Agentic Process Automation System (Now Certified for WorkSpaces)

Currently, our use case for the solution is majorly in IDP  solutions, including invoice processing, document processing, and exploring agentic solutions. Major issues that we are having involve multiple types of POs that come along and how to process them, how to get good structured data from them, and how to remove the hallucinations of AI models. These are the major scenarios, and after that, the processing in SAP and other platforms with that particular data follows, which is our automation and IDP  use case.

Document Automation plays a very good role for document processing. We were initially using IQ Bot, which is a product of Automation Anywhere , and we have now migrated to Document Automation. It helps us because in the previous version, we used to handle a lot of groups, and handling them was very difficult when we had multiple customers and a lot of templates of POs or invoices. Now we have one model, which is a smart solution that learns from what we give it and from the prompts and rules we use. It gives us better results, and we do not have to maintain it. It is also easy in live production because previously we needed to train in dev, then go to UAT and production. Now we can give validation two or three times in production and do not need to code it again and again.

We are using Document Automation on a large scale. For processing a manual PO and extracting its tags, it would take at least six to seven minutes for a person to do it manually. With Automation Anywhere , it is a very quick task because it extracts all the data in one go. If we send 30 POs, it will extract all of them at once, and then it will proceed with processing. One person can do all the work, and it saves a lot of time because it completes the task in a minute or two.

Automation Anywhere comprises multiple features, and it is a place where we can integrate a lot of solutions, which involve things such as automation solutions in Workday  or Salesforce , agentic automation, human in the loop, and other varieties of things. The feature I love most is how we can interact with a human in the loop within IDP solutions so that a particular person can validate that the extraction is correct and that the data going inside SAP or any finance system is accurate, and how they can interact with it. The processing reduces their work as well, which is also a good point.

The API has helped me achieve my automation goals by allowing us to do a lot of POCs so that we can interact with the whole process structure with human-in-the-loops because in our industry, data which is going in and coming out should be validated. This is helping by reducing a 10-person workload to one person who can validate and approve the work.

It is creating a lot of value for the organization because there is a very big team which used to do a lot of processes. For example, the research and development team used to find chips that have a lot of applications and determine what those applications are so they can perform R&D or innovation on chipset manufacturing. We created LLM-created solutions, and it is extracting what they used to do R&D with a lot of data manually, which took a lot of time. This automation has reduced the time, and whatever they used to do in a week is now completed within a day. This is an example of how we are reducing complex times with the automation.

Sometimes the challenges for me and my organization involve the infrastructure because agentic AI also needs infrastructure to be set up, and the current infrastructure setup is not very great to adopt it. Sometimes we have issues which take time to understand, and that is one of the challenges. Another challenge is that we are using it for IDP solutions, and AI and LLMs are still hallucinating and giving different results for the same thing. These are the things that we are still struggling with. Automation Anywhere support provides assistance whenever there is an issue. When we raise it with the team, they are very responsive and trying to resolve it by coming on a call, and they resolve it as soon as possible.

We are currently using the Autopilot capability of AI Automator AI , but not in a very vast way or in a major way because our automation is more of a scheduled version, so we are not using it much right now.

In the initial session, there were a lot of things that are in preview, such as A-Sera and A-Code, and there are some process discovery tools as well which are coming, which are actually very great. They are in preview, so I am waiting for them to come. From my previous experience, sometimes when a new product came, it took time to get matured. I would be expecting that when it launches, it comes as matured.

I have been using Automation Anywhere for the last seven years. Parallelly, I have done solutions with Power Automate , but as compared to this, I feel that I am more comfortable with Automation Anywhere.

We used to have a hypercare period when doing a deployment, and I see a gradual decline in the errors. Right now, if I talk about four years back, we used to monitor deployments for a month. Now we monitor for a week because we have already tested a lot. Generally, issues are not coming from Automation Anywhere; more of the issues are coming from the infrastructure, which is fine and usually happens.

Automation Anywhere provides support whenever there is an issue. When we raise it with the team, they are very responsive and try to resolve it by coming on a call, and they resolve it as soon as possible.

For small automations, I would prefer Power Automate  and similar solutions. For a complex one, I would go with Automation Anywhere.

It depends on the process. We are in the R&D space, and as I mentioned, the one-week work is done within a day, so it saved five days. For order processing, I would say that it is not very much time-saving because it is also taking time to extract and process, but it is giving us freedom in that the people who are doing the repetitive task are not doing that repetitive task now, and they are doing something else, maybe validation or something more productive.

For small automations, I would prefer Power Automate and similar solutions. For a complex one, I would go with Automation Anywhere.

The importance of AI governance in my organization is always crucial because of auditing, and we do not want any data to come out from there. Automation Anywhere gave us that particular security, and they are handling the data within their enterprise and not going outside. That is the first point which is why the organization is using Automation Anywhere.

We are using AI agentics and AI Agent Studio  for some of the processes. I would say that it is majorly in a POC stage, but as I mentioned, we are using it for some of our systems and the API with the agent connection as well.

The visibility is more because in any complex solutions, we used to take the help of it. Visibility for us is significant and it does create an impact because whenever we are stuck for some time and then use it, it is clearly helping us a lot for the creation of solutions.

The API has helped me achieve my automation goals by allowing us to do a lot of POCs so that we can interact with the whole process structure with human-in-the-loops because in our industry, data which is going in and coming out should be validated. This is helping by reducing a 10-person workload to one person who can validate and approve the work.

I do utilize Automation Anywhere Center of Excellence, CoE somewhat for solutions creation and if there is any complex way which we are not able to identify how we can resolve these things. We use that for support.

I give this review a rating of 8 out of 10.

Our main use cases involve automating finance-related bots, specifically finance invoices, intercompany receivables, and manual entries. All those types of bots we are automating.

The main automation we have built is for the finance team, which was receiving around 500 to 600 requests for manual general entries. For those entries, there was a particular request they used to get from the user, and then they had to validate that request before posting it in SAP and attaching the document in SAP, followed by communicating details of that request with the stakeholders. This was a time-consuming and repetitive task, so we built an end-to-end automation bot that monitors Outlook emails, monitors the shared mailbox, takes and validates the request, the user, and the attachment. Once that is done, it posts the document in SAP and sends a successful mail to the user. If there is any failure, the bot also sends the particular error message to the user to eliminate any communication gap between the user and the bot. This basically eliminates all the manual efforts of the users and increases the efficiency of the task process.

We are about to use Agentic Process Automation ; we are currently using it for a particular region and expanding that bot to other regions as well, alongside using it for Agentic Automation .

My experience with Automation Anywhere  is quite good because we can automate the whole end-to-end process with no human intervention. The main focus is that we are able to focus on higher-value activities rather than working on the same repetitive tasks, which is quite important and gives the highest impact to the business as well.

The main automation we have built for the finance team eliminates all the manual efforts of the users and increases the efficiency of the task process. We are able to focus on higher-value activities.

Currently, while working on the SAP part, I encounter some challenges, particularly not being able to extract the tree structure. There are some tree structures we are unable to extract, so we are finding difficulties with those objects in SAP. I think the SAP part can be improved.

I have been using Automation Anywhere  for around 3.8 years to build end-to-end automation RPA  bots.

Sometimes Automation Anywhere recorders might fail, and there could be package issues. I have faced those issues, but not major ones—just minor ones that I was able to resolve myself. I received support from Automation Anywhere too, and we used to raise tickets, which helped a lot, with hours spent solving the issues over a call.

In terms of Document Automation, I am not using it in the current organization, but I have used it previously for the invoicing process. It used to capture documents in PDF format, extracting data from tables, including quantities of the invoices and the final amounts, and sent the mail to the business user. For Document Automation, we used to get around 15 to 20 requests monthly for documents, saving about 10 minutes per document, which amounts to a 40% efficiency saving.

I would rate this solution with a score of 8 out of 10.

We have been working on multiple use cases with Automation Anywhere , but majorly it revolves around order management, where we do a lot of sales order processing and invoice processing because it gives us a lot of scalability across the globe for the industry we are operating in, making that our biggest use case.

I consider multiple features of Automation Anywhere  to be valuable, particularly the RPA  automation feature that we use to automate all the deterministic processes we've discussed, which I think is the most valuable one, and as we move into more AI and agentic space, there are some new features being introduced that I believe will be even more valuable.

The productivity benefit is the foremost advantage we gain from Automation Anywhere, as automating mundane tasks allows the business to focus more on human judgment-related processes and activities, which I think constitutes the biggest benefit we provide to the organization, along with several soft benefits such as compliance and regulatory support that come from these automations across different business functions.

For any tool, we have to consider that the evolving AI space is rapidly changing, and I believe Automation Anywhere can enhance its offerings to keep pace with developments in the AI sector, which presents a lot of room for improvements, but overall it is working well for us.

I'm Jitesh Mittal, working in the healthcare industry as a delivery lead for the automation and enterprise AI team, and we have been using Automation Anywhere for the last six to seven years.

Automation Anywhere has proven reliable for us over the last six to seven years, particularly for the deterministic automation we've implemented, although reliability also hinges on the dependability of the underlying applications and data; if those are not reliable, no tool can significantly help.

While automating deterministic processes at Automation Anywhere, we often encounter fragmentation across multiple applications, but the integration with these enterprise applications has been seamless, and as we venture into agentic automation, we also consider security measures diligently, ensuring that every defined persona has appropriate access to the necessary enterprise applications and adheres to data security standards.

Customer service and support from Automation Anywhere have been quite good for us as a Gold partner; we benefit from weekly meetings where we discuss issues and new features emerging in the market, which enhances our overall experience with the platform. On a scale of 1 to 10, I would rate the customer service at a nine.

We have definitely utilized the deterministic feature of Automation Anywhere, while the agentic feature is something we are still exploring, even though we have other multiple tools in our ecosystem, so we are indeed exploring this aspect of Automation Anywhere.

The deterministic automations have provided us with substantial productivity benefits across the organization, and as we aim to combine them with agentic features, we believe that we will be able to pursue end-to-end processes with human involvement, which will definitely enhance our advantages.

The use cases we're working on and the deterministic automations implemented so far have given us a great productivity boost in various functional areas across the globe, which has significantly helped the organization, and as we evolve towards more agentic and AI features from Automation Anywhere, these will further assist us in achieving end-to-end process automation rather than just peripheral automations.

When we began utilizing Automation Anywhere RPA  about 10 years ago, the controls to prevent infinite loops and retries were not as mature, but over time we've seen these practices mature, with the tool itself now guiding us on best practices, and we are leveraging exception-handling mechanisms to prevent vulnerabilities that could lead to infinite loops or other security issues during production grade.

The features showcased at the Imagine event are promising, as we are delving into the AI and agentic AI realms, which are already crucial in today's environment; if these can be made generally available for customers to use in their processes at a faster pace, I believe that would greatly enhance Automation Anywhere's offering. I would rate this review overall at eight out of ten.

We focus on multiple use cases, but primarily in the banking and finance vertical. We leverage Automation Anywhere 's platform extensively. For one of our leading customers, we have transformed most use cases with respect to collection, reconciliation, validating possible fraudulent activities, and proper audit of cases. Many use cases which are very time consuming have been solved with the help of automation.

Contract management presents two challenges. One is understanding contracts, which are unstructured in nature. With AI, we can now read and analyze those contracts. The second challenge is processing invoices based on the contract terms, which is deterministic and rule-based in nature. Once I understand the contract, I can apply the appropriate terms and conditions and process the numbers accordingly. The blend of cognitive capability and deterministic capability together solves this problem.

Process reasoning, which we call process mining, serves the underlying purpose of monitoring what is happening and identifying improvement opportunities. Audit logs recorded by the system are analyzed to provide insights on how we can improve further, where deviations are happening, and what the improvement areas are. This complementary capability is powerful for larger enterprises to evolve to the next level, not only for what we have delivered now but for how we can graduate to the next level.

One thing I appreciate about Automation Anywhere  is that we do not need to run around with multiple platforms. Automation Anywhere provides an end-to-end solution.

The platform has many features I value, but what has helped me most is not having to run around for multiple platforms. Automation Anywhere integrates very well with the underlying ecosystem. If I want to leverage document processing, the platform has very strong capability in that. If I want to leverage AI, I can easily embed AI from other parties within the same platform. If I want to take their services out of their platform into some other platform, they provide all that exposure through APIs. Automation Anywhere is very flexible and at the same time very cohesive, allowing us to solve all problems within the same platform without needing to run around.

As an organization, it is very easy to govern the process. If we had multiple platforms, we would need parallel governance and then one common governance on top of that. Automation Anywhere uses a non-monolithic design where multiple agents have different purposes, but at the same time there is a common orchestration layer. We can track the complete journey in an auditable form within the same platform, which helps us know what happened in each particular case. This provides a very seamless experience for anyone embracing the platform.

Automation Anywhere is a fantastic platform, but I rate it nine because I see one scope of improvement. They can leverage AI to accelerate their SDLC journey. AI for process recording, having that process recording fed directly into the engine, from engine to having the bots ready, and then governing those bots while preempting failures and having auto healing is where they need to immediately lead because that is where the competitors are converging towards.

We have our own IX suite as Concentrix , but that is more on the conversational side because we are very much focused on the CX stack as a business. We have invested in voice bots, chat bots, and other conversational capabilities which are part of our customer service. Where it comes to the back office where we need to get the job done, in the definition of agents—more execution agents—we are highly relying on platforms like Automation Anywhere.

Building our own product is always an aspiration every company has. We as a company invested a lot on the conversational side, and that is what we have in our IX suite. Automation Anywhere and platforms like it are complementary to each other, where our suite is more focused on front office conversational side with voice bots and chat bots. Behind the scene, when those dispositions land into execution with the back-office system, we rely on Automation Anywhere to close the gap. It is complementary.

Initially, there were some hiccups because of poor Java or JRE issues. Now that the platform is a cloud platform, they have migrated themselves into the cloud. They are taking all proactive measures in having patches updated and binaries updated with the latest versions and whatever threats they are seeing. I am not experiencing much downtime. In the last one year, I have not experienced any downtime driven by the platform. Downtime has happened because of other reasons not in the platform's control. For example, the underlying application is down, or there are network level disruptions happening which are not in their hands. From platform originated disruption, there is not much.

Automation Anywhere has an edge over other platforms in this area. They are really very proactive and believe in relationship. When we raise a ticket and share a concern, we hear back from them in a very short time. Given the way their business is going, they have created their own support model where we can have priority support. We need to pay a little premium for that, but by paying that premium, we can get priority support. For critical customers, we go for that option. When we have something we can solve ourselves, because this competency is like ten years old within our organization, we only disturb them when we see that we need to take platform advice. Otherwise, our architects and developers are solving most issues. I rate this aspect ten out of ten with no complaints.

The answer is yes. Given that we do not always have a choice to decide the technology, we step back and say we are not the pharma person, we are the doctor. We are not selling medicine, we are selling solutions. Sometimes we need to prescribe medicine by seeing the problem statement or something where a client has already invested. There are parallel products of Automation Anywhere. I do not want to name them now, but we all know. Because we do not have a choice and the client is already having a parallel relationship with them, we continue solving their problem using those platforms.

If we think about peer products like UIPath, Blue Prism , Microsoft Power Platform, and Workato , they are peer products where we have parallel competency the way we have in Automation Anywhere. At the same time, most of the hyperscalers are also coming with their own automation platform, like Microsoft Foundry  which is in the space of APA, and Google and AWS  are all bringing their own platforms. We are leveraging their platforms as well.

I was trying to recollect when the most challenging experience I had was when deploying Automation Anywhere. One thing which is part of the design is that we can easily have a different environment with a clear-cut checklist defined for the development team. Some of these experiences can be avoided. We do have a staging environment where we do all kind of checks and balances before we move things into production. The consistency of the environment is very important because sometimes the version we have in development versus in production are not the same for the base platform, because of which our bot is not able to recognize some of the screen elements and that is where it fails. That is where the retake happens. I think if we had some checklist handy, these things could be avoided. I do not think these are platform limitations. This is more with respect to the way we use that platform. If we are using this tool perfectly, we will hardly experience this.

Typically, in the past when we talked about ROI, we were more focused on KPIs and SLA of the contact center side, where we said we were able to reduce efficiency or drive accuracy. What I am seeing now in terms of ROI is that we are trying to gauge it at a larger perspective. The equation is more at the enterprise level where our metrics are getting aligned with business metrics of the customer. That is where the ROI is very disruptive now. With the power of AI, the demand is very high. Customers are asking for more and more impact on their bottom line. By saying that, we have to be very creative in our ideas and how to give them back.

By design, from the beginning, Automation Anywhere was designed for enterprise solution. From RPA  to APA, if we see the graduation, what we see as a consumer is that earlier we were having a bot, now we have a bot with a brain. The orchestration and the journey is now amplified with other use cases which were not part of the scope before, which were very decision-making, reasoning, and some contextual background that we need to answer those things which we are parking for human. Now, the agentic part has that reasoning also ready. For high-risk profile, when someone as a human is doing that validation, they do not need to use brain for reasoning. They just need to proofread the reasoning and see that there is a logic for that and approve it. That is where we see from RPA  to APA, and with the same platform, we do not need to disturb the platform. The only thing is the brain is also added in that platform. That is how more activities which are part of the human job are now part of the bot job or the agent job.

Three years back when everyone started talking about APA, the first thing we did is we stopped calling our internal advisors agents, because until that time, customer service agents were what we addressed them as. We said "You are the game changers and now technology is the agent." With that change in culture, we started embracing APA very extensively by saying that the rule of the game is the same whether we outsource this work to human or to agent. The discipline is the same, the governance is the same, the guardrails are the same, and security norms are the same because someone is trusting us with giving data. Data misuse is very common. The kind of role-based access control that we plan for human agent, the security guardrails for all the layers of the infrastructure, from the access, where things can go wrong because it is an agent with a very powerful LLM running behind, how to control and avoid any kind of side attacks that can happen on these things. We need to prevent and ensure that our customer data is safe irrespective of the situation. Some of those disciplines continued, and that is how we are graduating into APA. Rather than just calling agent, we have started calling them digital worker and the new hybrid setup of a human bot agent delivering customer problems together.

We are getting into this hybrid setup of human, bot, and agent, all part of the ecosystem in addressing one journey of the customer. In any use case journey, we will have some activities done by the bot, which is very deterministic in nature. We do not want to pay for AI in that situation. Some of the cognitive capabilities will have agent and agentic capability of AI, and then at the end of the day, we need someone to approve it, which is where human comes. The complete journey has to be tracked so that if any customer query is coming about why we gave them a certain amount for a claim, just thinking of claim processing as one example where sometimes customers bounce back and say that this was not the right claim settlement happened. In that case, we really need the traceability. We need to know how we processed it. Traceability in the journey cannot go and say that this is done by the agent and this is done by the human. The complete journey should be auditable. That is where the central governance which was mentioned is very important. Everything should be part of the central governance or the orchestration layer so that whether it is a human or agent, everything should be stitched with the same thread.

Something which is always there is the pricing part, but I do not want to press on that because there is no end of it. On the platform part, certainly one thing which we really need to see is how we can accelerate developer journey. Today, if a team is taking two weeks' time to build one agent, with other parallel platforms like Claude and Co-pilot who are trying to infringe into AI SDLC, the life cycle of agent development, where they are trying to step in into all the stages of defined build and run, how we can have that as part of the platform. I learned in conference that they already have something called AA Code, not in production and not out for the consumer, but they are already thinking on that. That is something which I was missing when I entered in this conference. We certainly need to leverage what Claude is doing or what other hyperscalers are doing in the same space.

Customers are coming to us with very disruptive pricing models. From the service industry, we cannot just go by the labor arbitrage or license-based model today. We need to be more value-driven model. Today I am selling a service with X dollar per transaction and I can give it with fifty percent cost. If I can get a similar model from Automation Anywhere, then it will be a seamless calculation for me. That is where I will say true partnership will do where we are bringing services, Automation Anywhere is bringing technology, and we both are following the same equation, and we both will graduate with the same level of commitment and scalability. That is where a part of pricing will help.

Positively, there is one unique selling point what Automation Anywhere and peer RPA platforms have is UI-based automation, which was their starting point. Because most of the legacy systems do not have API level connectivity today, and that is where the hyperscalers are not able to bridge. Automation Anywhere has both things. They have API which is intrusive in nature where we can have that direct connectivity with the base platform or the system, and non-intrusive where we can leverage their UI automation capability to ensure that there is no bottleneck. We can have the complete journey done. That is where Automation Anywhere has upper hand. Hyperscalers have upper hand because they have their own proprietary model where they can drive more disruption. They have base platform access—if I talk about Microsoft, it is very for them to have seamless integration with their Office platform, SharePoint , shared drive, Active Directories, and other things and give an end-to-end solution with Co-pilot and OpenAI and other things. Hyperscalers have their own advantage. Being doctor, we go by the problem and see how to position and solve it. Sometimes we are making combination of both and solve the problem. My overall rating for this review is ten out of ten.

My main use cases for Automation Anywhere  include document processing, payments, and customer pharma-related automations.

Some additional examples of my use cases are vendor invoice processing and intelligent document processing and payments for the normal people who are in the pharma domain in the United States and other regions.

Document Automation is used in our current process.

AI governance for maintaining compliance and data integrity is very much required to maintain the integrity and data integrity of our organization. We don't want to have any loose ends in the organization. For the effective utilization of an AI agent and to use it in the way it's supposed to be used, governance is very much required.

The impact on efficiency from Document Automation is significant. We are trying to get some data from the PDFs, and that is where we use this automation and document automation. We are trying to get data from basically unstructured PDFs. This has given us approximately 70 to 80 percent of savings. With respect to efficiency, there is almost close to 90 percent of efficiency, and with respect to the cost saved per person basis, it represents approximately 70 to 80 percent of an FTE.

To improve Automation Anywhere , I feel that the recent launches are not marketed to the granular level yet. There are some restrictions where I need to connect with the Automation Anywhere team to get some things resolved or gain access. Detailed documentation and an easy approach or easy access to all the latest releases would help.

I have been using Automation Anywhere for seven years.

In the age of Agentic AI, I think the biggest challenges for my organization are scalability and the cost metrics for the agents.

Before adopting Automation Anywhere, I did try other tools very similar to this, such as UIPath and even Blue Prism  to some extent. I found Blue Prism  to be more costly than this at that point in time, and I don't think it's used anywhere now. However, I feel Automation Anywhere is at par with respect to UIPath and has even crossed it in some areas right now with the leverage it is providing concerning the flexibility of the functions.

Regarding the pricing, I think it's high, and it's the same for the setup cost and licensing. Automation Anywhere has moved to the SaaS environment, and with the latest release I saw today, we don't need the dependency on a bot agent or a virtual machine to run a couple of applications. This approach is what we were actually waiting for because previously, the Automation Anywhere license was tied to one virtual machine per license. The licensing cost and the virtual machine costs are high for the company when the resource pool is larger. Now we are going to the cloud instance, which resolves that problem.

The main challenge and objective I am looking to solve with Agentic Process Automation  involves a couple of applications which are on the legacy side, which could not be feasible for the current set of Automation Anywhere features, but they are a very good fit for the AI agents. I am trying to explore that.

Agentic Process Automation  has helped me achieve the automation goals that I needed. They achieved it by getting the APIs, which means the overall turnaround time for an automation reduces drastically. I can directly build an automation in a couple of days if I have the API. If I don't have the API, then I need to follow the approval workflow. That takes some time, and then I need to get access to applications, follow the workaround, and see the available options in Automation Anywhere. That is a workaround which everyone should try to avoid.

I have started to use the AI Agent Studio  to build these automation processes.

I am currently exploring this further. We have very limited access to Automation Anywhere in our organization right now. The licensing cost is very high, so we are trying to use it wherever it's necessary only and not on a learning perspective yet.

Governance  is very important for my organization. It has to follow the organization's standard policies and all. Governance  is very much required. We don't want to use an AI agent and do some unnecessary work which could lead to a huge loss to the company going forward. We also don't want to do any repetitive tasks by the agent. Governance and cost metrics are required.

The feature meets this need for the company.

We have not used the Autopilot capability or Automator of Automator AI  yet.

I don't use the COE Manager. I don't personally have that level of access.

Deployments were a bit difficult earlier. We had to follow some set of procedures. We had to move from Dev to QA to Prod. The file movement and the code movement were not so easy earlier. However, with the cloud instances, we can directly deploy it, and testing has also been very easy nowadays.

With the last three versions, I can say that deployment has improved. Basically, when it moved to A360 versions and above that, the improvements became evident.

This review receives a rating of nine out of ten.