Finance is where it starts in most companies. Other areas are slowly adopting it. In banking, automation is taking place in underwriting, loan process origination, loan documents, and servicing. Automation Anywhere has established a strong legacy in finance and accounts. Given its extensive options, it can be implemented across multiple industries such as banking, capital markets, oil and gas industries, specifically in finance and accounts, and HR processes. It is particularly useful for repetitive and bulk document-based policies, setting up vendors, setting up employees, and numerous document-based aspects.

Automation Anywhere has evolved significantly and upgraded itself to provide agentic AI and AI-based automation solutions for document automation. The product has matured considerably over time. We can create workflows that can call an API. We can include prompts in particular workflows for ChatGPT-related functions, connecting to an LLM and RAG to perform tasks. For document automation, modern features are available to train documents, ensuring high accuracy and repeatability over time.

The system is very easy to use. I recently completed a course in document automation, typically designed for people involved in coding and technical aspects. Though I understand coding comprehensively, I don't do actual coding. The course was very accessible. Currently, extensive coding isn't necessary due to the hybrid model incorporating GenAI aspects, low-code, no-code capabilities, APIs, and numerous pre-built objects in Automation Anywhere.

The features include GenAI-driven prompting methods and workflow creation capabilities. In these workflows, we can create decision boxes and call APIs without coding. We simply pull objects, drop them, connect them, and add minimal coding when needed. The most crucial aspect isn't coding but rather sizing the automation and fleshing out the details.

Automation Co-pilot takes notes and performs automated analysis. It can extract details from videos, summarize conversations, and provide detailed information. During calls, it identifies instructions and performs tasks such as preparing reports and reconciliation. Automation Anywhere can also connect with Microsoft Co-pilot. Through Co-pilot, real-time operations can be executed, allowing direct interaction between vendors and automation through this component.

The normal RPA automations and integration with agentic AI are functioning effectively through Automation Anywhere. However, regarding document training, improvement is needed. Currently, it requires 10 or 12 formats for decision-making, which might not be optimal. The limited sample size affects automation efficacy, resulting in 50-60% accuracy. This needs refinement to achieve 75-80% accuracy with document understanding, reading, and processing various document types. The current accuracy rate of 60% for document-based automations requires human intervention. Focusing on improving this area would be beneficial.

I have been working in automation for the past six to seven years.

Maintenance is required for load handling, and some automations occasionally become idle due to sunsets. Customer support for error handling and troubleshooting is necessary. Currently, human dependency remains for these aspects.

The system is extensively used, with scalability depending on the number of licenses acquired. Each bot license handles a specific workload volume. Automation scale can be planned based on workload requirements, necessary downtime, and maintenance needs. Automation Anywhere accommodates these scaling requirements.

They are highly responsive to customer needs. I would rate their service as nine out of ten.

Positive

The setup process is quite straightforward. With agentic AI, we can incorporate APIs, integrate normal automated processes, and third-party tools. The workflow integration process is simplified, offering options to use existing objects or create new ones.

Implementation requirements depend on the specific process. Some processes require extensive in-depth study, while others involve simple automation. The complexity varies based on the process type and documents used.

Both time and cost benefits are achieved. While specific client details are confidential, the solution reduces time, increases accuracy, and decreases the number of process steps required to perform tasks. These improvements result in significant cost reductions.

From an RPA perspective, it is affordable. Integration with LLM increases costs. Considerable focus is required on fine-tuning prompts, standardizing processes, and implementing effective guardrails. The expense of Automation Anywhere varies depending on implementation methods.

The tools include ERP systems and existing automations. Automation Anywhere is platform agnostic and can work with multiple tools, including proprietary customer tools, in a collaborative manner.

Per bot, 0.10 FTE is achievable. Business analysis training requires approximately 20 hours.

Many on-premise features have migrated to cloud platforms to leverage agentic AI benefits. Automations and agents are predominantly deployed in public or private clouds, depending on organizational requirements.

Automation Anywhere continues evolving with a focus on business services. They need to conduct better research on document reading and improve the accuracy of agentic and GenAI automations to differentiate their product in the market.

I rate Automation Anywhere an 8 out of 10.

My main use cases for Automation Anywhere include enterprise use cases in travel, banking, and supply chain. It's used for automating back-office business processes that simplify the work of day-to-day business operations.

The AI Agent Studio plays a crucial role in our automation processes as we use it to create autonomous agents, specifically back-office autonomous agents. We use it to automate processes where AI agents call the RPA bots for automation. The bots will call, and the agents will be calling the bots for the action part, while decision-making is done by AI agents. Wherever decision-making is involved, the agents take the lead. If there are pure actions to be performed, an AI agent will call an RPA bot. If there is any knowledge discovery or details to be searched, then it uses context routing or a RAG index. Finally, whatever an AI agent has done goes through a human in the loop for final verification by a human reviewer. This is a business process and AI agents are new, so we have introduced human verification for any actions done by AI agents. It goes through a human review before actually being performed in core enterprise systems such as SAP, ERP systems, booking systems, or insurance claim systems.

The main challenge or objective I was looking to solve with agentic process automation is related to tasks which cannot be purely rule-based automation, such as reading emails or going through content which is more unstructured in nature. When referring to unstructured nature, it could be contract documents, mortgage documents, reading mail messages, or chat messages. In all those areas, we cannot employ RPA bots to perform the automation because RPA bots tend to expect the data to be in a more predictable, deterministic, and more structured form or a semi-structured form.

The Autopilot capability of Automation Anywhere has been more useful in semi-autonomous processes where I need to discover information from enterprise documents, such as policy documents or more enterprise-related documents. Autopilot is particularly useful for back-office teams because it provides more reliable and precise responses compared to traditional search methods. It is useful for teams in their day-to-day processes where they have to refer to information from enterprise documents, guidelines, or fetch information from SOPs.

The AI Agent Studio compliance feature effectively meets our needs by providing a trust layer that enables us to define which LLMs to use, the level of creativity that we can allow, and creative decisions we can allow it to take. It determines what gets revealed to the model provider versus what doesn't, including whether PII and PHI data is revealed or concealed. It also supports potential tokenization of sensitive data. The guardrail modules are beneficial; if the tool doesn't return the expected response, then it raises an error, alarm, or warning. These configurable settings within APA, in the control room, are a good way to start in order to pursue use cases that we couldn't pursue earlier, which were completely manual.

AI governance is very important in my organization, and the AI Agent Studio compliance feature effectively meets these needs. APA helped achieve automation goals, but it's evolving. We have tried simple and medium use cases, and there are more complex, cumbersome use cases yet to be picked up. For now, the complex tasks that we could not pursue through RPA, we are able to do with APA.

Automation Anywhere adapts well to customers' needs. Customers are migrating or willing to pursue more complex use cases requiring AI capabilities, and their platform supports the needs for more complex implementations.

The robotic capabilities are most valuable. The different screen scraping capabilities have been in use for a long time. Their agentic capabilities and AI capabilities have brought many new advancements and features. Their Autopilot, semi-autonomous agents, and autonomous agents have added numerous new features and capabilities. I would rate them high on these features because they have innovated and kept their platform more centered and relevant in the market by introducing these AI and agentic capabilities.

Automation Anywhere is a good platform with extensive capabilities. Their roadmap is promising, especially the AI roadmap. They have good AI capabilities, autonomous agents, semi-autonomous agents, knowledge agents, RAG capabilities, and AI engineering capabilities for faster development.

Regarding improvement areas for Automation Anywhere, they already have strong AI agentic capabilities. The autonomous agents and semi-autonomous agents have decent capabilities, and I don't see any gaps with the way it's been designed. We tried creating agents, autonomous, and semi-autonomous agents with Automation Anywhere, and it is working fine. We see good capabilities and faster time to implement. It has good low-code implementation capabilities, so we are able to perform implementations faster using the agentic AI stack in Automation Anywhere. The architecture and pro-code support for pro-code agents are areas that could use improvement, which they don't have currently.

I have been working with Automation Anywhere for five to six years.

Regarding stability and reliability, Automation Anywhere is good.

For automation at scale, it's a reliable platform. I can implement a large number of automations, and the platform performs well. I don't see any issues with stability or scalability; both are satisfactory. I can scale it in terms of hundreds of bots and configure hundreds of parallel robots easily.

I often communicate with the technical support of Automation Anywhere, and their technical support teams are excellent. They provide great support. Especially if you are a premium partner or a gold partner, you have a very good support option, including a partner success manager assigned to you with good SLA deadlines within which your query is resolved.

Positive

I have participated in the initial setup of Automation Anywhere as they are providing a SaaS cloud model, so the setup is quite easy now. 

In the initial setup, you don't have to invest much effort because they now have a complete AI SaaS module, and even the A360 has a SaaS module. You can subscribe to it and just have things connected. There could be some plugins or certain settings for the local studio, or another local robot which might require certain add-ons and dependency packages. But it's more of an initial effort investment; once you set things up, it works fine.

They offer both on-prem and SaaS cloud solutions, but on-prem may require more effort in setup. SaaS cloud is easier to utilize and configure. Nowadays, most customers are migrating to cloud options, which come with various benefits and ease of setup, including commitments in provisioning servers. Multiple benefits are available, and many customers, including those in the Middle East, are migrating towards cloud setups.

It's affordable because the subscription allows you to use a certain number of tokens from the LLM provider. You only pay for the tokens you actually utilize, and you won't pay more than that. This applies regardless of which service you are subscribed to, whether it's Gemini, OpenAI, Anthropic, or any other provider.

I would rate Automation Anywhere an eight out of ten.

I am in the infrastructure service offering team, and I am focused on product ownership. My role involves overseeing how technology and new versions of products are defined across the bank. For example, I am working on adapting our systems to the cloud for automation and integrating Generative AI. This involves utilizing Agentic AI for process automation.

My responsibilities include integrating product features and assessing how these functionalities can benefit the bank through various use cases. I conduct proof of concepts and demonstrations from a governance and compliance perspective, acting as the architectural strategist. Additionally, I oversee product models related to anti-money laundering (AML) and engage with various business teams including wealth management, retail banking, marketing, and services banking to showcase how these features can enhance automation.

As a product owner, I also train teams and raise awareness about new technologies that can expand their automation capabilities. It’s essential for me to understand all functionalities and features of the product, especially since we're in the banking and finance sector. We cannot blindly integrate any AML model; compliance and governance are crucial in this field.

I must ensure that any integration is safe and sound, focusing on control perspectives, model training, and handling confidential data with appropriate encryption. I have a team to support these processes and ensure everything adheres to banking standards. My goal is to communicate with the business teams about new technologies, educate them on how these innovations can improve automation, and help them leverage these benefits. That is my primary focus right now.

Automation Anywhere helped our organization for repeated manual work and automate structured/unstructured document use cases

The features of Automation Anywhere are quite impressive. One aspect I particularly appreciate is the integration of AI. AI has reached a mature level in its capability for agentic processes, which greatly enhances product usability and enables adaptation to complex use cases. Traditional RPA is no longer sufficient; it has become outdated in this regard. In my opinion, RPA has been essentially irrelevant for the last two to three years. What we are seeing now is hyperautomation, particularly intelligent automations that have evolved into what I refer to as Agentic Process Automation (APA). 

With Agentic Process Automation, we can manage end-to-end processes, particularly where human intervention is necessary. We can streamline these processes by integrating API tasks and obtaining responses from various sources, such as OpenAI. This integration leads to a higher level of accuracy in our systems. At our bank, we are moving away from the notion of traditional RPA and focusing on APA, emphasizing that everything will increasingly rely on APIs. Additionally, we are exploring how to facilitate seamless integration across different products. Our goal is to create a plug-in capability that allows for easy integration not only with RPA products but also with other systems to achieve comprehensive end-to-end automation. This is what we are striving for in our current initiatives.

Their vision is promising, but the product requires significant improvements in maturity and usability. I'm concerned that the product is not user-friendly. They continue to release new features, but end users struggle to adapt to them. This indicates a lagging development in product usability.

From a product maturity perspective, there is an area of improvement. We are currently on-prem, and as we transition to the cloud, there are numerous restrictions from our banking organization that we must consider. When planning to scale the product, I need to understand how it can handle increased bots. For instance, if I start with 1,000 bots and later expand to 1,200 bots, will the platform still be able to support this growth?

Additionally, as we integrate new features such as document automation, Intelligent Document Processing (IDP), and CoPilot capabilities, I need assurance that the current platform can sustain these enhancements. It is essential for Automation Anywhere to improve in these areas so we can effectively increase the scope of automation within the platform.

I have been using this solution for more than seven to eight years.

The stability of the product is quite good, and I am not facing any difficulties presently, though I have concerns as we add new features and hope to work closely with the vendor to address any potential stability issues.

The product is quite scalable from a high availability perspective and even offers seamless scalability in the cloud; however, there is a bit of lag in the on-prem setup which needs improvement, although currently everything is operating smoothly.

From a support perspective, we are quite aligned with the Automation Anywhere team and very happy with their approach. While there may be delays in enhancements, they acknowledge our suggestions and maintain a good relationship over the years.

Positive

I used Blue Prism over six or seven years back while working with a major bank in South Africa. At that time, we successfully implemented around 700 bots in production, marking the beginning of our journey into robotic process automation (RPA). After my experience with Blue Prism, I transitioned to Automation Anywhere, where I continued my journey in the U.S. at one of the largest banks—specifically, the third largest bank. We have successfully implemented around 1,000 bots, which are still operational today.

In addition to RPA, I have ventured into the realm of intelligent automation, focusing on providing end-to-end process automation solutions. My role encompasses solution architecture, advisory services, strategy development, and team leadership. I oversee various automation products, including IP products, workflow products, data messaging products, and Excel remediation products, ensuring comprehensive management of the entire process automation landscape.

Regarding pricing, I do not have extensive detail, but it varies based on the agreement with clients. Since we have a good relationship with Automation Anywhere, I do not see much difficulty in licensing costs or flexibility in licensing models compared to other products.

I can recommend Automation Anywhere. If you are considering Automation Anywhere, my advice is to avoid the on-prem setup and instead go for the cloud-based product to leverage more features, better scalability, and high availability, especially noting the lack of device pool limitations in the cloud environment which facilitates parallel bot operation without the restrictions found in the on-prem setup. 

I would rate Automation Anywhere an eight out of ten.