4 Common Technologies of AI in ERP and Best Practices to Begin

Adopting AI in ERP is no longer a future execution, but a ‘NOW’ action.

Even though solutions like on-demand chat are available, many powerful AI use cases haven't been integrated into ERP systems yet. However, AI adoption has become beneficial because it enables businesses to drive revenue growth and reduce costs (McKinsey & Company). This is why integrating AI into ERP systems will soon become essential in the ERP market.

In this briefing, you will understand the 4 most common AI technologies in ERP and explore the 5 fundamental practices to get started.

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Understanding AI in ERP: 4 Most Common Technologies

1. A simple definition of AI (Artificial Intelligence)

Before going deeper into the ERP ecosystem, let's learn about the evolution of AI technologies and their impact on our daily lives.

AI - or Artificial Intelligence, refers to the capacity of machines to carry out certain cognitive functions recognised by our human minds. Some of the most popular AI technologies utilised in modern times can be named Siri on iPhone, ChatGPT, Gemini by Google, etc. Here is a short brief of the AI evolution:

2. Most adopted technologies of AI in ERP

If you are searching for AI adoption in ERP, you will find hundreds of resources. However, here we will focus on the 4 most relevant AI technologies for operating ERP systems in business management.

Generative AI: Content Creation Opportunities

As mentioned above, Generative AI is a technology that focuses on generating data and producing different data formats, such as images, videos, articles, emails, or even code. While still under development, Generative AI has been integrated into an ERP system to perform many different functions and act as an intelligent assistant. It can be used in 3 common ways:

• Automated Content Creation: It saves businesses a lot of time and money by generating reports, service descriptions, or business materials that are customised for particular audiences.

• Automated Report Generation: Generative AI can read and analyse business data in ERP, and help produce customised reports with valuable insights and visualisations.

Keep in mind that generative AI is not yet a one-click solution. It still requires human supervision and high-quality data to produce more precise results. However, as this technology matures, it has the potential to simplify the process of creating content and provide valuable insights from your ERP data.

AI Vision & OCR: Data Capture Automation

Optical Character Recognition (OCR) and AI Vision can revolutionise how you work with data in your ERP system. While OCR extracts text from physical documents, Vision AI analyses photos and videos using computer vision. Both technologies have high accuracy rates, your data input tasks will greatly benefit from their automation. How they may help your business is as 3 following ways:

• Automated Invoice Processing: OCR is not a new technology anymore. Many businesses are using this technology to extract invoice data from paper or digital files. Deloitte reported that companies can save up to 80% on processing expenses by using OCR invoice processing.

• Enhanced Inventory Management: Specific inventory control procedures can be streamlined with AI vision. These cases may cater to automating product tracking and identification inside warehouses.
• Improved Documents Extraction: OCR saves businesses a lot of time and increases data accuracy by capturing receipts, and extracting vendor bills or delivery notes.

Pattern Recognition: Smart Anomaly Detection

Pattern recognition is an essential AI feature that enables systems to detect reoccurring patterns and trends within a data set. It helps businesses to automatically detect inconsistencies and fraudulent activities like misconfigured product listings or a surge in incorrect bookings. This technology has a track record of successful uses in:

• Demand Forecasting: The technology can analyse your historical sales data and identify hidden trends. Then from the generated information, it forecasts the potential demand for your specific products.
• Fraud Detection: It can also analyse business financial transactions within your ERP and spot irregularities or potential fraudulent behaviours.
• Predictive Maintenance: Pattern recognition can predict possible maintenance requirements by evaluating sensor data from equipment inside your ERP.

Optimisation & Prediction

These AI features are beyond data processing. They make use of machine learning algorithms to find trends, forecast future ones, and suggest the best action inside your ERP system. Even though forecast accuracy could vary based on market conditions and data quality, let’s look at 4 key examples of prediction and optimisation features in AI:

• Inventory Optimisation: These features help forecast changes in demand, optimise inventory levels, and recommend the best times to make purchases.
• Manufacturing Order Optimisation: It can also help you to optimise scheduling and resource allocation for production based on real-time data.

In a nutshell, there are endless AI-driven possibilities that can contribute to improving your business operations within an ERP system.

Read more: Breaking Down Cloud-Based ERP Systems and Its Impact on Business

3. Combing AI Technologies to Optimise ERP Functions

When we combine those AI technologies, you can imagine your ERP system can automatically predict your needs, automate repetitive tasks, and generate insights for smarter decisions. This is the transformative power of AI adoption in ERP, as it empowers businesses to optimise various business functions. Let's explore how AI elevates your business in five key areas:

5 Fundamental Practices Before Starting The Adoption

The AI integration process is more than just integrating a solution. To tap into the transformative power of AI in ERP, you need thorough planning. Let’s dive into 5 best practices to consider before adopting AI in your ERP:

Practice 1: Assess your current data landscape

Implementing AI on a solid ERP system is one of the keys to success. Before the adoption, assess your current data environment:

• System capability: Can your ERP handle AI integration? Think about the constraints and potential modifications required for optimal AI performance.
• Data quality: AI is driven by precise and clear data. Verify the accuracy of your ERP data to see if any entries are missing, inconsistent, or out-of-date. To guarantee reliable AI results, a data cleansing procedure may be required. According to Gartner, companies lose up to $15 million annually as a result of incorrect data, and it can get worse when generated by AI technologies.

Practice 2: Identify your AI goals and use cases

AI won't solve all of your ERP problems magically. You must set precise objectives for your business and pinpoint the particular areas where AI will have the greatest impact. Consider these questions:

• Which task will AI assist with? I.e., data entry and invoice processing.
• Where will AI enhance workflows and productivity? I.e., supply chain forecasting and inventory control.
• How will AI support more intelligent decision-making? I.e., churn prediction and predictive maintenance.

Practice 3: Prioritise user adoption and change management

The human factor plays an important role just like technology in AI adoption. This smart technology will get smarter in the future, and those who aren’t prepared to learn can get left behind and frustrated (Forbes). You need to create a thorough change management plan to guarantee user adoption and optimise AI's capabilities:

• Transparency & communication: Keep your team in the loop during the AI integration process. Explain how AI will affect their jobs and point out the benefits for both the company and the employees.
• Instruction & support: Give your employees the appropriate training to make use of the AI in the ERP. Provide ongoing support to guarantee that all members are at ease and confident utilising AI tools.

Practice 4: Choose the suitable AI solution and partner

There are several providers in the AI world and each provides a wide range of solutions. Carefully assess your demands to make sure the AI solution you choose will work smoothly with your current ERP system:

• Goal alignment: Will the AI solution solve your use cases and meet particular business goals that you previously identified?
• Provider expertise: Select a trustworthy AI provider with expertise in your sector and a track record of successful ERP integrations.
• Scalability: Think about how scalable the AI solution is. Can it expand and change to meet your changing demands as a business?

Practice 5: Security and ethical considerations

Based on the report results of McKinsey & Company, while getting the benefits of higher productivity from these AI technologies, you also need to take safety measures to reduce any possible risks. Keep in mind that the quality of AI output depends on the data it is trained on.

• Data security: To protect sensitive data that AI technologies use, put strong data security measures in place. You can verify compliance with pertinent data privacy laws.
• Anti-bias detection: AI systems may carry biases from the training data. To guarantee unbiased decision-making, build procedures for identifying and reducing bias in using AI.

Read more: Top 5 ERP Software For Small Businesses In Australia

AI in ERP: Started or Get Left Behind

Even though AI could still be in its early years, nearly every new software product will include AI technology in the future (Gartner). Adopting AI in ERP is already the trend emerging a few years ago. Have you checked the AI box in your digital transformation strategy? Talk with our certified ERP experts if you need help adopting AI solutions in your operations. We have over 7 years of experience providing ERP services, whether analysing your business processes, implementing and customising ERPs or ongoing maintenance support tailored to your needs.

References:

• McKinsey & Company (2023). The state of AI in 2023: Generative AI’s breakout year.
• Gartner (2018). How to Create a Business Case for Data Quality Improvement.
• Fortune (2023). A.I. might not replace you, but a person who uses A.I. could.
• McKinsey & Company (2018). AI, automation, and the future of work: Ten things to solve for.
• Gartner (2017). Gartner Says AI Technologies Will Be in Almost Every New Software Product by 2020. 
• McKinsey & Company (2024). What is AI (Artificial Intelligence)?