extracting and comprehending information from these documents within the platform.

We can incorporate human validation into the training process to ensure accurate data classification and extraction. This valuable step, while adding a few minutes to the process, allows for human oversight and correction, ultimately improving the reliability and quality of the results.

UiPath Document Understanding helps reduce human errors, especially in data entry functions. 

By automating processes, UiPath Document Understanding can save approximately 70 percent of the time.

Customers realize value quickly with UiPath Document Understanding, typically seeing results within a few weeks of implementation.

UiPath Document Understanding offers valuable features like Arabic language support, which is crucial for effective communication and automation in the Arabic-speaking world. Furthermore, its integration with advanced language models leverages AI to quickly understand and classify document data, improving efficiency and accuracy in processing information.

One area where UiPath could improve is by including pre-trained models for general-use documents specific to the Middle East. This would enhance the platform's utility in the region by allowing users to more effectively automate tasks involving documents in Arabic and other Middle Eastern languages.

I have been using UiPath Document Understanding for almost five years.

The premium support UiPath offers is speedy and satisfactory. However, basic support may be somewhat limited.

For cloud deployment, the initial setup is fast and straightforward. On-premises setup, however, can be complicated and requires more effort.

Deploying UiPath Document Understanding in the cloud takes only a few minutes, while on-premises deployment requires three to five days.

UiPath Document Understanding is considered a bit expensive compared to other options like Microsoft Azure, which can offer similar quality at a more affordable rate.

I would rate UiPath Document Understanding seven out of ten.

The main use case for UiPath Document Understanding is to automatize delivery notes, foreign invoices, and nonstandard documents in general that do not have an electronic format but are PDF. Most of them are delivery notes, invoices, packing lists, or shipping documents.

We are working with all the solutions from the UiPath, such as UiPath Platform, Process Mining, Test Cloud, Document Understanding, and now we are starting to experiment with Agnetic AI.

We do use Forms AI because it's a part of the UiPath package. We have used this in some projects. We are experimenting with Agentic AI, and we are at the very beginning of the journey. The first project started but is not completed yet. 

The main benefits that UiPath Document Understanding provides include an increase in productivity, as it takes over tasks from humans, especially in Italy, where finding qualified people is challenging. It frees up resources to engage in value-added activities and enhances quality and compliance, particularly for projects with clients that have large patent portfolios.

UiPath Document Understanding helps reduce human error during the working process. For example, while reviewing results with the client, we noticed an invoice had the date of tomorrow, so human error is an issue for compliance and audit reasons.

Action Center is the most user-friendly tool I've seen in the market for validating the documents and extracted data.

The main area for improvement for UiPath Document Understanding is pricing. They are cutting out the middle market because 60,000 pages are very high for that segment. To have that, they have to pay for this package, which doesn't make sense for many clients.

For handwriting and signature understanding, the quality has to be quite good. It can read something standardized, such as the handwritten bank paper for bankruptcy, but when it comes to shipping notes, it has problems. The handwriting performance is not always good, which is understandable.

I have been working with UiPath Document Understanding for more or less four years.

For stability, I would rate UiPath Document Understanding a ten because I have never had any issues with it.

I would rate it an eight out of ten for scalability due to cost reasons, as it does have an impact, but from a technological point of view, it stands well.

As a golden partner of UiPath, their tech support is a ten out of ten for us. We are also a golden partner for Microsoft, and I would not rate that a ten.

Positive

The setup process for UiPath Document Understanding is simple.

The small bundle that UiPath sells for Document Understanding is 60,000 units or pages. Clients need to come close to 60,000 pages a year or more.

The main competitor for UiPath Document Understanding is Microsoft Azure with Power Automate. I prefer UiPath Document Understanding because Microsoft Power Automate lacks good connection capabilities to automate end-to-end processes. It's time-consuming and often results in lost data due to a lack of infrastructure control setup by the client. This is a known problem that Microsoft is likely addressing.

Overall, I would give UiPath Document Understanding a nine because, despite pricing being a pain point, the solution is really great and yields good results. 

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.

Our old process involved manual data extraction from a large volume of documents with varying types and templates. This labor-intensive task required a significant workforce. We implemented UiPath Document Understanding to automate this process and eliminate the need for hand-coding solutions.

UiPath Document Understanding helps prepare data for machine learning by labeling documents used to train the models that will ultimately automate document processing tasks. We also use it to extract information from various identity documents like passports and ID cards, financial statements, credit card statements, and bank statements, and it can even process bank transaction data.

The documents we process using Document Understanding include tables and sometimes handwriting.

Around 70 percent of our documents are completely processed using Document Understanding.

The UiPath OCR works perfectly to extract handwriting, signatures, and multiple formats.

AI and machine learning prove valuable in training Document Understanding systems by analyzing data and identifying patterns, improving the system's ability to extract information from new documents.

AI streamlines Document Understanding by eliminating the need for manual coding. Users input documents into the AI, which then automatically classifies and extracts relevant information from each file. This saves staff over 20 hours a week.

UiPath Document Understanding integrates well with other systems.

For any newly implemented processes, human review will be necessary every day until Document Understanding is fully trained. The validation takes one minute per document.

The implementation of UiPath Document Understanding has saved us 50 percent of the time spent previously processing documents.

UiPath Document Understanding significantly reduces human error in processing documents, with complete accuracy achievable for standardized formats. However, its effectiveness in handling handwritten data varies depending on complexity.

UiPath Document Understanding helps save 20 percent of staff time to work on other tasks.                                          

The most valuable feature of UiPath Document Understanding is the AI Center.

UiPath Document Understanding, while effective for its own platform, could be even more valuable if it integrated with other commonly used platforms, allowing for a more universal approach to document processing.

I have been using UiPath Document Understanding for three years.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

The technical support is easy to access through the UiPath portal.

Positive

I've used IQ Bot from Automation Anywhere, Microsoft Intelligent Document Processing, and UiPath Document Understanding. IQ Bot and Document Understanding offer similar functionality, but only Microsoft's solution works across different platforms. We mainly use UiPath Document Understanding because it aligns with our client's preferred platform.

The deployment was straightforward. One person is enough for the deployment.

UiPath has a higher upfront cost, but its Document Understanding feature is not a significant additional expense compared to the overall platform.

I would rate UiPath Document Understanding nine out of ten.

We have six people that use UiPath Document Understanding.

I recommend UiPath Document Understanding to others.

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.

Positive

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.

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.