Automation Anywhere has enabled repetitive tasks previously done manually to be automated.
Regarding RPA, ever since generative AI was introduced, we have ChatGPT and Google Gemini. The advent of these generative AI models has shifted the automation landscape. Automation has moved from Robotic Process Automation towards Intelligent Process Automation. The difference between RPA and IPA lies in their ability to handle changes. For example, if a website form changes its layout, a traditional RPA bot might fail because it can't identify the fields or buttons in their new positions. However, with IPA, the bot is intelligent enough to understand the fields' requests and can still process the data regardless of UI changes. Besides this, numerous other IPA use cases leverage Large Language Models and generative AI. For instance, a company could have a trained dataset monitored by an RPA bot, which then uses generative AI to create and send daily reports to top management, analyzing current numbers concerning past performance. This is a fascinating area that I've been exploring and working on lately.
For business users without technical skills, automation is achievable depending on the complexity of the task. Simple processes like sending custom emails from an Excel list can be easily automated with basic tutorials. While time and practice are necessary for mastery, basic automation can be initiated with just a few introductory videos.
We recently started using Automation Anywhere Copilot, so we haven't had the opportunity to integrate it with many of our automations. However, we have integrated it with SAP, where the bot reviews SAP data and provides the user with the required information at runtime. I have utilized this feature, and it's quite interesting. They also offer integrations with many other software, so the integration level is relatively high. Regardless of the type of features the business uses, whether they are using Salesforce, Microsoft Dynamics, SAP, or even AWS, integrations are available. They provide custom APIs that can be used for integration.
Automation Copilot helped increase our productivity by 60 percent.
Copilot has enabled staff to focus on other tasks by automating processes. For instance, the business department aimed to automate 350 processes this year, but by September, they had already surpassed that goal with 370 automations. Similarly, last year's target of 250 automations was exceeded, reaching 300. This increased efficiency has significantly improved workflows.
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.
The amount of time Automation Anywhere helps save is dependent on the automated task. For example, the bill review task we automated helped save 10,000 hours per month.
We have several custom ERPs used internally but primarily rely on Microsoft Dynamics. We have a BCRM portal built on the Dynamics portal, hosting both our BCRM business-facing and CRM customer-facing systems. We also utilize Excel with VBA macros and other platforms, including Kofax for OCR. Kofax's Arabic language detection capabilities are crucial for processing UAE ID cards containing Arabic text. Kofax is our organization-wide OCR solution, integrated with Automation Anywhere. Overall, we have integrated Automation Anywhere with various software solutions.
Integrating Automation Anywhere into our workflows, APIs, and business automation is simple. RPA functions like a digital employee, and we can instruct them to perform tasks. Any activity currently done by a human employee can be done via RPA. However, the crucial question is whether it should be automated. If a task is performed infrequently, such as once every six months, creating an automation is inefficient. The time spent developing the automation could be better used to complete the task manually. Automation is ideal for repetitive tasks performed frequently. If a task isn't repetitive, automating it might not be beneficial. Regarding the capabilities of automation, nearly any work an employee performs on an organization's system has the potential to be automated.