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Artificial Intelligence in Enterprise Processes

Introduction

Running an enterprise involves a great number of tasks that require proper analysis and data and effort coordination, and enterprise resource planning (ERP) systems are widely used to facilitate process management. New technology, including artificial intelligence (AI), is increasingly implemented to make the existing ERP solutions even more effective. Today, AI can be listed among the fields of computer science that attract the attention of specialists in diverse industries, which is due to the potential of AI in ERP and process improvement.

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AI: The Definition and Applications to ERP and Enterprise Processes

On the whole, AI refers to machines’ ability to develop a form of intelligence, thus becoming capable of acquiring new knowledge and using it to complete tasks of different complexity levels. Thanks to AI technology and revolutionary research, machines are becoming able to cope with tasks that come easily to physically and cognitively healthy people, such as using information perceived in different ways to make decisions and study the environment.

Although not every business uses AI to a full extent, modern enterprises can already implement AI technology to deal with various tasks. The term being discussed is quite broad and may refer to a number of processes and technologies, including natural language processing and deep learning.

AI in enterprise processes and ERP presents a relatively new field of research, which explains the limited availability of AI-driven software solutions for enterprises (Basl & Novakova, 2019). As of now, AI belongs to the central trends associated with the planned transition to the ERP 4.0, but its use in enterprise processes is in its early stages. In the context of ERP systems, the primary role of AI and machine learning, which is one of the subsets of AI, is to optimize processes by decreasing the workload on the human workforce. To do it, AI technology is used to complete some routine tasks that do not involve making fateful decisions but still require focus and concentration (Basl & Novakova, 2019).

Therefore, AI can help enterprises to optimize employment costs, simultaneously reducing expenses associated with human errors. Another function of AI-based resource planning solutions for enterprises is to facilitate external communication (Basl & Novakova, 2019). In particular, this goal can be achieved by implementing AI-based digital assistants.

AI in Dealing with Customers

Resource planning systems utilized by enterprises can be expected to undergo positive changes when it comes to communication between representatives of companies and customers. Nowadays, AI technology allows software developers specializing in ERP to create AI digital assistants enabling companies to collect and process information from customers in a faster and more effective manner. As an example, Infor, a large producer of ERP software, has presented its Coleman AI Digital Assistant recently (Jha, 2018). The product in question is aimed at facilitating the work of interaction specialists and significantly reduces the time needed to process customers’ requests (Jha, 2018).

The assistant makes use of natural language processing and machine learning technologies allowing it to search for items requested by particular customers and can even analyze customer information to suggest personalized offers (Jha, 2018). Therefore, AI technology changes ERP systems by giving rise to tools that minimize customer response time, thus improving consumer experiences.

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AI can improve interaction with customers by using chatbots integrated with ERP software. According to a survey conducted by Oracle in 2016, at least 80% of sales and marketing specialists had already implemented such solutions or were planning to do it by 2020 (Bergdahl, 2018). Chatbot integration services are becoming increasingly popular as AI-powered chatbots provide inexperienced users with new tools to analyze customers’ current needs and requests with reference to their order histories and other types of information (Iyer, 2018). However, direct communication with customers is not entrusted to AI-based applications even despite the predominance of routine tasks in this type of workplace activity (Bergdahl, 2018).

AI in Dealing with Suppliers

Similarly to the situation with customers, AI integrated into ERP systems also helps to facilitate managing organizational processes involving collaboration with suppliers. One way of how AI enables large companies to build productive partnerships with their suppliers is the existence of ERP tools for the automated processing of invoices. Large-scale businesses may need to process dozens of invoices daily, and manual processing often increases the risks of human errors and delays that put further collaboration in jeopardy. Modern specialists in resource planning regard ERP applications for automated invoice management as an example of basic and the most popular AI-powered automation tools (Bergdahl, 2018).

On the one hand, invoice management automation enables employees in financial departments to devote more time to tasks that require human thinking instead of focusing on routine work. On the other hand, process automation is beneficial to companies’ suppliers who are interested in getting payments on time.

Modules to manage relationships with suppliers are often included in ERP software solutions, but the extent to which AI is used in this aspect of communication can be difficult to estimate. As Dash, McMurtrey, Rebman, and Kar (2019) mention in their literature review, machine learning is applied by companies in high-technology fields, such as the aerospace industry. It is said to propel supplier communication to the next level by increasing the transparency of supply chains.

AI and Internal Operations

AI affects ERP systems when it comes to internal operations by providing new ways to eliminate disconnectedness between different departments. The so-called intelligent or I-ERP solutions are expected to make use of advanced analytics and machine learning to create benefits related to the diverse aspects of effort coordination (Jenab, Staub, Moslehpour, & Wu, 2019). The speed and accuracy with which different departments of an enterprise exchange information is a specifically important area for improvement targeted by the new intelligent systems of resource planning.

Speaking about particular ways of how AI affects internal operations, I-ERP systems are supposed to take quality control measures to the next level. They improve automatic incident detection by providing users with early and timely alerts in case of manufacturing nonconformances and generalize on findings from different departments to suggest possible solutions (Jenab et al., 2019). Intelligent ERP systems are anticipated to gain traction in the future and use their opportunities for self-learning and access to full information on business processes to reduce mental effort that internal decision-making in enterprises usually involves (Jenab et al., 2019; Morris et al., 2016).

Discussion of Findings

The findings above can be considered important since they demonstrate general trends peculiar to AI as it applies to different enterprise processes and ERP systems. As is claimed by multiple experts in ERP, midsize enterprises are more likely to implement “basic and incremental AI” to decrease costs associated with specific processes (Bergdahl, 2018, p. 2). The promise of AI is definitely great today, but it is too early to say that it has already revolutionized ERP and enterprise processes.

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Considering that intelligent ERP systems are not used massively despite their multiple advantages, it is possible to say that AI-driven solutions for enterprise management are not appreciated at their true value, at least now.

To continue, the findings indicate that AI continues to penetrate diverse kinds of software aimed at the simplification of business processes in enterprise management. Modern AI solutions are supposed to help enterprises to meet multiple goals, including staying popular thanks to being able to respond to customers’ requests faster than the competitors. Other ways of how AI changes ERP systems are presented by reducing the amount of time needed to work with documentation, which includes invoice processing.

Importantly, the generality of some findings indicates that the uses of AI in ERP and organizational contexts are still to be researched by the scientific community to include the analysis of multiple real-life cases. Despite the promising nature of AI-driven ERP solutions, it is not entirely clear whether companies’ actual benefits from using them outweigh the risks and challenges associated with their implementation. With that in mind, more research is needed to predict the exact time of the AI revolution in enterprise management.

Conclusion

To sum it up, AI affects ERP systems even though AI-driven solutions are not implemented by the majority of businesses. AI is integrated into ERP systems to increase customer satisfaction by using digital assistants allowing communication specialists to access information from diverse departments to process consumers’ requests. As for communication with suppliers, AI can be used to speed up invoice processing and facilitate supply chain management with the help of machine learning. Concerning internal operations, there are some intelligent ERP systems that use AI to improve teamwork, control production quality, and provide managers with assistance in decision-making.

References

Basl, J., & Novakova, M. (2019). Analysis of selected ERP 4.0 features and proposal of an ERP 4.0 maturity model. In P. Doucek, J. Basl, A. M. Tjoa, M. Raffai, A. Pavlicek, & K. Detter (Eds.), International conference on research and practical issues of enterprise information systems (pp. 3-11). Cham, Switzerland: Springer.

Bergdahl, J. (2018). The AI revolution: A study on the present and future application and value of AI in the context of ERP systems (Master’s thesis, Uppsala University, Uppsala, Sweden). Web.

Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), 43-53.

Iyer, S. (2018). 4 reasons to integrate your ERP system with chatbots [Blog post]. Web.

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Jenab, K., Staub, S., Moslehpour, S., & Wu, C. (2019). Company performance improvement by quality based intelligent-ERP. Decision Science Letters, 8(2), 151-162.

Jha, S. (2018). Coleman AI Digital Assistant is set to redefine the future of work: Infor’s Rick Rider. ETCIO. Web.

Morris, H. D., Rizza, M. N., Mahowald, R. P., Hayward, D., Jimenez, D. Z., Motai, Y., & Stratis, A. (2016). I-ERP (Intelligent ERP): The new backbone for digital transformation. Industry Development and Models, 1-12.

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