Our initial use cases are mainly for finance. We are doing account payable, accounts receivable, reconciliation, and those types of things with the automation. In terms of accounts payable, we automate the invoice processing since it is an end-to-end. This means that the vendor will send an invoice to email, which will be picked up by the bot automatically. Then, it will extract the information from the invoices and post it to our SAP.
It is a web-based solution but hosted on our server.
The biggest driver was the cost savings. We wanted to improve productivity and save costs. Therefore, we gave most of the mundane tasks currently being done by a human to a bot. Some of the mundane tasks were reading invoices and keying in the data. We are talking about 15,000 documents every day. That is a huge volume that needs a lot of people. With the bot, it is just a fraction of the cost, because there is a huge savings in terms of manpower.
More for regulatory and audit purposes, we still require a human to approve it. Previously, we had the human to do it, then we had people cross-check it. Then, you have another layer of the approval. With the bot, we don't require two people. We only have the approval because we still have a person who does the approval, which we have to maintain.
The most important part is how easy it is to pair the automation. So, it is a canvas that is just drag and drop. You don't have to code, so it is a no-code to low-code solution.
It is good for simple tasks that we have done in the past, e.g., reading the invoices. A valuable feature is the document processing. Usually when we talk about document processing in the market, you just have OCR. Where once you extract the information, you need to program or do some type of data wrangling to actually get the value of it or process it. For Jiffy.ai, they have the machine learning behind it, so we didn't need to code one by one. For example, if you have 5,000 vendors who are sending you different types of invoices, then we are not talking about 5,000 invoices. We are talking about one vendor who has three types of templates, so that is about 15,000 documents to process. Even if you do OCR, you want to extract the information and code it to read this and that. So, Jiffy.ai has machine learning where we don't have to teach all the documents, instead we just need to teach it a few. Then, the machine will already know if it finds this type of information, then that is what it is. For example, the easiest way is the invoice number. Most vendors usually have similar wording: invoice number, invoice NO, and INV. However, in all 15,000 documents, you see that the vendors just play around with this wording. It won't differ much. Therefore, the machine learning knows because of this, you don't need to teach it all 15,000 documents. After about 10 documents, the bot can pick it up themselves and learn about it.
There are not a lot of vendors in the market who provide built-in machine learning. In the invoice, you have multiple things that you want to extract: invoice number, PO, and some other line items. With machine learning, we expect it to know what to extract from, by looking at different templates of invoices. It should know that this is similar. Even though you use the different wording across multiple templates, the machine should know that it is an invoice number. We expect the machine learning should be able to do this, and the Jiffy.ai machine learning is able to do it with 80 to 90 percent accuracy. So far, we haven't had a big problem in whatever the machine learning reads, doing it correctly. If it didn't read correctly, we would have to correct it, then the bot will learn from that, "Okay, this is actually the better way," so it can do better next time.
They are still new in the market. Or, at least, they are still a small player. They require a lot of improvement in terms of learning material as well as the community developers. If you compare Jiffy.ai to an established solution, like UiPath, you can go to YouTube and find a lot of learning material posted by UiPath, partners, and other people in the community. However, for Jiffy.ai, you won't find that available in the market. Because of this it is very hard for us to find talent in the market. Most of the developers in the market are used to the bigger players. For Jiffy.ai, if you search a resume because you are trying to find someone who has used Jiffy.ai, you won't be able to find it. So, when we onboard a new person, we want them to learn this new system, but it is a bit hard for them to pick up because there are no external learning materials on the Internet.
For training, they provide the foundation and advanced training. If you have other issues, they have a support portal, which shows a brief summary of the features. It's not very extensive, like Google Cloud Platform. Sometimes there are things that may not be available in the portal. While other products will also not have available the information in their portal, other people know it. So, you don't have in the community discussions about solutions to a problem that would not be available in the portal.
We started this project last year in May.
In terms of the portal’s stability, the system is quite stable. We almost never have downtime, and if so, it is very minimal. However, in terms of bot stability, it depends on the server. The bot sometimes gets stuck, then you have to restart the bot, which is something for them to improve.
I do not see any issues in terms of scalability. We can automate a process for a certain department and that process can be very similar to a process of another department. We might need to just change it a little, so we can use the existing solution that we have created. For example, if we create a reconciliation, then the same engine can be used for any big reconciliation tasks in other departments not related to finance. It could be done for engineering, operations, etc. It is very scalable in terms of reusing the existing solution.
They are still quite a small player. Because of that, they can focus on the customer a lot more. If I am comparing them to a bigger player or other players that we have worked with in the past as well, they are a lot more responsive, passionate, and focused on us. They help the customer.
We can create almost any type of solution in a very inexpensive way. In the past, we bought software to do certain processes. However, with Jiffy.ai, we can build the same software at a fraction of the cost. We no longer had to buy this other vendor's software anymore, which we licensed every year. With Jiffy.ai, we just have to pay the setup costs in the beginning and have them do it for us. We wouldn't have to pay them if we are doing it by ourselves. If you just use their service and do the setup ourselves, then we don't have to pay for the service, we would just need to pay for the service to use the Jiffy.ai platform to build our software. So, in this example, we are actually saving 97 percent of the costs.
The deployment process is quite fast. Because they are small, they could focus on us. With the development, there are not a lot of processes to do it. We just have to set up the server. We can use their cloud, as cloud hosting or hosted in our on-premises. Even if it is hosted on-premises, the setup is quite fast. Training our staff was also quite fast. I didn't have any issues. I was quite happy with the setup.
The initial setup could take about a month or less, but we also had the incremental setup for our sister company. So, we have multiple entities in our company. The first time that we set up, we set up from scratch so there were a lot of other things that we needed to set up, but setting up another tenant for our sister company took a few days.
The reduction of work on a manually basis by project is between 50 to 90 percent. There are some processes where we almost automate the whole thing, and we just need manual handling by a person in certain rare situations. In that case, the reduction could be 90 to 99 percent. However, for certain processes, we can only automate 30 to 50 percent because the rest of the process still needs to be done by a human because of regulatory purposes, etc. So, it's a huge range: 30 percent to 99 percent.
The pricing is quite competitive. As a small player in the market, they are quite aggressive in their pricing. With the features that they offer, it is quite worth the value.
Before we chose Jiffy.ai, we looked into other solutions, especially bigger, more established solution providers, like UiPath, Blue Prism, and Automation Anywhere. In terms of simplicity of usage, Jiffy.ai is easier to use since they are on a webpage. We put a portal on it and everything is available there. The UI is a bit more user-friendly and intuitive.
In terms of trying to do end-to-end process automation and how easy it is to do it, these are big pros and cons when compared to UiPath. In some ways, they are easier, and in some ways, they are not. I like with Jiffy.ai that we can use Python, but with UiPath, we can't use Python and need to use .NET. I'm unsure if they have enabled Python now. We also have a lot more flexibility with Jiffy.ai, e.g., we can connect to Google or any kind of system without having to do integration. We can just go from the front-end and record it. UiPath has this as well. You need to install Orchestrator on your PC. Then, you can install the design anywhere, because it is web-based, which is an advantage.
In other solutions, you have to install and set it up. If I have a new developer come in, then I have to install the system on their laptop before they are able to do their work. With Jiffy.ai, you can do it anywhere, on any laptop, as long as the laptop has access to the webpage. You just need access to the webpage, then you are able to do it. We control it from the portal as well. So, if I want to shut down or restart the bot, then I just have to go to the portal. I don't have to go to somewhere else, log into the server, or remote desktop to several laptops to do it. Everything is centralized on one laptop in one portal: the user access, the bot management, the task management, and the user interface for the human to manually handle certain stuff. Everything is on one page. This is an advantage over other solutions.
You have to be open to trying new things. There are certain things that if you are already used to other bigger players in the market, then there are things that you like and things that you don't like. However, even the things that you don't like, it is mostly because you are already used to the way the service player is doing it. Therefore, if someone is doing it differently, it could be actually better, though it may not feel like it. I think you will find it exceeds your expectations.
Even with using humans, we have multiple redundancies to ensure there are no errors. The end results are not a lot of errors, though using the bot reduces the redundancy in having people check each other's work.
We are still reducing the full-time employees doing the work, but not up to 100 percent. We still need to maintain certain people for handling tasks that can't be handled by the bot, like manual exception and manual handling. Therefore, we cannot 100 percent automate everything. There are certain scenarios that require human judgment, preventing us from using the bot to do them.
I would rate this solution as an eight (out of 10).
One use case is to automate single sign-on for different applications with the Active Directory. When people change roles within the company, we need to alter the permissions for multiple users. Another use case is assigning tickets to technicians. We classify what the customer needs and then automatically transfer the ticket to the person in our company who will handle this case. We also have some financial automation dealing with bank transactions.
We sometimes use Automate to help us with data migration. When we have a new CRN that is a hotspot, we make a little robot for data migration because the company has accumulated a lot of data in 30 years. We only did this once and changed the bot after three months. These are some of our use cases. We have had 40 different automation cases in three years.
We like to deploy on-premises because we have many related programs. When our customers make some tickets, we prefer to have everything in-house rather than on the cloud. We have servers in our office. One of the principal applications is on the server, and we have five bots in different areas, including finance, human resources, and operations.
Since adopting Automate, we have spent much less time on general tasks. In the past, we had one person writing all the emails and assigning them to the people who would handle the case. Now, that is not necessary. Another example is data migration. Migrating a large volume of data sometimes involves repetitive tasks. It's easier and faster for us to develop a little robot to do all that.
It's pretty easy for us to use a bot for single sign-on. When people join the company, we can add them to the bot and set all their permissions based on the Active Directory. Our employees spend less time on these tasks and are more productive.
It also reduces human error. When you have a human dealing with ticketing, they must collect customer information like names, phone numbers, ticket numbers, etc. It's always possible for the employee to mix up customer information or input the wrong numbers, but the bot never makes these mistakes unless they are broken.
Automate has over a hundred connections with email, PDFs, Excel, the web, and various areas. It's like Excel, which has functions, so you don't need to know how to code or be a developer. People need to understand how to use all these connections and functions to customize the solution. Having a little technical knowledge is better because it's faster and easier to use, but you don't need to know how to code.
All the applications that we need are drag and drop. A lot of functions can be implemented quickly with only some tiny adjustments. They change only one value and the name, then drag and drop. It's essential for us.
Our employers have strong technical backgrounds, and we have a standard training course for Automate that takes about 10 hours. Usually, we have four sessions that are about two and a half hours. It's crucial when you have a training session that you have some time in between to apply the knowledge and improve. After the next session, you take a week to test and improve. After about 10 hours, you can understand how to use the system well.
They also have Automate Academy, where you can learn best practices. The problem with RPA is that it's impossible to know everything about data and SAP. The standard training is high quality, and you can improve as you go. If you need something special, you can check the bots the community has produced and see how users have built bots using data from ServiceNow or many other solutions.
We have tried other RPA solutions like UiPath or Automation Anywhere. They are fantastic products but complex, and it's hard to understand everything. It would be best if you had a skilled developer or several. Automate is easy to implement, administer, and use. It's appropriate for us because our cases are simple and easy to automate. We don't need complicated tools with many features; We prefer to get a significant ROI quickly.
We can automate several processes with only one bot, so it's much cheaper than other products where you need to buy additional licenses. We need a good value for our automation. The other solutions might be more robust, but Automate is perfect for us because we're not a large enterprise.
The intelligent automation feature could be improved. It's interesting because it's simple, but the automation quality isn't always good. It's easy to use, but sometimes you need to make a slight improvement to the automation, and that's not so easy.
We have many customized cases, so sometimes, it's not enough. We need to change and customize some processes. We have a little methodology where we sit with the key user and save all the programs because they often have a lot of exceptions. Understanding the risk and whether the processes are good candidates for automation is crucial.
We have been using Fortra Automate for around three years.
Fortra's scalability is excellent because you can increase the number of bots, studios, and developers. If you have a critical process that requires high availability, if one process fails, you need to have another bot pick that up. A big company cannot fail to pay the payroll.
It's flexible because you can choose what you want as needed. If I need more bots, I can add them. Maybe I have a project where our resources aren't stable. I can add a subscription for another developer for six months and pay more to use this bot. I don't need to have a perpetual license for developers.
I rate Fortra customer service eight out of 10. We are based in Barcelona, where they have their European headquarters, so there are 30 people here. They also have 200 people in Argentina who speak Spanish. They respond quickly and provide excellent support. This is crucial because I need a quick solution if something fails.
Positive
We have used UiPath, Automation Anywhere, and Blueprint. Those are good solutions, but they are probably too big for us. These solutions usually have a charge for licenses. When you need more functions, the prices are higher. For example, after automating a process for six months, you might realize you have a new requirement, so you need to pay a lot of money to change the technology.
The other solutions are a little too complicated because you need high-level developers and more users. I'm sure all that is great, but it depends on your needs and objectives.
I have been involved in Automate since the proof of concept, and I've been working on the setup and maintenance of this project for the past three years. Setting up Automate isn't too complicated. However, installing any software application involves some prerequisites and planning. You need to understand the best practices of the project. It was a little more complicated initially than now. After three years, we can set up a bot with our eyes closed.
Deploying attended and unattended bots is similar because we have the figures you can use for how you want the robot to start. It's pretty easy to say that I want an attended bot because I am the user, and it is a mixed project. Sometimes we have a complicated process we need to program, like when we receive an email request and we need to start a bot to make a ticket. It's not complicated.
We started seeing value about six months after we created the automated processes. By that time, we had begun to develop some best practices. When you get an update, you need to install it the first time. You don't lose anything, but it isn't automated. They have two or three updates every year. There is routine maintenance, but you don't need hard services to maintain it. We typically review every new process and need to do some maintenance when there are errors.
Deploying Automate required a few people. We need some IT people for technical requirements because it involves the Active Directory and configuring the servers. We also required functional people who understand the process and an implementation consultant. The team size depends on the organization because some organizations have mixed profiles. You might be able to do it with three, but it's easier for us to have the most qualified person in each area.
We had help from the vendor for the implementation because we prefer a fast deployment according to best practices. If you go at it alone, it might take a long time, and we don't have the time to be careful.
We get a high ROI because we can automate all the processes ourselves without hiring a consultant. Learning to develop automation in-house can be a costly process because you need to buy the license and spend money on training, so the initial cost is higher. In our case, we have technical backgrounds, so our staff can pick it up quickly.
Automation has reduced staff hours and the risk of errors. We are more efficient and productive. People are more motivated because they're doing fewer repetitive tasks.
The pricing is quite reasonable. We can run the process with one bot. The limitation is at the server level because they have some resources, and sometimes that is enough. It isn't too expensive, so you can get a good return off of one, and you can also have a pack of five bots that is quite competitive.
They also have an enterprise license called ultimate with HA ability. It's pretty good because you get a discount as the bots increase. We don't have this license because we are a small company with only 40 processors, but I think it's a reasonable model. If you are working with several price scales, the price of one bot is only $3,000, and it's $6,000 for a perpetual license. If you are one FTE and reducing error, it's a pretty easy decision.
I rate Fortra Automate eight out of 10. To implement Automate, you need to understand that 100 percent automation does not exist. There are always exceptions and multiple factors in your environment that change different aspects. It's essential to have good governance when the report fails. It also helps to have proper documentation for when you need to solve problems.
It would be best if you were realistic about what you can achieve with automation, but you don't need to automate everything to benefit from it. Automating 20 percent of all your processes might reduce your work by 30 to 40 percent. That's a good start; you can study the cases to improve the bot after the fact. Don't try to develop a complicated robot that can deal with 90 percent of your cases. An RPA is good when you see the results after one month.
Within my company, I am responsible for a line of business where we roll out automations for various internal stakeholders, and WorkFusion is the platform of choice within my organization.
Approximately 90% of our use cases are RPA-governed use cases. We have one use case now that is a machine learning (ML) based use case. While we are not adverse to using ML within WorkFusion, we are still in the process of identifying or exploring the right use cases within the organization that are the best fit for ML.
A helpful feature is the analytics capability within WorkFusion. The analytics dashboards, i.e., their Tableau dashboards, are really helpful for building a business activity monitoring layer. These are very visible to senior management and stakeholders, who can see and know what is happening within processes. They can see what benefits are getting delivered as processes, either ;daily, weekly, monthly, or quarterly. It is easy for stakeholders or senior management to understand the value for the money that they are spending.
The solution has ELK Stack with Kibana, which is useful and helpful for monitoring all of WorkFusion's infrastructures. This has been really helpful, even though we have just started using it very recently. Previously, if there was an issue, we would log into each of the servers separately and try to fix it. We were introduced to ELK stack two weeks ago, though it has always existed in the product. It has been really amazing. If there are failures, we immediately get notifications. We have been able to integrate it with our enterprise alerting system that creates tickets automatically, which is amazing. We don't have to spend and dedicate hours just to look into the infrastructure to see if there has been a failure or not. We can be reactive instead.
WorkFusion definitely has a very strong capability to process any document using OCR and machine learning frameworks, particularly from an extraction perspective. We have just used this, deploying one use case using these capabilities. We have been happy with the results. We are getting almost an 80% automation rate. Based on this success, we have started exploring other opportunities within my organization to see if there are other use cases that best fits this AI paradigm.
It is difficult for non-technical users to use WorkFusion. If we really want to build automation, we definitely need Java developers. Their RPA is not for non-technical people. If I look at competing products, like Automation Anywhere, UiPath, Blue Prism, or even Power Automate, a non-technical person can build basic automations and deploy them. Since non-technical people can easily build basic automations, they can be deployed in no time. With WorkFusion, there isn't a similar feature that works well. One needs to write code from scratch to build automation. That is definitely a deterrent if we really want to do quick, dirty automation. I would not rate WorkFusion very high on their no-code, low-code paradigm. In this area, I would definitely expect them to improve.
From an AI engine perspective, they are currently focused on document processing. They should provide the capabilities to integrate ML models. which are just outside document processing. In the world of machine learning, there are several such models available. They should give that capability or provide those capabilities within the platform. That would go a long way.
I have been using WorkFusion for more than six years in various capacities.
Since we are working primarily on RPA, I find their RPA nodes to be not very stable at times. The nodes may crash at times, and it doesn't send any notification that the nodes have crashed. Maybe the configuration at our end is not right, but this is what we see. From that perspective, we need to continuously monitor those nodes. If we had a better way of monitoring all those available nodes, it would be much better. That is the most fragile part of WorkFusion's infrastructure. Every other component within WorkFusion is very stable.
RPA, as a whole, is an amazing product within WorkFusion, when it comes to deploying at scale.
WorkFusion is the platform of choice for scalability. This is one reason that we are using WorkFusion compared to other competitive products, where the ease of building RPA is much better. If I look at the competitive products from a scalability standpoint, WorkFusion is the platform of choice for us. It is amazing.
We have already 50-plus processes deployed in production. We have around 50 to 70 more processes in our pipeline, and the pipeline is growing.
We have a 20-member team at the company working on WorkFusion.
From a customer support perspective, we expect timeliness of response. If we are raising a ticket, we expect that whoever picked up the ticket will respond in no time. At least, the first response should come in no time. After the initial response, if they require certain logs, then they should take them from us and come back to us within a stipulated time frame. It should have the right recommendation and a solution that should solve the problem.
Their response time is sharp. If we raise a ticket, which is a production-critical ticket, their initial response comes in less than 20 minutes. I haven't seen any tickets open for more than 48 hours before they are getting closed, however complex it may be. Their support team is doing a wonderful job. I would rate them as eight out of 10.
Positive
The initial setup wasn't complex. They definitely expected us to provision several infrastructure components, like servers. However, once the servers were available from the organization, all the ports were open, and the DNS names were set up, then the installation of WorkFusion was smooth. It takes anywhere between a day to two days at max.
It took us almost a month to implement their machine learning models for OCR and Intelligent Document Processing. This wasn't WorkFusion's problem. It was more on the data collection side. Their model requires us to bring in a lot of documents to train the model. It took time from our side to provision those documents with the needed quality. The model doesn't work if you give it 10 documents. The model won't suddenly launch. It doesn't work that way. Once we had given it the right set of data, then the initial out-of-the-box model was done in a month's time.
When we were building our first ML model, we obviously weren't able to achieve the success rate that we were aiming to achieve. We engaged WorkFusion who spent almost a month with us unconditionally, helping us with our complex document structures. The results have been amazing.
From an engagement standpoint, we are happy with WorkFusion. Their customer success team, delivery team, and sales team are amazing to work with.
We have not yet seen ROI. We have been using this product for two years. We realized that we need to deliver more automation on this platform to get the benefit. That is a function of the use cases that we are picking up. Our success criteria for automation is a reduction in FTEs, which is because we are measuring a break-even from a FTE reduction rate. This is also a function of how we are internally finding out use cases within our organization.
The pricing is high. They should have a better pricing structure. It was not easy for me to try out WorkFusion initially. For example, if I am working on SAP, the licensing may be very expensive. Though, it is fine to use it at scale and pay for the license. We have an enterprise license now, so we are not worried anymore. We are fine with it.
As a first-time customer who wants to engage with WorkFusion, not working for my company, then I would want to do a PoC. The cost is prohibitive because they will ask me to get infrastructure with six or seven servers. Then, you must buy a WorkFusion license to do a PoC, which is expensive. I would then have to set up a team to build the PoC and see the benefits. That is very prohibitive.
We explored other solutions, like UiPath and Automation Anywhere. We even tried Power Automate. We evaluated them almost two and a half years ago, then we settled with WorkFusion as our platform of choice due to its scaling capabilities.
When it comes to building automation using RPA, it is not very intuitive or user-friendly. We definitely require developers to build automations. Compared to using alternative products to build automations, such as Automation Anywhere or Power Automate, WorkFusion is not easier for building quick and dirty automations. However, if you want to build automation that can be deployed at scale for several transactions within a day, WorkFusion's RPA is amazing. This is one product feature that we found rather interesting.
If you are looking for building small-size, quick and dirty automation and something that you can build from scratch within two to three days to run on each of your user's desktop, then WorkFusion is not the platform of choice. However, if you are looking for building automation which can run at scale, e.g., if you are doing suspect transaction processing in a bank and processing approximately 50,000 transactions in a day, then you want to really ramp that up at scale. In that case, WorkFusion is the platform of choice.
There is room for improvement. I would rate them as six out of 10.
We started out with accounts payable, and now, we're starting to do more vertical revenue cycle management in healthcare. We're starting to use it for other things.
We're on version 11, but we're moving to 2019 or 360. We're in the process of migrating right now.
We were able to replace a lot of the manual and data entry workload that our accounts payable clerks were doing. We had 18 accounts payable clerks, most of them are still there with us, and the work that was done before by this original group can now be done by four people.
The ease of use in getting started is probably the big thing. They had a lot of references that we were able to check. It is a well-known player, and being able to get up and running quickly was the big thing.
Migration is probably our biggest challenge. When we were trying to do the migration to the new version, it was a little bit painful. We didn't have that many bots, and I can't imagine the level of effort that some of the bigger customers have to put in for that. The product is stable, but moving from one release to the other was the issue.
We also had difficulty with some of the documents that we were trying to automate. IQ Bot or intelligent document processing doesn't have all the features and functions that we were originally told it had. They need to improve that. It had some limitations, and we've had to add other products to the mix just because they weren't able to deliver some of the things that we were told that they could deliver upfront.
They've been talking about task mining for a while. They don't have a good tool out there to evaluate the current processes and come up with a plan. It is a trial and error process where you have to sit down and go through what different people are doing, for how much time they're doing it, etc. There isn't a good systematic way of capturing that information. There are other products such as Kryon, and there is a new product called Soroco that we're looking at, that would let us evaluate the task that could be automated. So, it is not really process automation; it is task automation. You don't really see end to end when you're doing these projects. You don't just decide to automate the whole process. You select the tasks that you are going to automate, but you don't really know the effect that particular automation is going to have on another task. You don't have the big picture. It would be very helpful if both UiPath and Automation Anywhere provide a center of excellence that you could run on your systems so that the people who are running things come back and say that:
All these are manual today. You're looking at what other companies are doing and hoping that you're going to get an ROI, but you don't really know until you start the project, and by then, it is too late. You've already spent the money on the software, the bot, the integration, the services, etc. At the end of the day, you're thinking if you would get the ROI. You have to measure it to see if you're going to get it after you've already spent the money. It would be really nice to have something that you could run upfront to know about the automation tasks that are costing you money today and where you should focus your efforts on automating.
I have been using this solution for about two years.
It is pretty stable, but moving to the new release has been a major pain.
Its scalability is great. It is for sure built for enterprises.
We have six to eight people who are doing validation now and are actually using the product for validation. They are accounts payable clerks and healthcare revenue cycle management folks.
It is being used extensively in the finance back office, and the idea is to bring it more to the front office for customer onboarding and things of that nature. We haven't done a lot of that, but that's in the plans. We are trying to figure out the next processes to automate.
It is good.
We didn't use any solution previously. We did add UiPath to another area of the business, so we did bring on another vendor just to see what the differences were and how easy it was to use compared to the other one. We haven't really said we were going down one path as a company. We didn't want to put all eggs in one basket, so we decided to do one project with Automation Anywhere and one project with UiPath and compare the two.
UiPath has a little bit better document management. They have some process mining that we've started to use. Pricing is roughly about the same. There is not too much of a difference. I haven't seen too much significant difference between the two products.
It was pretty straightforward. We originally thought we could do it with internal resources, but we had to bring a third party in to do it. So, it is straightforward if you have experience and training. Our tech team can do it now.
We started out with a simple invoicing, and the bulk of it took about three weeks or so. Testing everything probably took closer to six weeks. We finally got it going in two months.
When we first started, we thought we could have more of our team doing it, but we had to bring a third party in to do the bot building and all that. It was a reseller, and they had technical people. Our experience with them was good. They were a little expensive, but it was good.
For its deployment and maintenance, we have about three people. We have created a small center of excellence team with three, or sometimes four, people. We have a business analyst. We have two developers, and we have somebody from finance on that team who is putting a lot of hours into that piece. We're truly trying to figure out the next processes to automate.
For accounts payable, we saw a return in about five months from the time we started. There was a significant reduction in the number of hours that invoicing was taking. We went from 18 people to 4 people. We didn't let all those people go, but we did let a few people go, or we moved them into different job functions.
We're paying a monthly subscription fee for the bot and for services. There are no additional costs beyond the standard licensing fee.
I would advise looking at other companies to know what they're doing. I would also advise putting a complete plan in place so that all bot management needs are considered upfront. It is helpful to put a roadmap in place and have a center of excellence or something like that to make sure that the processes that you're going to implement have an ROI. You can validate the effort upfront by using process mining or other tools and evaluate what the effort is now and what the savings are going to be upfront.
We did a couple of projects that we thought were going to have significant ROI, but they didn't. It was more of an internal process that we had to fix. It wasn't so much about automation; it was about the way we were doing business. We weren't following processes, procedures, and things like that, and that was what was causing the issue. We had automated a bad process, so it didn't have the return that we thought it was going to have. We had to do some procedures. We had to change some things internally.
I would rate it a seven out of 10. It's a good product. It does what it's supposed to do.
Mainly, we concentrate on RPA bots. However, we have a few demands where we needed RDA, their assisted bots. We need some assistance with the bot where some inputs are required from the business. 70% of the time, we concentrate on RPA bots. For the other 30%, there is also a requirement for RDA bots.
Mainly, we interact with Windows web and sometimes with mainframes. Currently, we are targeting APIs as well. We are working on a few of the API-related stuff and Excel-related work, Outlook, sending email, and tracking email.
In terms of third-party applications, we have multiple websites, which are web-based. Based on the client's requirement, we use it to interact with the applications.
As an RPA guy, every tool has its own advantage. That said, Pega has the best feature in terms of creating UIs. We can create the best UIs. Other tools, don't have a better UI for RDA.
Some go for RDA bots in the banking sector, and they prefer Pega Robotics.
We have centralized the monitoring. In Pega Robot Manager, we currently have multiple dashboards where we can track, schedule, and handle everything. That is the game changer in the last one-and-a-half years. It amended what the business expected.
The initial setup is pretty easy and quite fast.
It's stable.
You can scale the solution if you need to.
Excel connectors need to be improved as there are a few connectors in Excel areas.
The recording is not at best in Pega Robotics. They could look into it.
The interactions with some applications, like Windows and web applications, are a little slow. They need to improve their speed. It should be normal, to open it with other tools. However, right now, it's a little hard.
Users should feel they can develop easily. It will sometimes hang while interacting with third-party applications.
Instead of adapters, if we had direct interaction of the applications with API and had scripts, that would be better.
We have only CSharp supported right now. We'd like to have Python supported. If it was, it would be easier for us and make the solution more flexible.
Currently, we are adding three different configuration files, however, if I had the option, I'd prefer to make a single configuration file and then differentiate it in all three environments.
It can be difficult to create logic in automation. It's hard than if we used other RPA tools.
If they could add more toolbox options, that would be ideal.
They need to do what they can to help us reduce complexity in the automation and combine things under, for example, a single control.
I've been using the solution for five and a half years.
Recently, when I compared all tools, looking at the preloaded applications and Windows-related applications, if you process end-to-end, and if you are releasing it to production without any bugs, it is running with 95% perfection with maybe some hiccups via network issues. Basically, it is stable.
Therefore, Windows is quite stable. However, Citrix gets 80%. It's still good, however, when choosing the tool, it's important to consider if Pega is a suitable tool to give the best results.
100% it is scalable, however, if a person is not aware of everything the solution can do, they may see it is only 70% scalable, or a little less scalable in general. The more you understand about the tool, the more scalable it becomes. It becomes easier to scale too. It's just that Pega may be perceived as a bit more difficult, than Blue Prism, UiPath, or Automation Anywhere. It's not, though. You just need time to adapt.
I'm working for a sizable company that has hundreds of clients. In my client area, almost 150 to 200 bots are running. The other areas where my colleagues are working also have bots running.
We are new to the tool and RPA, in general, was so new at the time for every organization around 2015 or 2016. At the initial stage, we used to contact support frequently due to functionalities issues. We need to raise the ticket if we want to talk with them. Then they will connect with us and help with resolving the issues or the bugs, and patching also.
They are helpful. If we communicate clearly what we need the process moves fast.
Positive
I use Pega Robotics and Blue Prism in my day-to-day life.
In the market, in India, other solutions are used as well. Some may use Blue Prism or UiPath. For example, if you have a 100% RPA area, maybe UiPath concentrates on 40%, Blue Prism 30%, Automation Anywhere is another 30%, and Pega is around 3%.
It has a default platform from where Pega provides a PRPC. Inside that, we need to deploy Pega Robot Manager. From there, we can deploy to Pega Robot Manager. From there, we can assign it to multiple users for daily use.
It was easier to deploy before. In some newer versions of Windows, we used to face some of the update tree issues. Currently, we need to download the update tree separately, and we need to install it on our machines. Then, we need to install Pega. Normally, it is easier, however, with these newer versions in Windows - 10 and 11 - we are facing some issues.
There are Office versions that are not working perfectly. We need to uninstall and reinstall it, and for some Windows 11 versions, the toolbox icons are not visible. We have tried multiple times, uninstalling and installing.
Deployment times vary. Normally, it will take hardly less than half an hour. However, if the solution is ready and we have groups assigned, we can directly deploy it to our servers. From servers, we can directly deploy to the users as well from Robot Manager. In the user's machines are the VDAs that we are using. We need to configure all the settings, the run-time, the config files, and everything. That takes ten minutes to one hour at a maximum. Once the setup is done in every machine, we can easily deploy that that will be hardly five minutes.
The costs vary. In a previous organization, we used to take care of everything. It's a yearly fee they used to take. Recently, what I'm seeing is they have to categorize licenses. Some of the bots, they will run for some time, so they can get licensing costs and can pay quarterly. Typically, it is yearly.
I'm not sure of the exact costs. I haven't gone that deep into licensing.
The company assessed three more tools and chose Blue Prism and Pega Robotics. They use Pega Robotics for doing RDA, and Blue Prism for scripting-related items as they are strong in their area. Out of the five tools they have evaluated, they have taken Blue Prism and Pega Robotics.
We're a customer and end-user.
If a person wants to use this product, they need to know what is happening and how it runs. If the person is new and needs to run the solution, they need to follow the use case that the developers are giving to understand how the bot will run. With so many people, what they do is when the bot is running, they disturb some of the applications, and the bot will close or give exceptions.
If a person intends to use coding, they really do need a good grasp of what it is they want to build. Only them should they move into actual development. It will make it easier in the end if there's a good understanding at the outset.
In any case, after two or three months of using the solution, chances are you'll have enough knowledge to be able to help train others.
I'd rate the solution eight out of ten. If it was faster and had less complexity I would rate it higher.
