The Role of AI In ERP & How It’s Empowering Accurate Decision-Making

ERP Software / February 2024

The integration of Artificial Intelligence (AI) in Enterprise Resource Planning (ERP) Software is widespread, with 65% of CIOs integrating AI into ERP systems in 2022.

As of 2024, one in six UK businesses has adopted artificial intelligence technology, with most citing ‘data management and analysis’ as the primary reason. This makes the combination of AI and ERP fitting and somewhat limitless.

The use of AI in ERP has the potential to enhance decision-making through progressive learning and automation. The powerful combination has proven to:

  • Improve productivity
  • Increase accurate reporting
  • Provide in-depth predictive analysis
  • Reducing operational costs

Nonetheless, there are limitations to what AI can do in a business environment. Like meeting complex requirements and formulating strategic approaches. There’s also the feeling of unease about AI adoption in general. The main concerns being job displacement (39%) and loss of human skills (42%).

Why Business Leaders Desire an AI-enabled ERP System

Artificial intelligence has gone beyond just being a buzzword. It is considered an essential ERP capability, with 53% of UK businesses actively seeking more intelligent ERPs.

There are four key reasons why businesses seek out AI integration in ERP systems:

  1. AI-Powered Automation: Automation is the driving factor behind 42% of AI adoption among businesses. With ERP systems, AI provides the ability to automate workflows and functions based on data. This helps to reduce labour costs and increase the accuracy of decision-making.
  2. Predictive analysis: AI plays a vital role in predictive analysis by learning, understanding, and finding patterns to forecast outcomes. Alongside deep learning, predictive analysis is listed as the most critical ERP technology among CIOs.
  3. Competitive Pressure: Competitive pressure is the reason for 31% of AI adoption among IT professionals. Businesses want to ensure they’re not left behind in the race for AI-powered ERP capabilities.
  4. Future-Proof Operations: As well as future-proofing against competitors, AI learns how operations are carried out. It can then identify opportunities for greater success in future business functions.

Find the Best ERP Software That Matches Your AI-Specific Requirements

Get Started

What Are Your Key ERP Requirements?

ERP Modules & Functions That Benefit Most From Artificial Intelligence

Human Resources Management

According to 69% of IT developers, ERP Human Resource (HR) modules are one key business process predicted to be entirely replaced by AI. The 3 key driving forces behind the adoption of AI for core HR tasks are improved efficiency, better employee experience, and reduced costs.

Uses of AI in ERP HR Modules

81% of HR leaders have already experimented with artificial intelligence, with HR departments embracing AI in major functions:

  • Employee records management (78%)
  • Payroll (77%)
  • Recruitment (73%)
  • Performance management (72%)
  • Onboarding (69%)

76% of CHROs believe their department will be lagging behind others if it does not adopt generative AI tools in the next 12-24 months.

Customer Support

It is estimated that 95% of customer interactions will be powered by AI by 2025. The use of generative AI – such as chatbots – for customer experience (CX) has proven to increase resolution times and improve CSAT scores.

Businesses embracing AI in ERP systems have the benefit of improving customer interactions and CX through the use of Natural Learning Processing (NLP). NLP has played a significant role in enabling the use of chatbots and text prediction, which has – and will continue to – heavily influenced customer-facing factors:

  • Chat-based customer support (57%)
  • Email communications with customers (53%)
  • Search functionality (45%)
  • Voice-based interactions (42%)

ERP Finance & Accounting

The driving force behind AI in ERP finance modules is automation. Essentially, all financial processes are manual; data entry, reporting, data collection, and verification. Making the entire process time-consuming, costly, and prone to errors.

An Oracle finance survey highlights, “incorporating AI into ERP systems can yield a 36% drop in errors and reduce the time it takes to close the books by 3.5 days.”

Integrated with data from other functions and departments – and collected in one ERP system, AI understands talent gaps, predicts salary costs, and identifies the most profitable areas of a business.

Supply Chain & Manufacturing

AI is heavily utilised in Manufacturing ERP tools, with an emphasis on optimising supply chain processes. AI functions allow for greater predictive planning, analysis, and even maintenance.

While striving for greater warehouse efficiency and better on-time delivery, AI technology is drastically improving the accuracy of inventory management and tracking. The use of AI-enabled cameras and sensors automates stock control, which has improved production planning and prevented overstocking.

3 Examples of Successful AI ERP Implementations

1. Transformative Retail AI: Walmart & SAP

Mike Hanrahan, CEO of Intelligent Retail Lab by Walmart, claims “AI is going to be as transformative to retail as e-commerce was”.

Walmart was an early adopter of SAP’s cloud ERP system. This allowed the multinational retail corporation to integrate AI technology with its data-collection system. The goal was for “better inventory [management], having the products [customers] want on the shelves, and having cleaner, safer stores”.

As a test, Walmart first infused AI technology across its New York store. This included AI-enabled cameras and sensors that generated 1.6TB of data every second.

These cameras worked with an ERP system to understand what products were selling, what products needed replenishing, and how long food products had been sitting on the shelves.

2. Mitsubishi Electric and Oracle Cloud

Positioned at the top end of industrial automation, Mitsubishi Electric implemented the Oracle Cloud ERP system to help with its just-in-time manufacturing practice. With up to 10,000 work orders a day, they needed a system to help bridge AI and process automation.

Once deployed, Mitsubishi Electric saw several gains in its industrial processes:

  • Equipment uptime increased by 60%
  • Production increased by 30%
  • Manual processes were reduced by 55%
  • Floor space reduced by 85%

3. Award-Winning Mobile App: TD Bank Group (TD) & Microsoft

TD partnered with Microsft and implemented Microsoft Azure ERP. The system’s AI capabilities allowed TD to harness the potential of their AI subsidiary, Layer 6.

The integration of AI has enabled TD to focus on improving CX through its banking app. Ultimately, this led TD to receive an award for innovation in artificial intelligence.

Top Risks Posed By Implementing AI In Business Functions

Beyond the hype and excitement of potential uses, there are risks to consider when integrating AI with ERP.

34% of developers believe the EU’s security regulations to be a concern for dealing with AI and machine learning applications. At the same time, 37% believe migrating data from legacy systems to be the top barrier to AI adoption. On top of this, there are more upfront challenges that businesses face:

Data Privacy & Security

Using AI in ERP tools will require a huge amount of processing data. This opens up an increased risk of data hacking from smart attackers, or cybercriminals. Once all business data can be accessed on a singular system, it will need to be properly secured and encrypted.

Quality of Data

It’s important for leaders not to be overawed and relinquish complete control. After all, the technology is only as good as the data that is put into the system. If the data is incomplete or contains errors, then the decision-making of an AI-powered ERP will produce incorrect results.

Expertise & Training

The use of AI in ERP systems is complex and requires user training or a dedicated team of experts. It’s not a case of feeding the system data and leaving it to run by itself. Instead, AI applications need to be routinely assessed and maintained. The large pool of processing data needed for an ERP system to function also needs to be regularly monitored by experts.

Hidden Costs

Beyond implementation and the integration of AI-powered applications, there are further costs:

  • Hardware (such as sensors, cameras, and IoT devices)
  • Additional modules and applications with AI functionality
  • Hiring expert personnel

Small and mid-sized business leaders need to understand these extra costs, which could act as a deterrent when budgeting for an AI ERP solution.

Use Our ERP Software Finder to Identify the Best Tool For Your Business Processes

What Are Your Key ERP Requirements?

AI capabilities are already deeply ingrained in ERP products. But, there are areas – or trends – for further progression:

  • Hyperautomation: ML or AI-powered ERPs are a significant driving force behind hyperautomation capabilities. They provide the function to automate nearly all business processes that help to reduce human involvement and eliminate human error.
  • Augmented Reality and Virtual Reality: AI-constructed data sets are at the centre of how businesses will use AR and VR in the workplace. Through digital overlays, business leaders can see predictive analysis, real-time reporting, and in-depth data insights.
  • Explainable Artificial Intelligence (XAI): XAI is a method used to understand how an AI model uses data and generates algorithms to come to a final decision. This is used to help humans see the processes the model took. Essentially building trust and confidence for initiating AI models in business operations.


What Are the Best AI ERP Systems Available Today?

Although highlighting the best AI ERP systems is subjective and dependent on several factors (including requirements, industry, and budget), several ERP Software providers already excel in AI functionality:

  • Oracle ERP Cloud
  • Microsoft Dynamics 365
  • Oracle NetSuite
  • Epicor
  • Acumatica

What Are the Different Types of AI Used in ERP Systems?

  • Machine Learning (ML): Used for predictive analytics and detecting anomalies through algorithms that use data to predict future trends and help with overall decision-making
  • Natural Language Processing (NLP): Used to understand unstructured text and extract in-depth analysis. Also used to improve customer experiences through Chatbots and customer interaction.
  • Generative AI: A subset of AI, this uses algorithms and frameworks to generate new content or data that is similar to its training data but devoid of plagiarism (think ChatGPT 3.5). Using generative AI in ERP systems has the potential to forecast trends and predict decision-making processes as accurately as possible.