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Generative AI ERP Systems: 10 Use Cases & Benefits in 2024

Cem Dilmegani
Updated on Jan 2
4 min read

Enterprise resource planning (ERP) software is integral for streamlined business processes and improved business outcomes. For a more advanced business process, businesses migrate to cloud ERP, seeking more dynamic, adaptable solutions. 

With other technologies like RPA, the power of generative AI has a potential to improve the ERP process. This transformative technology offers a resolution by enhancing ERP capabilities, predicting future scenarios, and customizing experiences, promising a revolution in how organizations leverage their ERP systems. In fact, Microsoft already promises generative AI ERP solutions in its Dynamics 365 Copilot tool, which will be followed by others.1

In this article, we will explain what generative AI ERP systems offer to businesses.

What are the challenges faced in ERP technologies?

Customization vs. standardization

ERP software often needs customization to cater to specific organizational needs. However, excessive customization can lead to issues with updates, upgrades, and support.

Data accuracy and quality

The efficiency of an ERP system is contingent upon the accuracy of data input. Inaccuracies can lead to flawed insights and decisions.

Scalability issues

As organizations grow, their ERP systems need to scale accordingly. Some ERP solutions might not handle rapid growth efficiently.

Training and user adoption

Employees need training to use the ERP system efficiently. The complexity of some ERP systems can result in a steep learning curve.

Data security and compliance

Ensuring the ERP system adheres to data protection regulations (like GDPR) and is protected from cyber threats is crucial.

What are the use cases of generative AI ERP systems?

Generative AI, particularly models and techniques that can generate new content or data based on patterns they’ve learned, has a lot of potential for enhancing ERP systems. Here are some potential use cases of generative AI within ERP systems:

1- Data augmentation and enhancement

Generative AI tools are increasingly evolving in data analysis skills. For example, ChatGPT has a new Code Interpreter plugin for data analysis and visualization. In general, generative AI tools are advanced in analyzing vast amounts of data. Specifically, they can contribute to ERP data analysis and protection by:

  • Synthetic data generation: Filling in gaps or creating synthetic datasets from actual business data and customer data for improved analytics, especially when actual data might be scarce or sensitive.
  • Data cleaning: Predicting and correcting data entry errors based on patterns in the data.

2- Demand forecasting

Generative AI models can predict product or service demands by generating potential future scenarios based on historical data and market trends.

3- Predictive maintenance

Using generative models to anticipate when parts or equipment may fail by simulating various operational conditions can enable the prediction of potential problems that can occur in business processes beforehand.

4- Scenario planning & simulation

Generative AI models are competent for creating different scenarios given the correct prompt and context. By using its potential for scenario planning and simulation, businesses can create “what if” scenarios for business strategy planning so that they can anticipate potential challenges or opportunities.

5- Customization and personalization

  • It can be used to generate customized user interfaces or experiences based on individual user behavior, roles, or preferences within the ERP system.
  • Generative AI can also be used in marketing and sales operations for improving customer experience, such as personalizing content for specific target audiences.

6- Automated report generation

ERP also includes the preparation and planning of massive amounts of reports from different business operations. Creating detailed, coherent, and customized reports for different departments, stakeholders, or purposes without human intervention is an important contribution generative AI can bring into the ERP.

You should check our ChatGPT in audit and intelligent automation in audit articles for more on report generation automation.

7- Enhanced user assistance

Using natural language processing (NLP) abilities of the generative AI technology to produce contextually relevant help content, troubleshooting guides, or workflow suggestions for users is another important use case. By understanding natural language queries of users, AI chatbots and voice assistants are especially promising generative AI technologies for simplifying user interactions within ERP systems.

Also, generative AI technology has vast potentials for education and educational processes across different areas. It can also be used for educational purposes in teaching employees certain ERP systems.

8- Financial planning

An important element in ERP is financial planning. Advanced generative AI models are capable of generating potential financial models or projections based on varying business conditions or strategies, which can be a good contribution to enterprise financial planning. Also, it can be used for improving fraud detection capabilities.

For more on these, check our article on the use of generative AI in finance.

9- Supply chain optimization

Simulating different supply chain scenarios to find optimal routes, inventory levels, or supplier interactions are some of the things generative AI can achieve in supply chain. 

Check our article on generative AI in supply chain management for learning more about this and other supply chain related use cases.

10- Product design and development

In manufacturing modules, generative AI could aid in generating new product designs based on specified criteria or customer feedback. 

Check our article on generative AI in manufacturing to learn more about this.

What are the benefits of integrating generative AI into ERP systems?

  1. Enhanced data analytics: Generative AI, by producing synthetic datasets that augment existing data, enable better testing, modeling, and insights, especially when real data might be sparse or confidential.
  2. Improved decision-making: By simulating various business scenarios, generative AI offers insights into potential outcomes, assisting leaders in making more informed and proactive decisions.
  3. Improved operational efficiency through intelligent automation: Tasks like content generation, report creation, or predictive analysis can be automated with generative AI, reducing manual effort and the potential for human error.
  4. Personalization: Generative AI can customize interfaces, recommendations, or content to individual users or departments, leading to a more tailored and efficient user experience in the ERP system.
  5. Better demand forecasting: Generative models, by accurately predicting product or service demands by generating potential future scenarios based on historical data and market trends, ensures optimized inventory management and resource allocation.

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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