Enterprise resource planning (ERP) software enables organizations to visualize processes across various departments, allowing them to make quicker, more informed decisions. Generative AI, in conjunction with technologies such as RPA, has the capability to improve ERP processes. Microsoft is currently providing generative AI ERP solutions via its Dynamics 365 Copilot tool, and it is anticipated that other companies will soon adopt similar approaches.
Enhanced Strategy and Framing
The crucial initial phases of an ERP implementation are frequently hindered by a lack of complete insight into the existing situation and misalignment among stakeholders.If these problems are not resolved, they can greatly elevate the risk of delivery later in the project. GenAI presents groundbreaking opportunities to tackle these issues, particularly in enhancing the analysis of the current state and automating the collection of requirements. By leveraging GenAI in these domains, organizations can ensure a reduced risk of delivery during the strategy and framing phase, gaining improved clarity on both current and future operational environments.
Automate the Collection of Requirements. GenAI can decrease the time required for this typically manual and error-prone process by 30% to 60%. Utilizing a large language model (LLM) is optimal for capturing, categorizing, and prioritizing business requirements from sources such as stakeholder interviews, system data, and business goals. Practically, this involves implementing your selected LLM within a requirements management tool. While the cost savings are beneficial, the true advantages are strategic: business requirements can now be documented and prioritized in line with strategic objectives, aligning stakeholders and subsequently generating numerous follow-on benefits later in the process.
Enhance the Evaluation of Transformational Potential. GenAI, when paired with process mining tools, can provide crucial insights. This will lead to improved analysis and a 20% to 40% reduction in the time spent on this task. Practically, this means that AI-driven simulation tools can be integrated with existing ERP systems to model various future target scenarios, offering insights into potential future states and delivering predictive analysis of their performance. This ensures that ERP transformation targets are based on data, aligning both short- and long-term business objectives. It will also facilitate faster harmonization of technical capabilities and strategic goals.
Streamlined Execution and Implementation
The execution and implementation phases are frequently the most difficult for ERP projects, often hindered by scope creep, delays in timelines, and compromised quality. GenAI’s capability to automate tasks and improve decision-making during these phases provides substantial benefits, such as assisting teams in minimizing manual efforts and enabling them to concentrate on higher-value activities.
GenAI in ERP is capable of analyzing organizational data, including legacy system code, process flows, and functional specifications, which offers a more thorough understanding of the current state. This can result in a time reduction of 20% to 50% for this phase. On a technical level, this involves deploying a large language model (LLM) tool onto document repositories. It may also entail utilizing tools that incorporate GenAI, like the LeanIX inventory builder or the SAP Signavio repository builder, to evaluate existing business processes and technology architecture. The enhanced understanding gained allows for more accurate decision-making, minimizing the blind spots that are often present in traditional methods.
LLMs can decrease the time spent on document creation and updates by 30% to 50%. They can be integrated with document management systems to automate the creation and updating of documentation, which includes functional specifications, technical designs, and test cases. These tools can also be linked to project management platforms for real-time updates and enhanced consistency, allowing teams to dedicate their efforts to higher-value tasks.
GenAI-driven data intelligence tools can automate a significant portion of the data cleansing and mapping process—typically one of the most intricate elements of an ERP transformation—by automating much of the “extract, transform, and load” data migration. This can lead to a reduction in data preparation and mapping time by 20% to 30% and ensure that cleansing and mapping are performed to a high standard. It can also shorten the duration of each data migration cycle (testing based on simulated data prior to the final cutover), thereby facilitating more testing cycles. This paves the way from legacy systems to the new ERP environment.
Performance Optimization Done Right
GenAI can decrease the time needed to develop and validate intricate, company-specific code that links various modules and systems by 30% to 40%. Additionally, code generation models can automate quality assessments and enhance consistency. This promotes standardization and best practices, thereby minimizing the risk of errors. ERP vendors are at the forefront, providing GenAI-powered tools (like SAP’s Joule) that facilitate the rapid development of high-quality code.
The time savings GenAI can offer during testing can be as high as 60% to 70%, but this is merely one of its advantages. The technology excels at producing test scripts based on design documentation, historical testing data, performance metrics, and defect reports. These scripts can address both functional and non-functional requirements, coming closer than traditional approaches to achieving 100% test coverage. This broader array of scripts can subsequently be executed by automated testing tools to ensure early detection of defects, adding additional rigor to the testing process.
The human aspect of an ERP implementation is vital. AI-driven chatbots, after processing project documentation, can be integrated with learning platforms. Lengthy training sessions for implementation teams can be substituted with in-context, just-in-time knowledge transfer. In comparison to conventional learning methods, the time required to onboard new team members can be reduced by 50% to 60%. Furthermore, once the ERP system is operational, user errors stemming from insufficient training can be decreased by 50%. GenAI achieves this through concise coaching sessions and chatbots that provide tailored advice based on user behavior and challenges.
Gen AI’s Positive Capability on ERP Map
The incorporation of GenAI into ERP transformation initiatives enhances efficiency in both strategic and operational capacities, albeit in distinct manners. Strategic positions, including executive sponsors and program directors, gain from improved decision-making facilitated by real-time insights and predictive analytics. For instance, program directors can monitor project health and risks through GenAI-driven dashboards, which enables more agile resource allocation and alignment with business goals. Although these roles are less influenced by automation, they achieve significant efficiencies by minimizing manual oversight and concentrating on high-impact decision-making.
Operational roles experience significant advancements through automation and optimization. Functional consultants can utilize GenAI to refine fit-gap analysis and process mapping, significantly speeding up delivery timelines. Likewise, testers benefit from the automation of test case generation and execution. GenAI assists teams in identifying defects sooner and ensuring greater accuracy with reduced manual effort. Data migration specialists and developers also witness considerable enhancements, as GenAI automates data cleansing, mapping, and repetitive coding tasks, allowing them to concentrate on more valuable challenges.These operational enhancements positively influence strategy as well; improved and expedited execution yields more reliable and timely data for governance and decision-making.
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