Enterprises across the globe, regardless of industry, are embracing enterprise resource planning systems to achieve business agility. According to Allied Market Research, the global enterprise resource planning market is projected to reach $117.09 billion by 2030, growing at a CAGR of 10.0% from 2021 to 2030. Gartner also predicts that almost two-third (65.9%) of spending on application software will be directed toward cloud technologies in 2025. However, there are also some alarming facts associated with ERP implementation.

McKinsey states that more than 70% of all digital transformations fail to achieve their desired ROI.

Oracle states that 70% of implementation projects fail to stay on schedule or within budget, and two-thirds have a negative return on investment due to cost-overruns.

Now, the big question is how the ERP implementation process can be streamlined to avoid failure risks. This is where Artificial Intelligence (AI) can play a pivotal role. AI has been in the news for quite some time with open access to tools like ChatGPT. In this blog, we’ll discuss how AI can be used in ERP implementation to avoid business risks and keep projects within timelines and budgets.

Problems With Traditional ERP Implementation

ERP implementation process refers to installing, setting up, configuring, and customizing an ERP software so that it can meet the specific needs of an organization. Some of the problems with traditional ERP implementation are listed below.

ERP implementation is all about connecting the physical world with digital. It starts with discovering business processes. However, manual process discovery is not only time consuming but also error-prone. The reason being IT leaders rely on interviews with the people involved in executing the process to gather a significant portion of their baseline data. This approach runs a high risk for error as there’s no real way of telling whether or not a person’s view is objective or fully informed.

In some of the cases, the identification and documentation of processes occur at the technology level, resulting in only partial documentation of the processes. This limited visibility into the processes can result in inadequately designed or configured systems. Consequently, this can lead to heightened customer dissatisfaction and increased costs due to redundant resources and duplicated work.

In most of the cases, testing is performed manually. According to a GitHub survey, manual testing is considered as the biggest bottleneck. Since manual execution of test cases is involved, it can be a time-consuming process. It either consumes a lot of your time or sometimes organizations overlook it, which negatively impacts adoption.
‍
Learn more: ERP Implementation Failures and How to Avoid Them
How Can AI Help In Addressing The ERP Implementation Challenges
‍Automated Process Discovery

Using AI, the entire process discovery lifecycle can be automated. AI's computational logic can easily pull out data from applications (ERP, CRM, BPM, and ECM) that are involved in executing a business process from beginning to end. AI tools can further link together various data and process attributes to develop a virtual twin of existing processes, i.e. baseline business process model.

AI tools have the power to simulate and assess processes in various scenarios. Analysts can utilize these tools to redesign and evaluate new processes, as well as create and test automation workflows in a virtual environment known as the "metamodel" before implementing them in the real world. This approach allows for efficient testing and refinement of processes, minimizing risks and optimizing performance before actual implementation takes place.

Enhanced Process Efficiency
In most cases, organizations rely on consultants and experts to advise on how they should redesign processes. This approach is unscientific as it is purely based on the guesses and experiences of that person. Since AI powered tools allow organizations to virtualize and validate new processes, business leaders can re-engineer business processes with minimal risk.

AI tools can drill down data to an unprecedented level of granularity to deliver actionable insights. Business leaders can use these insights to monitor process performance in real time.

Better Risk Coverage
Manual testing cannot ensure adequate risk coverage due to human involvement. AI-based testing tools can generate and execute test cases. They can also detect anomalies to ensure comprehensive test coverage. Furthermore, AI enabled tools can introduce continuous testing in the development pipeline so that bugs can be detected and resolved in the early stages of development. Read our whitepaper on AI and Test Automation.

Process Automation
Robotic process automation (RPA) can be used to automate repetitive, time consuming, and rule-based tasks in ERP implementation. This reduces manual effort, improves efficiency, and accelerates the implementation timeline.

Test Data Management
AI tools can be used to synthesize data that can be used in the testing. AI tools can leverage generative models like generative adversarial networks (GANs) or variational autoencoders (VAEs) to generate diverse and realistic test data that covers a wide range of scenarios, helping to enhance the effectiveness and efficiency of software testing. Read more on test data management.

Natural Language Processing (NLP)

Natural Language Processing (NLP): NLP is an AI-powered technology that enables human-like interaction with the ERP system. One of its use-cases is that testers can create test cases simply by writing in English. AI can autonomously transform these cases into automated scripts that can be used for testing. This will significantly reduce test creation time. Another use-case is that chatbots can be used to deliver real-time support, and assistance while training users on the new ERP system.

These are certain ways in which AI can be used to keep ERP implementation and migration within timelines and budgets. Like any other industry AI can play a critical role in addressing bottlenecks while maximizing the ROI on ERP investments.