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Top 4 Use Cases & Case Studies of ERP AI in 2024

Updated on May 16
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Top 4 Use Cases & Case Studies of ERP AI in 2024Top 4 Use Cases & Case Studies of ERP AI in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

Enterprise resource planning (ERP) systems help organizations manage and connect daily business processes in various fields such as finance (financial analysis, procurement, accounting), operations (supply chain management and planning, inventory management), and human resource management (workforce planning and management). 

The market value of ERP is expected to reach ~$52bn in 2024.1 More the ERP systems continues to cover more complicated business processes, more the conventional ERP become insufficient. Artificial Intelligence (AI) capabilities offered for ERP solutions can help businesses streamline complicated ERP processes with applications such as ML models and conversational AI systems. As business leaders realize that, they invest more in AI-enabled ERP applications (see Figure 1).

Figure 1. Global AI for enterprise applications market from 2016 to 2025

This article explores top use cases and case studies of AI technology in ERP to better prepare business leaders for future investments in AI.

Use Cases

1. Finance & accounting

AI in financial management can

  • automate repetitive accounting functions
  • increase the efficiency of transaction-processing
  • verify accuracy of statements and reports

For more, feel free to read our research on AI in finance.

Most ERPs offrt tools for financial management. However, the use of AI with native integrations can increase the capabilities of ERPs in areas such as document management & accounts payable process. For a detailed analysis of the increasing capabilities of ERPs in financial management and accounts payable process:

2. Advanced analytics & forecasting

Most operations activities such as supply chain management and production can benefit from accurate predictions. AI models can improve predictions using historical data and current conditions. More specific applications exist in:

  • Production: Better managing seasonality to avoid underproduction or overproduction.
  • Warehouse Management: Better demand forecasting and being more prepared to supply chain disruptions.
  • Sales: More granular analysis of sales can provide better forecasts which translate to better targets, improving employee performance.

Watch how Samsung is using AI for better demand forecasting

3. Human resources

Most ERP systems provide basic HR functionality. However, increased analytical capabilities can help companies improve most HR tasks such as performance management, compensation management, and recruitment.

For more, see AI in HR.

See how AI is used for automating recruitment:

4. Customer service

Integrating AI to ERP can allow quicker, cost-effective, and consistent service. A widely cited benefit is chatbots that are used instantly to answer customers’ common inquiries. Thus customer service representatives can handle more complex customer queries.

For more, feel free to read our research on AI in customer service.

Watch how Vodafone leverages AI to offer intelligent customer service

Case studies

1. AmerisourceBergen

As a drug wholesale company based in the US, AmerisourceBergen had previously used spreadsheets to pull in data from various systems to determine production costs.2 After pulling data, historical information and know-how of the employees were also considered to figure out how sensitive customers were to price changes.

They then moved to an integrated system that automatically calculates production costs, analyzes historical transaction data, and pulls in outside data such as weather forecasts to create a foundational layer for future deployment of artificial intelligence.

With the old manual system, pricing team members needed to spend 3 hours on more complex price analysis and 5 hours on the more routine tasks involved with price administration. With smart automation, they are able to spend just 1 hour on price administration and the other 7 hours on value-added activities.

2. Mitsubishi Electric

After implementation of the AI and process automation in Oracle Cloud, Mitsubishi Electric claims to have achieved:3

• Uptime has increased 60%

• Production has increased by 30%

• Manual processes reduced by 55%

• Floor space has been reduced by 85%

3. Walmart

As an early adopter of HANA by SAP, Walmart claims to have been able to process its high volume of transaction records (the company operates more than 11,000 stores) within seconds.4

Watch how Walmart expands its use of AI in its stores

Select AI-enabled ERP systems in line with your daily operations

Machine learning capabilities are not the most important criteria in ERP selection. Companies should select ERP systems in line with how they will benefit them while running their daily operations. However, the below factors are important to ensure that the ERP system is future proof when it comes to machine learning:

  • Effective data management: Companies rarely have a chance to modernize their ERP systems since these are critical production systems that have been deeply integrated into the companies’ operations. So companies need to make sure that when they switch to a new ERP system, it is flexible enough to store and provide company data in granular detail, in line with its operations. As long as data is easy to access, companies could use the machine learning components of their ERP or other software to build machine learning models to solve their operational problems.
  • Ease of integration: No single company should be expected to be the company’s machine learning software provider since machine learning impacts every aspect of a company’s operations. An ideal ERP software should be easy to integrate for 3rd party providers.

Which vendors integrate AI & ERP?

With increased interest in AI, every major ERP vendor claims to have integrated AI capabilities in their offering. It is impossible to verify all of these claims, but some vendors claim specific improvements in their ERP solution thanks to machine learning:

  1. Infor Coleman: Provides conversational UX with chat, voice, and image recognition capabilities
  2. NetSuite Intelligent Cloud Suite claims better insights and greater efficiency through the integration of predictive analytics.
  3. SAP S/4HANA Cloud is a real-time enterprise resource planning suite built on an advanced in-memory platform. HANA enables companies to use machine learning algorithms on their data or build their own solutions with connectors to HANA. Predictive analytics, for example, HANA, can facilitate cost forecasting to reduce budget overruns and make more accurate resource investment decisions.
  4. Microsoft Dynamics AI provides virtual agents, sales insights, and customer service insights thanks to machine learning.
  5. SYSPRO claims that its ‘digital citizens’ AI-powered bots can integrate directly into the ERP to deal with repetitive tasks across the organization.
  6. Epicor EVA, AI-based voice command user interface for productivity improvement. It is claimed to streamline routine tasks like quoting and scheduling. It also provides data-driven recommendations (e.g., anomaly detection in manufacturing)

You can also check out our list of AI tools and services:

For any questions on ERP and AI, feel free to reach out to us:

Find the Right Vendors
Cem Dilmegani
Principal Analyst

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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