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<\/figure>\n\n\n\nAI or Automation: What Is the Difference?<\/h2>\n\n\n\n
Automation has been around much longer than AI.<\/p>\n\n\n\n
ERP systems execute processes based on predefined rules every day. When a particular event occurs, an action follows automatically.<\/p>\n\n\n\n
Think of sending a confirmation, starting an approval process, or flagging a contract that is about to expire.<\/p>\n\n\n\n
The rules are known in advance. The system does not need to interpret what should happen.<\/p>\n\n\n\n
AI works differently.<\/p>\n\n\n\n
AI can help interpret information, identify relationships, summarize text, and make suggestions based on available data.<\/p>\n\n\n\n
This becomes valuable when a process cannot be fully captured by fixed rules.<\/p>\n\n\n\n
For example, a service request in which a customer describes a malfunction in their own words requires a different approach from an automatically generated maintenance reminder.<\/p>\n\n\n\n
In the first case, information needs to be interpreted. In the second, the conditions are already clear.<\/p>\n\n\n\n
Both processes can be made smarter, but not necessarily with the same technology.<\/p>\n\n\n\n
When Is Traditional Process Automation Enough?<\/h2>\n\n\n\n
Many tasks within equipment companies are predictable.<\/p>\n\n\n\n
A rental contract has an end date. A maintenance interval may be based on a date or meter reading. A sales order follows certain administrative steps. An invoice can be submitted for approval when predefined conditions are met.<\/p>\n\n\n\n
For these processes, traditional automation is often a logical choice.<\/p>\n\n\n\n
With Microsoft Power Automate<\/a> can be used to configure workflows that respond to events in connected systems.<\/p>\n\n\n\n For example:<\/p>\n\n\n\n These are examples of processes in which the required action can be determined in advance.<\/p>\n\n\n\n The benefits are clear: fewer manual steps, consistent execution, and less dependence on individual employees.<\/p>\n\n\n\n AI does not automatically add value here. When a process can be executed entirely through reliable rules, a fixed workflow is often easier to manage and control.<\/p>\n\n\n\n The situation changes when information is not immediately available in a structured format or when multiple factors need to be assessed.<\/p>\n\n\n\n Consider a service request.<\/p>\n\n\n\n A customer reports that a machine regularly loses power during operation. The description contains no standardized fault code, and the cause is still unknown.<\/p>\n\n\n\n A fixed workflow can register the request and forward it to the service department, but that does not yet establish which information is relevant to preparing the service work.<\/p>\n\n\n\n AI may help by interpreting the request and retrieving relevant information from previous work orders.<\/p>\n\n\n\n Have similar malfunctions occurred before? What work was performed? Which parts were used? And what information could help the service employee prepare?<\/p>\n\n\n\n The value here lies not in automatically performing a predefined action, but in making relevant context available more quickly.<\/p>\n\n\n\n That does not mean AI should diagnose the problem independently.<\/p>\n\n\n\n A similar malfunction does not necessarily have the same cause. Assessment by an experienced service employee or technician remains important.<\/p>\n\n\n\n AI supports preparation. The employee decides what actually needs to happen.<\/p>\n\n\n\n The development of AI agents introduces another possibility.<\/p>\n\n\n\n Within predefined boundaries and depending on the available functionality, an AI agent can collect information, prepare process steps, and carry out certain tasks.<\/p>\n\n\n\n The main difference from a traditional workflow is how information is processed.<\/p>\n\n\n\n A workflow follows predefined rules. An AI agent can also interpret information and use it to determine which permitted next step is appropriate.<\/p>\n\n\n\n Microsoft is developing these capabilities within Dynamics 365 Business Central. Dynamics 365 Business Central<\/a>.<\/p>\n\n\n\n The Sales Order Agent is one example. It is designed to support the sales process by processing incoming customer requests and preparing sales documents.<\/p>\n\n\n\n The Payables Agent also illustrates how AI can be used to process vendor invoices.<\/p>\n\n\n\n These are examples of standard AI applications within the Microsoft platform. For equipment companies, there is also room for industry-specific applications.<\/p>\n\n\n\n Within Dysel\u2019s Equipment Life Cycle (ELC)<\/a> work is underway on the Service Impact Agent. This agent is intended to support service employees in assessing malfunction reports. By examining information from previous work orders, it can bring together relevant service history, possible approaches to resolving the issue, and information about parts used.<\/p>\n\n\n\n The goal is to give a service employee faster access to the right information when preparing a work order. Not because the agent takes over every decision, but because relevant knowledge from previous work becomes more accessible.<\/p>\n\n\n\n In practice, an organization does not have to choose between automation and AI. The two can complement each other.<\/p>\n\n\n\n For example, a service request can be registered automatically, after which AI helps retrieve relevant information from previous work orders. The service employee reviews that information and determines the next steps, which can then be processed through a fixed workflow.<\/p>\n\n\n\n This creates a combination of predefined process steps, intelligent support, and human judgment.<\/p>\n\n\n\n The strength lies not in any one technology, but in how these elements work together.<\/p>\n\n\n\n For that to happen, processes must be well connected. When service data, parts information, contract terms, and scheduling are spread across different systems, it becomes harder for automation and AI to work together reliably.<\/p>\n\n\n\n An AI agent can provide useful support only when the information it uses is sufficiently reliable and relevant.<\/p>\n\n\n\n For equipment companies, this is an important requirement.<\/p>\n\n\n\n A machine has more than an item number or serial number. Throughout its lifecycle, information is generated about purchasing, configuration, sales, rental, maintenance, repairs, parts consumption, contracts, costs, and revenue.<\/p>\n\n\n\n This information is interconnected. If AI can access only part of it, the context needed to make a useful recommendation may be missing.<\/p>\n\n\n\n That is why intelligent automation does not begin with selecting an AI tool.<\/p>\n\n\n\n It begins with how business processes and information are organized.<\/p>\n\n\n\n Dysel's Equipment Life Cycle (ELC)<\/a>built on Microsoft Dynamics 365 Business Central<\/a>helps equipment companies connect operational and financial processes throughout the lifecycle of their equipment.<\/p>\n\n\n\n\n
When Does AI Become Interesting?<\/h2>\n\n\n\n
AI Agents: When Support Becomes Execution<\/h2>\n\n\n\n
The Combination Is Often More Valuable Than Either Technology Alone<\/h2>\n\n\n\n
Why the ERP Foundation Still Matters<\/h2>\n\n\n\n