International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
Integrating Generative Models in Business Process Automation for Cost Reduction and Efficiency Pradeep Kumar Sharma, ServiceNow, Santa Clara, California, USA ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - While traditional BPA approaches depend on
take proactive actions by basing the analysis on historical data. It can enable businesses to avoid possible bottlenecks and to keep functioning smoothly. Continuous optimization another advantage of integrating generative models is that constant optimization is possible. The models learn continuously and improve on new data, and therefore, their performance improves with time. As a result, always-on business processes can progress from one another, which enable improved results and cost-effectiveness. Incorporating generative models into business process automation could improve the precision and uniformity of operations [3]. By removing human error from the equation, these models can be executed with high accuracy, resulting in better customer satisfaction and decision-making. Even after a few decades, integrating generative models in business process automation can revolutionize the world of business. These models can enhance efficiency, cost savings, and overall business performance by processing complex tasks, making predictions, optimizing processes, and improving accuracy. Thus, the implementation of generative models for business process frameworks is going to be a general practice in the near future as technology advances [4]. Within this context, business process automation leads fast to some of the most practical and attractive solutions, delivering cost-reduction benefits few other approaches can match. Recent developments in technology and the age of artificial intelligence and generative models have led to an increasing interest in the topic of how to use generative models within the field of business process automation. Generative models are a type of AI that can create new data that resembles an existing dataset, and so they could greatly improve the power of automation [5]. That said, there are a few critical technical challenges to solving the successful integration of generative models in business process automation. A notable issue is the required computational power and resources for training and running the generative models during the process. Such models are usually quite sophisticated, and generating new data based on them requires detailed information and processing capabilities [6]. As such, businesses might have to spend more on adding infrastructure and resources, also proving to be costly and lengthy. Another concern is the need for more interpretability and explains ability in generative models. The main contribution of the research has the following:
prescriptive workflows that follow fixed paths, they are challenged by the need to adjust to dynamic business environments. This usually leads to expensive and timeconsuming manual intervention to fix process deviations. This challenge can be answered by the integration of generative models in BPA. Generative models utilize complex AI algorithms to identify patterns and relationships in data and produce new outputs. Integrating these models into BPA enables organizations to free themselves from the limitations of rigid automation and become more flexible and adaptable in how they automate their business processes. That's because now we're talking about automatically adapting to new or varying process variations, leading to lower cost and increased performance, which is the main value of this concept. They can also identify process inefficiency and make recommendations for improvement, most directly improving process performance as well. Generative models in BPA allow for the automation of business processes that are more flexible, accurate, and optimized to help organizations drive both cost savings and efficiency. That is how the new force of BPA is reshaping the world and enabling organizations to stay ahead in the fast-changing world of businesses. Key Words: Limitations, Improving, Generative, Flexible, Algorithms, Environment.
1.INTRODUCTION Generative Models in Business Process Automation the models analyze and learn from vast amounts of data using machine learning algorithms that know how to perform tasks or make predictions without needing to be given specific instructions by humans. This could potentially assist in enhancing and optimizing the tasks, which in turn saves time and money. Handling Complex and Repetitive Tasks: A major advantage of integrating generative models in business process automation is the ability to manage complex and repetitive tasks effectively [1]. This means that these models can be trained to recognize patterns and make decisions based on a set of rules, which makes them capable of performing a task that would otherwise take a lot of time and resources if it were done manually. Such automation can lead to improved productivity and overall business performance by freeing up employees to focus on more strategic and high-level tasks [2]. Generative models can also be used for predictive tasks. These models can highlight potential issues or opportunities in the process and can also
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