Our organization has a defined AI automation strategy. We recognize its importance and have prioritized its development over the next two to three years. This focus on AI, particularly in customer service, will help us gain a competitive advantage in the marketplace.

Generative AI has the potential to revolutionize how we implement automation. Our organization sees a significant opportunity to leverage generative AI within the Automation Anywhere landscape. As a bot-focused organization, we have achieved a 30 percent automation feasibility rate. By combining automation with AI, we can achieve a 70 percent rate. This represents the kind of scalability we envision for our organization.

AI, specifically through machine learning techniques, will impact data analysis and decision-making.

Automation Anywhere's AI is excellent for automating any process.

Generative AI stands out in automation, especially for crucial tasks like invoice processing, where it can extract information, or for tasks requiring web research, going beyond simple rule-based automation.

There is AI training on the roadmap, and we're excited to see how much AI will enhance the solution in the future.

Automation and AI used to be a frightening prospect for our clients, who feared job displacement. However, they have embraced the idea after experiencing how these technologies can free up their time for more critical tasks.

AI is the future, and automation is a crucial component. Our healthcare organization, which manufactures products like gloves and masks and provides in-house hospitality to patients across the US, has significant opportunities for automation and AI implementation. With six data centres globally and a new CIO focused on cost reduction, we're aggressively pursuing cloud migration to Azure and have partnered with Google for AI integration over the next five years. These initiatives aim to streamline operations, reduce costs, and enhance patient care.

AI currently offers a variety of APIs that can be combined to create customized strategies tailored to specific customer needs for service provision and operations. By leveraging generative AI and advanced tools like generative AI bots or Copilots, businesses can develop and deploy automated processes to streamline daily activities. By carefully identifying non-automatable processes, generative AI can assist in addressing these challenges and improving overall efficiency.

Typically, extracting information from transactional systems requires tools like Automation Anywhere. For less-structured tasks, generative AI is a great option. One area where these tools can improve is web scraping. For instance, if a website changes, traditional bots fail. However, with generative AI, we can create bots that understand page context and adjust accordingly. For example, if the input field for a flight code changes its location or ID, a generative AI-powered bot can still identify it by understanding the context of the surrounding labels, like "flight code". I believe we'll see a significant evolution in the coming years, with computer vision and generative AI enabling bots to understand the location of information on a screen and adjust web scraping strategies dynamically.

Our primary application of Generative AI for our telecom client is to detect service outages, such as when an area experiences a loss of service. We've been strategically planning our Generative AI approach for this year and the next, focusing on utilizing RPA to identify potential solutions and valuable insights within our data. For instance, in the context of outages, we aim to pinpoint the areas with the highest outage frequency, understand the reasons behind those outages, and correlate that information with customer complaint data. By analyzing metrics like complaint resolution times and outage resolution times, we can create a benchmark that helps us identify areas where we can enhance our customer service.

AI is being used to automate the case intake process, which involves analyzing structured and unstructured documents like emails, articles, and scientific papers. Previously, this task was time-consuming, requiring staff to manually search documents for key information. The process has been significantly accelerated by implementing Automation Anywhere's IQ Bot, with its cognitive automation, AI integration, and natural language processing capabilities. The software automatically highlights crucial details like patient information, product specifics, and adverse events, enabling reviewers to assess document relevance quickly. This automation has reduced review time from 30 minutes per document to five to ten seconds, allowing a greater volume of literature to be processed. Furthermore, the NLP feature automatically identifies and justifies the presence of specific criteria within the documents. By integrating Automation Anywhere into their internal tool, the organization has reduced the workforce needed for this process from over 100 people to 12-15, resulting in increased efficiency, faster turnaround times, and a more manageable workload.

I use a variety of AI technologies for our research and development, including generative AI, custom bit models, GPT-4, and LaMDA. We've also built our own product for GPT-4 called Thor, which utilizes intelligent document processing to extract data from contracts, purchase orders, invoices, and receipts. This data is then integrated with our machine-learning models and downstream systems. Our focus is on transforming our processes using RPA, AI, and cloud technologies, migrating existing systems to the cloud and enhancing them with AI and ML capabilities.

Generative AI is used in some of our automation processes to eliminate paperwork within a three-hour timeframe.

Our automation and AI strategy prioritizes delivering immediate value to customers. We begin by identifying and addressing their most pressing issues to demonstrate the benefits of Automation Anywhere. This approach allows us to establish credibility and build strong customer relationships, paving the way for tackling more complex challenges later on.

In terms of our AI approach, we are very cautious with AI. We are a Microsoft shop, so we are limiting our AI exposure to an Azure tenant-based AI resource. We are probably building our own LLMs to manage the intelligence or the exposure of intelligence to the AI engine. I do not see us ever using external AI modules.

We are on the path of implementing GenAI for digital transformation. We are doing demos of the AI agents to implement GenAI across all the verticals at Boston Scientific. We are figuring out the right opportunities and use cases.

Currently, my company is evaluating building some agnostic processes and creating our own LLM to consolidate data into a single data store. Once that LLM model is ready, we see an opportunity to automate many things using Automation Anywhere by calling those LLMs rather than multiple applications. Our top priority currently is document automation, extracting data from documents, which we have also been doing for the last three years. At the enterprise level, a separate artificial intelligence team is working on building an LLM that can be utilized by all other application teams.

AI governance is important for my company in general. Having governance in place ensures things work out properly and stay aligned. However, that part is managed by our product manager.

In the age of agentic AI, the biggest challenges for me and my company come from people not being open to this technology. In financial services and traditional banks, people are very cautious. They need extensive testing and need to see the technology working to understand its advantages. AI governance is very important to me and my company as many areas are inquiring about AI. Without governance, we would be in chaos.

In the age of agentic AI, the biggest challenges for me and my company come from people not being open to this technology. In financial services and traditional banks, people are very cautious. They need extensive testing and need to see the technology working to understand its advantages. AI governance is very important to me and my company as many areas are inquiring about AI. Without governance, we would be in chaos.

In the age of agentic AI, the biggest challenges for my company are the need for a significant cultural change and managing information security. Automation Anywhere helps me overcome those challenges in part since it empowers people to focus on value-added activities instead of repetitive tasks that hold no value. I hope that Automation Anywhere continues to help me achieve my automation goals.

We are using some AI. There are 20 to 30 tools at this point. So we are trying to bring in Automation Anywhere into one of those ten. It's in the evaluation phase at this point. There's a big team working on it. We want to get the right tool both legally and via a vetting process before we can bring any tool into the company. That's an ongoing process.

AI governance is very important in my company and in general. Before we even tapped into the AI infrastructure, that was one thing we made sure was set up beforehand. We have a whole AI governance policy and a committee. Anytime there's an AI project, it needs to go through that committee. They need to understand all the data that's going to be processed through it, what tools we're going to be using, and what the project is as a whole. It needs to get approval before we can build it out. We had an AI committee and a governance structure in place before we delved into the AI space. All of our data is in a secure cloud. We can't have anything touching the public environment, just based on the sensitivity of the data that we have. It's very secure and strong as a governance structure.

It's part of our CTO's mission to embrace automation tools, artificial intelligence, and robotic process automation, bringing internal processes into the 21st century and beyond. Automation Anywhere helps us overcome challenges in the age of GenAI. The platform has solved our initial challenges as we transition from being a pilot to a long-term customer. With GenAI, challenges revolve around use case identification and implementing AI in processes while ensuring governance and protecting sensitive information, government information, and student information. AI governance and data protection are extremely important for our company.

It's early in our implementation. In the age of agentic AI, my biggest challenges and those of my company include finding the right use case and then choosing the right platform.

Agentic AI can be used today out of the box with minimal configuration, and it now solves end-to-end processes compared to the standalone jobs that RPA could do previously.

We are part of a big transformation program within our firm, and what we have understood over the past several years is that our technology needs to evolve as the needs of the business do. The RPA aspect of Automation Anywhere's platform can only take us so far. Agentic is a game-changer due to the AI disruption that's happening, and Automation Anywhere has been at the forefront of incorporating the best of AI into the product.

In the age of agentic AI, probably the biggest challenges for me and my company are determining which direction to head and settling on the right path. Automation Anywhere can help me with that.

With AARI, our aim is to deploy more autonomous, self-assisted agents across different departments within our company. Regarding IQ Bot, we have been evaluating it; however, we have not deployed or implemented it within our environment yet, as we are actively working on proof of concepts with Automation Anywhere. In terms of feedback for IQ Bot, it is still early for us to determine its learning capacity from feedback to improve automation quality over time; however, the results have been great so far, and we are actively working with the product team, providing continuous feedback and wishlist items for improvements over time as new versions are released.

With all the things happening with AI in the market right now, one of the big challenges that clients have is ensuring the quality. There are fears around AI that people have about whether the AI will hallucinate and those kinds of things. Having a partner like Automation Anywhere, a company that is known in the industry, is a leader in the industry, brings a lot of credibility to how people look to solve those problems and drive that comfort level. Customers also are looking for the kinds of solutions that help enable their workforces to be more effective. Agentic capabilities bring a lot to that. If you think about not just the traditional labor savings, but how you can elevate the performance of the employees that will still be there, the agentic AI brings a lot to the table for those kinds of solutions as well.

AI governance is important for my company, however, we haven't implemented anything with AI yet. When we get there, it will be of high importance. Still, there's nothing formal currently.

We are among the smaller scale clients for Automation Anywhere. The AI component is something we are still not utilizing much because it is difficult to justify the license cost to our clients. In the healthcare sector, there is hesitation to implement AI due to security concerns and a preference for manual processes during their initial step into automation.

As of now, we have not tried AI, but we know that with AI, we can see many improvements for the bots.

Currently, we're determining our path forward with agent process automation and AI. We have an AI council and are still determining where it fits into that picture. In the age of agentic AI, our company's biggest challenges include getting buy-in and understanding across the organization. All decision-makers and stakeholders need to understand what's possible, what competitors are doing, and how the industry landscape is changing.

The biggest challenges for me and my company in the age of A2019 AI stem from being a financial institution and credit union, which creates a particularly conservative environment. This takes more evangelism to get adopted, as financial institutions tend to have more regulatory compliance issues that we must be very conscientious of, so we have to proceed slower on that front. AI governance is definitely getting attention and discussion in my company. Everyone has accepted that it's going to be a huge part of the landscape in the very near future, so it's important to ensure we have everything managed and planned within compliance for protecting private, personal information.

In the age of GenAI, the biggest challenges for my company include changing the entire mindset. Users need to trust the solution and what is being asked of us is that we need to be able to show the value it is bringing. It's not just a matter of trusting the technology, we need to be able to show exactly what we are going to achieve by using this technology. For that, we need to have a proper framework that tracks all the benefits we are extracting from automations and shows them that this will actually take us to the next level.

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.

From my organization's perspective on automation and AI, we have already deployed the use cases we wanted and need to explore additional features that become available in AI. My approach regarding generative AI is specific to generative AI and CoPilot. We need to evaluate these features because we have not yet explored those areas, but we plan to do so in our future roadmap.