If you operate a GenAI-supported ERP system, your company will benefit from more efficient processes, shorter decision-making paths and significant cost savings. This is because the AI analyzes relevant company data and independently generates useful recommendations for action from the results.
We will show you exactly how GenAI in ERP is now revolutionizing everyday working life using specific practical examples. At the same time, we also take a look at the challenges of using AI in ERP – so that you can mitigate potential risks from the outset.
What is GenAI? Definition and how it works
The abbreviation GenAI stands for “generative artificial intelligence”. In this sub-area of AI, the technology generates new content instead of simply analyzing existing information. Based on large training data sets and neural networks , it learns patterns and uses them to independently develop content.
When used correctly, generative AI accelerates numerous creative processes that used to take significantly longer. This includes, for example, the creation of the following media types:
- Texts (articles, e-mails, summaries, etc.)
- Images and graphics
- Audio (e.g. music and speech)
- Videos and animations
- Code
GenAI findet heute bereits in vielen Bereichen der Geschäftswelt Anwendung, um Mitarbeitende bei ihrer täglichen Arbeit zu unterstützen. So nutzen in Deutschland inzwischen 67 % der Beschäftigten regelmäßig generative KI am Arbeitsplatz. Das besagt eine globale Studie der Strategieberatung Boston Consulting Group (BCG).
The exchange between system and human is always interactive. A widespread application example is written and verbal interaction with a Large Language Model (LLM) such as OpenAI’s ChatGPT or Google’s Gemini: After a text or voice input, the underlying language model processes the previous context and continuously evaluates which linguistic units fit best next. The response is generated step by step from these probabilities, resulting in coherent and context-related texts.
GenAI in ERP: How is generative AI changing usage?
Wer schon einmal mit einem klassischen ERP-System gearbeitet hat, kann ein Lied davon singen: komplexe Masken, überladene Oberflächen und die Suche nach Informationen können die Nerven strapazieren. Viele Aufgaben gestalten sich dadurch so umständlich und zeitraubend, dass sie sich eher stumpfsinnig als wertschöpfend anfühlen.
This changes with GenAI in ERP:
- The software is evolving from a sometimes complicated tool into an active partner that recognizes connections and is always on hand as an assistant.
- Instead of laboriously clicking through menus, users simply interact with the ERP system in natural language.
- In the best case scenario, the AI technology makes useful suggestions before the user even asks for them.
AI agents also keep an eye on entire process chains – from the initial inquiry through to delivery. Without any human intervention, they are able to identify problems within the value chain and independently initiate optimization measures.
| ERP system without GenAI | ERP system with GenAI |
| Reactive tool | Active assistant that thinks for itself |
| Complex masks, overloaded surfaces | Intuitive, dialog-based interaction |
| Navigation via menus and functions | Process-related workflow |
| Manual triggering of analyses necessary | Automatic evaluation and identification of problems |
| Optimization measures require human action | System makes independent recommendations for action |
Important here:
Erst das intelligente Zusammenspiel von Mensch und Maschine liefert einen echten Mehrwert. Während generative KI im Unternehmen für mehr Tempo sorgt und Empfehlungen ausspricht, bleiben die Steuerung und Entscheidungshoheit nach wie vor dem Menschen vorbehalten. Ziel ist es also nicht, den Menschen komplett zu ersetzen, sondern eine starke Symbiose zwischen KI-Technologie und Mitarbeitenden zu schaffen.
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Generative AI use cases in ERP: sales, merchandise management and service
Generative AI in sales
1. support in researching interested parties
Im ERP-System sind sämtliche Vertriebsdaten zentral gespeichert, darunter:
- Customer master data
- Customer history
- Former contacts
- Open opportunities
With a GenAI-supported ERP system, this valuable treasure trove of data can be analyzed in a targeted manner and thus optimally used to identify contacts.
Sales teams can ask the system simple questions such as: “Which existing customers haven’t ordered anything for a long time but are a good fit for our new product?”. As soon as the AI has searched the internal CRM database and added contextual information from external sources, it creates compact profiles of potential prospects. If desired, the technology can also generate possible conversation starters and automatically handle the initial contact.
2. lead qualification and prioritization
When new leads enter the ERP system, the time-consuming manual selection process usually begins. Which contacts are worth sticking with – and which should the sales department not pursue any further?
Generative AI can easily take over the monotonous pre-selection of warm and cold leads by analyzing their behavior very precisely, e.g:
- How often and for how long has a lead visited the company website?
- Have they downloaded marketing materials such as Whitepapers or case studies?
- Has the contact registered for a webinar?
In addition, GenAI compares current leads with similar cases from the past, thereby making the assessment of the chances of success more precise. A promising contact with several interactions and a suitable company profile automatically moves up the priority list. Less promising contacts, on the other hand, are initially marked for later actions.
3. relief from other routine activities
Summarizing meeting notes, suggesting follow-up emails or preparing appointments – many other recurring tasks also cost your sales team a lot of time in their day-to-day work. Time that would be better invested in active customer care.
GenAI in the ERP system automates repetitive tasks, giving sales staff more time for personal discussions with customers and individual advice. This helps to build long-term customer relationships.
Karl Maresch, Asseco Solutions
GenAI transforms the ERP system from a pure system of record to a system of action that proactively supports employees
Generative AI in merchandise management
1. optimization of order quantities and times
Timing is everything – this also applies to merchandise management. Companies can only fulfill their customer orders on time if the required material is always in stock at the right time and in the right quantity. A GenAI-supported ERP system also actively supports you with this challenge:
- Analysis: First, the AI analyzes historical stock and sales data, seasonal fluctuations, current order situations, external economic data and much more.
- Pattern recognition: It recognizes patterns in the data that indicate certain trends or fluctuations in demand.
- Quantity calculation: Instead of applying rigid scheduling rules, the AI ultimately calculates dynamically when and in what quantity parts are to be ordered.
If, for example, sales of a product increase unexpectedly and a supplier’s delivery times are extended at the same time, the system suggests an adjustment to the order quantity in good time. So instead of reactive reordering, the system uses predictive control.
2. reduction of inventories and capital commitment
While too much stock ties up capital, too little jeopardizes the ability to deliver. A balancing act that most companies have to master anew every day. GenAI in ERP helps you to balance this area of conflict:
- Identification of excess stock: By evaluating inventory turnover rates, consumption data and safety stock, the AI recognizes excess stock.
- Identification of understocks: At the same time, the technology records materials and products that are not available on time or in insufficient quantities.
- Making recommendations: GenAI suggests targeted measures – such as reduced or increased repeat orders or sales promotions.
3. automation of recurring orders
Many orders always follow the same patterns – especially when it comes to consumables and C-parts. GenAI recognizes these routines and can automatically generate order proposals in the ERP system or trigger orders independently if required. The AI also takes current requirements and price trends into account. As a result, the reduction in manual ordering processes significantly reduces the workload on the purchasing department, allowing it to focus more on strategic issues.
Generative AI in service
1. automatic processing of customer inquiries
In customer service, inquiries are often received via a wide variety of channels, e.g. email, contact form, chat or customer portal. GenAI can immediately evaluate these incoming messages and assign them in a structured manner based on their content:
- Ticket creation: Depending on the type of message (fault report, query about an invoice, technical problem with a product, etc.), the AI creates a ticket in the ERP system.
- Adding information: The technology also adds relevant data such as customer number or affected items.
- Prioritization and delivery: It then prioritizes the request according to urgency and forwards the ticket directly to the relevant department.
The advantage: manual sorting work is now a thing of the past. Just like incorrectly assigned tickets and long response times.
2. proposed solutions from verified knowledge sources
In parallel, it is possible for GenAI to access a verified knowledge database. This contains, for example, information from:
- documented service cases
- technical manuals
- internal solution guides
- Step-by-step instructions for troubleshooting
Based on the specific problem, the AI determines suitable solution proposals and makes them available to employees in a clearly structured format. For common standard queries, the system can even send the answer to the customer automatically. The result:
- Significantly faster processing times
- Higher first-time redemption rates
- an outstanding service experience
- more satisfied customers
GenAI in ERP: what a successful AI strategy requires
We have seen: Generative AI brings many advantages for a wide range of business areas. However, before you decide on a GenAI-supported ERP system, you should also be aware of the challenges. If you are aware of these, you can prevent potential risks.
1. the ERP system must understand processes
A common misconception is that artificial intelligence only needs a big pile of business data to automate tasks. This is not the case. In order for GenAI to automate routine tasks, it also needs to know your company-specific processes in detail. Otherwise, it is highly likely that the technology will not perform tasks correctly.
Prozesskenntnisse kann generative KI natürlich nur dann erlangen, wenn das ERP-System selbst eine prozessbasierte Funktionalität unterstützt. Moderne Lösungen wie APplus beispielsweise bilden Prozesse in einer für die KI verständlichen Sprache digital ab. Hierfür wurde der sogenannte Flow Mode entwickelt, der auf einer BPMN-Engine (Business Process Model and Notation) basiert und Anwender*innen prozessorientiert Schritt für Schritt ans Ziel leitet.
2. the quality and consistency of the data must be right
In order for generative AI to correctly utilize the data in the ERP system for analyses and recommendations for action, it must first understand it. It must know exactly which information the ERP solution maps and according to which system. However, as the tables and columns of databases are often named cryptically, GenAI cannot simply interpret the information correctly.
A modern data catalog is therefore required that can be used to manage both structured and unstructured data and optimally prepare it for AI models. It not only provides information about the origin and responsibilities of individual data records. It also ensures that no outdated or inferior data flows into the evaluation process. This leads to high-quality answers and precise forecasts.
3. data security must be guaranteed
Highly sensitive company data is managed in an ERP system – from financial figures and customer data to personal information. The use of GenAI in ERP increases the risk of this data falling into the wrong hands. This happens, for example, when:
- Companies use public AI models that use business data for training
- Employees use unauthorized AI tools (so-called shadow AI), resulting in an uncontrolled outflow of data
- GenAI is not strictly integrated into the rights management of the ERP system and unauthorized users gain access to evaluations
- Attackers enter malicious commands, causing the AI to reveal confidential information
Our tip: Use European data centers for cloud-based AI tools. These are subject to strict EU data protection regulations and ensure that no information is transferred uncontrolled to third countries.
4. employees need AI skills
Employees and managers need to know how to interpret and classify the results of generative AI. After all, artificial intelligence can also make mistakes and give incorrect recommendations. Training on the correct handling of GenAI is therefore essential for the responsible use of the technology.
In addition, new professions are emerging that focus on the development and control of artificial intelligence. These include prompt engineers as well as context engineers and AI managers. Overall, professions that require strategic thinking are gaining in importance. Jobs involving repetitive activities, on the other hand, are becoming less relevant.
5. lack of trust can hinder progress
Even the most powerful technology has no added value if it is viewed with skepticism by users. A lack of trust in GenAI is often reflected in the following behaviors:
- Employees bypass AI-supported functions
- Results are fundamentally questioned
- Decisions take longer because every detail is checked manually
There are many reasons for this: concerns about data security, fear of errors or uncertainty about how to use the technology. Companies should therefore pay attention to transparent processes and regular training. Expanding employees’ skills and communicating changes openly creates acceptance – and thus the basis for real technological progress.
Conclusion: GenAI in ERP gives you a real competitive advantage
Generative AI in the ERP system is already delivering so much added value in a wide range of business areas that it represents a real competitive factor. If you want to continue to operate as efficiently as the competition in a few years’ time, you cannot ignore technological progress. The use of GenAI in ERP is practically a must in order to remain fit for the future.
But using AI simply for the sake of AI makes no sense. Instead, you need to define clear use cases that will really move your company forward with GenAI. Furthermore, AI should never be seen as a single project or tool, but as an overarching instrument to support your entire corporate strategy. To manage this correctly, your entire workforce needs comprehensive skills in dealing with artificial intelligence.om administrative service provider to strategic partner for company management.
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