As more ERP vendors, particularly the top ERP vendors, incorporate artificial intelligence (AI) into their offterings, it’s important to understand the basic principles behind this dazzling technology.
Here’s a beginner’s guide that outlines the types of AI, the types of data sources, the risks, and some use cases.
The Role of AI in Business
Increasingly, businesses are using AI, including generative AI, to gain efficiencies and sharpen their decision-making.
One way that companies are accomplishing this is by incorporating AI into their existing ERP, CRM, or SCM systems. Another common approach is purchasing enterprise software with built-in AI capabilities.
AI in ERP can provide intelligent insights that help business leaders make decisions, such as what suppliers to engage or even what products to develop.
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The Different Types of AI
One type of AI that existed before the birth of ChatGPT is machine learning-based AI. This AI can make predictions, automate decisions, identify anomalies, and streamline processes.
For example, intelligent process automation is the practice of using machine learning algorithms, robotic process automation, and other automation technologies to streamline repetitive tasks.
Generative AI is most commonly used for software development or the development of any output that can be produced from analyzing large amounts of data. This not only includes structured data but also unstructured data, such as procurement documents or maintenance manuals.
What Types of AI are in ERP Software?
ERP vendors are incorporating these and other types of AI into their offerings.
For example, many ERP systems have natural language processing capabilities that enable the system to understand user queries and provide a conversational output.
This chatbot functionality is one of the most common uses of generative AI in ERP software. Chatbots and other digital assistants improve the user experience and boost productivity by allowing non-technical users to query for specific information within the ERP system or ask for a summary of data from multiple sources.
The Different Types of Data Sources
While there are many types of data that organizations can use to train AI, here are a few of the main ones:
One example of public data is open data. This is data that is freely available to the public, such as scientific research and social media content.
Another example is web data, which includes the vast amount of non-subscription-based data on the internet, such as text, images, and videos. Some AI applications within ERP systems connect to the internet. Others do not.
Generative AI systems may not always have access to all of the public data they access now when answering a user query within an ERP system. This is where third-party data comes in.
For years, organizations have recognized the value of combining their proprietary data with third-party data to drive decision making. However, these partnerships will become even more important as the New York Times’ lawsuit against OpenAI unfolds.
Internal data might include historical data on the company’s financial performance, sensor data from the company’s manufacturing plant, or user-generated data from customer support interactions.
While organizations can use this data to train their AI, it’s far from a plug-and-play proposition. Most companies have some amount of siloed or inaccurate data within their organization, and this data must be cleansed and standardized before becoming AI training data.
The Risks of Using AI for Business
These are just a few of the risks that come with using AI for business purposes:
Regardless of the types and quality of data an organization uses to train its AI, unreliable outputs are still possible. In fact, AI hallucinations are quite common. Quality assurance testing and safety measures are essential.
Organizations must assess data for bias. Biased outputs can be much more difficult to identify when coming from an AI system. AI systems compute answers without necessarily sharing their reasoning or data sources.
As vendors continue to fine-tune the AI/ERP integration, some are adding an additional layer to the baseline function of the ERP platform. This way, employees can take full advantage of the historical data that’s already in their system.
However, any technology with access to proprietary business data comes with security risks.
In particular, features such as predictive analysis and predictive text have raised concerns. Most notably, organizations have wondered whether the AI is learning from their inputs and using this to generate information for competitors.
Until AI data storage policies are precisely defined, companies must take extra precautions. This means establishing AI governance and data governance policies to ensure confidential information isn’t inadvertently shared.
Ultimately, human intelligence is still required to manage data, manage AI applications, and make the final call on financial and operational decisions.
The Top 10 Use Cases of AI in ERP
1. Analyzing ERP financial reports to identify irregularities in data entries or unusual transactions.
2. Anticipating equipment failures and recommend preventative maintenance schedules, proactive repair procedures, and more.
3. Automating time-consuming tasks, like invoice processing and purchase order approvals.
4. Optimizing supply chain management by analyzing real-time data to predict demand fluctuations, optimizing inventory levels, and recommending transportation routes.
5. Improving talent management and retention by analyzing employee data to identify high performers, recommend training opportunities, and predict potential churn.
6. Ensuring compliance with regulations and reporting requirements by automating legal processes and ensuring accurate and timely reporting.
7. Improving product development and innovation by analyzing customer data and market trends to suggest new product ideas, optimize product features, and personalize marketing campaigns.
8. Summarizing data by analyzing various sources within the ERP system and generating reports in digestible formats that can be easily presented.
9. Enhancing customer service using intelligent chatbots that understand the context of conversations, learn from user interactions, and generate personalized, human-like responses.
10. Augmenting financial forecasting by generating plausible financial scenarios based on potential economic, policy, or market changes.
Your Next Project: Implementing an AI-Enabled ERP System?
By integrating AI into ERP software, companies can improve their business operations across various functions, including accounting, human resources, customer service, supply chain management, and more.
The role of AI in ERP is still being defined. However, we do know that this technology is transforming ERP for the better (for the most part).
Have you thought about how new technologies could expand the functionality of your current systems? Our enterprise software consultants can assess your current technology and determine if new integrations are possible or if a full replacement is necessary. Contact us below for an ERP consultation.