Published: 8 July 2024
Contributors: Molly Hayes, Amanda Downie
Artificial intelligence (AI) in enterprise resource planning (ERP) refers to the integration of AI technologies such as machine learning (ML), natural language processing and predictive analytics into ERP systems. These AI-powered systems can automate routine tasks, provide advanced data analysis and forecasting, and enhance decision-making. The goal of AI in ERP is to improve operational efficiency and streamline business processes.
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With AI, traditional ERP systems can transform into intelligent platforms that learn from data, adapt to changing conditions, and optimize business intelligence in real-time, increasing overall efficiency and reducing costs. According to a recent report from the IBM Institute for Business Value, organizations applying generative AI solutions to their SAP data are already experiencing greater profitability.
ERP vendors typically design their systems as a series of modular apps. Which together have the capacity to manage every part of a business from an organization’s finance department to procurement and supply chain logistics. Since the introduction of the term “ERP” in the 90s, the ERP software industry has grown into a USD 44 billion-a-year market.1 Today, many leading global corporations use some form of ERP solution to access a “single source of truth” across the entire business.
As ERP software became more popular—and its capabilities more robust—organizations adopted these systems as part of a cohesive business strategy. Rather than acting as another piece of software, ERPs have the potential to uncover new insights and significantly impact business processes, as well as provide new avenues for business intelligence. And through the 2010s, ERP systems became critical for the management and analysis of big data as modern organizations generated and collected more information than an individual might possibly process.
Over the last decade, AI-enabled ERP systems have automated select tasks like data entry and analysis. But more recent advancements, such as generative AI, have begun to dramatically transform the ERP landscape. Cloud ERP systems benefit from more computing power, supporting more robust AI applications.
Advanced machine learning models and natural language processing capabilities have made ERP systems more user-friendly and precise, ushering in a new era of sophisticated business software. The promise of today’s AI-enabled ERP systems has been reflected in a handful of recent business deals. Including Microsoft’s USD13 billion partnership with OpenAI and the introduction of its AI-assisted ERP software, Microsoft Dynamics 365.2 SAP, another leading ERP vendor, announced its “Joulie” generative AI assistant in 2023.3
ERP software uses AI technology in various ways to improve and manage business operations. Some AI technologies frequently integrated into ERP systems include:
Natural language processing (NLP) allows an ERP system to understand and respond to human language, facilitating better user interactions. In recent years newer large language model (LLM) technologies such as ChatGPT have significantly improved the discipline, allowing for more nuanced and contextually relevant NLP tools within ERP software.
For example, NLP can process unstructured text like customer emails to perform sentiment analysis, or understand back-office user queries in informal language, making the software more intuitive to use.
Robotic process automation (RPA) automates routine and repetitive tasks—or entire workflows—using “bots.” Applications include data extraction, data entry and file migration. Using RPA, an ERP system might automatically generate reports, distribute key human resources documents, or automatically provide data management for customer and employee information.
Machine learning (ML) systems “learn” from data over time to improve predictions and decision-making processes. Applied to ERP solutions, the technology can help reduce operational errors and increase efficiency as an AI becomes better at a task over time. As ERP systems tend to harness large quantities of organization-specific data, ML models trained for specific business cases can have a major impact on an ERP’s functions.
Chatbots and virtual assistants take advantage of NLP to provide real-time support, improving customer experience and ushering employees through ERP software workflows. In an ERP system, chatbots and virtual assistants are adept at handling employee self-service portals, such as answering questions about routine human resources tasks.
Image recognition or computer vision, uses AI to identify visual inputs such as objects, text or locations. ERP systems use the technology to analyze visual data—such as videos or scanned documents—and render them in searchable or editable formats. Image recognition technology can also be used to monitor manufacturing materials for better quality control.
In recent years, conversational AI and generative AI upended business processes by mimicking human intelligence and adding more functions to ERP systems.
Using the power of AI, ERP platforms can develop reports or recommendations, providing organizations with actionable insights based on real-time data collection. Some of these applications include:
Generative AI can automatically create detailed business reports from raw data, saving time and ensuring consistency. These reports can be generated on-demand, providing stakeholders with information as they need it.
Generative AI can draft emails, marketing content, code or technical documentation based on predefined parameters. Applications include generating personalized messages to individual consumers or employees or translating code from one language to another.
AI generates various business scenarios and evaluates potential outcomes, significantly advancing the strategic planning capacity of earlier ERP systems. For example, an AI-enabled ERP system might analyze sustainability regulations and produce a series of customized recommendations to reduce an organization’s carbon footprint.
Given the number of AI tools available for integration with ERP platforms, the technology has a wide range of practical applications and potential use cases. Some common AI ERP implementations include:
A predictive maintenance system typically involves Internet of Things (IoT) sensors or digital twins. Using these systems, an organization can monitor a piece of crucial equipment to forecast routine maintenance or flag issues, preventing unnecessary disruptions or costly last-minute repairs.
Industries such as transportation, energy civil infrastructure and defense significantly benefit from intelligent predictive maintenance, as it can prevent potentially dangerous breakages or outages. The technology has been used successfully to increase wind farm output and decrease the amount of unnecessary energy used by agricultural manufacturing plants.
Demand forecasting in ERP systems can be critical in the production planning process. Using historical internal data—and sometimes third-party datasets—an organization can anticipate how the market might fluctuate, enabling more precise planning. In ERP systems demand forecasting can be merged with inventory management systems to prevent stockouts.
Machine learning also augments the spend management process. Oracle’s financial AI tools automatically optimize cash flows, by using algorithms to compare projections to actual performance and generating more precise cash forecasts.
AI transforms the code development and migration process by intelligently automating coding, testing and application lifecycle management—various ERP systems tools for automating code translation or the migration of data.
NLP and RPA streamline the processing of invoices and other routine paperwork, reducing manual entry errors and speeding up payment cycles. Some of SAP’s ERP modules automate receipt and invoice verification for deliveries to production sites, while Oracle’s AI-assisted financial tools process supplier invoices with document recognition and intelligent invoice entry.
AI-enabled ERP systems dramatically improve the customer relationship management (CRM) process. Using NPL and ML technologies an ERP can automatically resolve common issues, improve the user experience, and respond to consumer queries in real-time, 24 hours a day. For example, SAP’s customer relationship management module uses generative AI to write emails and prepare account briefings.
ERP modules designed for human capital management (HCM) use AI functionalities to automate routine tasks, personalize the HR process for employees, and surface talent during the recruiting process.
SAP SuccessFactors, for instance, provides personalized learning recommendations to over 4 million client employees a month and automatically sources candidates matching specific job descriptions.
Machine learning algorithms and AI-enabled search functions embedded into business to consumer (B2C) and business to business (B2B) purchasing platforms surface goods and services meeting specific criteria.
For example, recommendation engines can provide procurement specialists with bids conforming to specific sustainability or budget constraints, as in SAP’s Ariba network.
Process mining uses algorithms to analyze a business’ workflows. With the large volumes of historical organizational data stored within an ERP product, AI can recommend more streamlined, cost-effective or sustainable processes—as well as reveal inefficiencies or pain points.
Anomaly detection was one of the first major use cases for AI in ERP systems. The technology automatically flags potential fraud issues, providing an early alarm system for stakeholders and freeing up compliance experts for more complex tasks.
Historically, anomaly detection has been useful for banks and other financial institutions, though in recent years the use case has been applied to more complex parameters like predefined KPI standards.
Intelligent order management can monitor and optimize nearly every aspect of the ecommerce and fulfillment process. From dictating fulfillment routes based on specific constraints to automatically updating customers on the location of their goods. Integrated into an ERP system, these AI-enabled order management tools combine multiple datasets to ensure that the commerce process runs smoothly end-to-end.
The IBM Sterling® Order Management platform, for instance, merges sales channels into a unified data stream that tracks inventory levels and organizes customer orders-as well as managing returns and shipping options. The system also identifies potential disruptions, improving supply chain resilience.
NLP and ML can summarize lengthy reports or documents, providing key insights to human workers. For example, an organization might use AI algorithms to glean key takeaways from legal or compliance documents, or generate summaries of internal reports.
AI-enabled ERP systems reduce human error through automation and advanced data analytics, quickly and accurately exploring large datasets that can be impossible for a human to understand.
A successful, agile business responds to market changes and unexpected events quickly. With AI-enabled ERP systems, organizations can ensure that all business processes are operating at the most efficient possible level and respond to challenges quickly with real-time insights and analytics.
ERP systems with process automation autonomously perform rote labor like invoice processing and order management, freeing up human employees for more creative and valuable work.
AI can identify and mitigate security threats or anomalies faster, and with more accuracy, than human employees. AI does this by continuously monitoring systems for unusual activity, AI-enabled ERP systems significantly enhance an organization’s overall security.
According to a recent report from the IBM Institute for Business Value, 64% of CEOs say that they are facing significant pressure from investors, creditors and lenders to accelerate AI adoption. But well over half of businesses don’t yet have a consistent approach to implementation. By choosing intelligent ERP systems and pursuing a thoughtful implementation, these organizations can reap the benefits of AI. Some best practices for implementation include:
Careful data governance: Data used to train and tune a successful, purpose-built AI is generally high quality, error-free, and stored securely.
Scalable infrastructure: Whether an AI-enabled ERP is hosted in the cloud or as a hybrid of on-premises and cloud, investing in scalable IT infrastructure helps support advanced AI capabilities.
Continuous monitoring: By regularly monitoring and updating AI-enabled ERP systems—or engaging a third-party partner—organizations can maintain the system’s performance and ensure long-term success.
Well-considered integration strategy: As with other ERP and AI implementation practices, a clear integration strategy that aligns with core business objectives typically helps organizations realize their goals.
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Footnotes
1 “Market Share Analysis: ERP Sofware, Worldwide” (link resides outside ibm.com), Gartner, 01 August 2023.
2 “Introducing next-generation AI and Microsoft Dynamics 365 Copilot capabilities for ERP” (link resides outside ibm.com), Microsoft, 15 June 2023.
3 “SAP Announces New Generative AI Assistant Joule” (link resides outside ibm.com), SAP, 26 September 2023.