Artificial intelligence is currently one of the most widely discussed topics across industries. Whether on LinkedIn, Instagram, TikTok, or in the news, hardly a day goes by without new use cases, product announcements, or debates. The topic has long since reached businesses as well. At trade fairs, in industry publications, and in customer conversations, it often feels like there is little else being discussed.
Few people still question whether AI will change the way we work and live. The conversation has largely shifted to how quickly organizations need to act and which solutions make the most sense for their business. <\/p>\n
One observation keeps coming up in conversations about AI: although everyone is talking about it, they are often talking about very different things.
Some think of tools like ChatGPT or Microsoft Copilot and the ability to ask questions in natural language. Others focus on systems designed to analyze data and identify patterns. And then there are AI agents that can carry out tasks independently and support business processes with minimal human intervention.
All of these technologies are commonly grouped under the label of \u201cAI\u201d. In reality, however, they serve different purposes and solve different types of problems. <\/p>\n
In simple terms, most companies are currently encountering three main types of AI:<\/strong><\/p>\n Let\u2019s take a typical food wholesaler. The sales team notices that a long-standing customer has been placing significantly fewer orders over the past few months than they used to. The obvious question is: Why is revenue declining for this customer? At first glance, this might seem like a straightforward AI use case. In reality, however, the answer depends very much on which type of AI we are talking about. <\/p>\n Let\u2019s assume the relevant information is already available in Microsoft Dynamics 365 Business Central. Sales figures, orders, customer records, and product categories can all be accessed within the system. In this scenario, an AI assistant could support the user by answering questions about that data. For example, the Sales Manager might ask: <\/p>\n The AI assistant searches the available information and provides the relevant answers. Its role is to make knowledge easier to access and help users find information more efficiently. This category includes tools such as ChatGPT, Microsoft Copilot, and similar systems that answer questions, generate content, or provide information. In these scenarios, AI acts as an assistant, helping employees work more effectively in their day-to-day roles. <\/p>\n Another type of AI goes a step further. Instead of simply answering questions, it looks for patterns and relationships within the data. Imagine a company analyzing its sales figures. The data reveals that customers in a particular segment have been ordering significantly fewer products from a specific product category over the past few months. At the same time, sales of alternative products are growing at an above-average rate. In this case, the AI is not responding to a specific question. Instead, it highlights developments that may have gone unnoticed. These types of solutions are often referred to as analytical AI. Their purpose is to uncover patterns, relationships, and anomalies that are difficult to spot in large volumes of data. <\/p>\n This can be particularly valuable in food wholesale distribution. Seasonal fluctuations, changing prices, promotional campaigns, and shifts in purchasing behavior generate large amounts of data every day. Within that data are insights that can help businesses identify trends earlier and make better decisions. <\/p>\n AI agents work differently again. While an AI assistant provides information and analytical AI identifies patterns, agents can take action and carry out tasks. Returning to our example, an agent might detect that a key customer is ordering significantly less than usual. It could then automatically create a task for the responsible Sales Representative, gather the relevant information, and prepare an initial recommendation for next steps. Similar scenarios are possible in purchasing, where agents monitor supplier delays, collect relevant information, or trigger specific workflows automatically. The key difference is that agents do not just provide information. They can become part of day-to-day business processes and actively support how work gets done. This is one of the reasons why AI agents are receiving so much attention right now. The promise is not only better insights, but also less time spent on repetitive tasks. <\/p>\n At first glance, these differences may seem largely theoretical. In practice, however, they are highly relevant for businesses. Before discussing AI solutions, it is important to understand what kind of support is actually needed. <\/p>\n Depending on the objective, completely different technologies may be the right fit. That is also why many discussions about AI end up heading in the wrong direction. The conversation often focuses on tools before the actual goal has been clearly defined. <\/p>\n In my experience, this is where many AI discussions start in the wrong place. Companies often begin by asking questions such as: <\/p>\n The more important question comes first: What are we actually trying to achieve? Do we want to make better decisions? Make information easier to access? Reduce repetitive tasks? Gain clearer visibility into key business metrics? Or simplify day-to-day work for specific teams? Only once the objective is clear does it make sense to evaluate which solution will deliver the greatest value. <\/p>\n The most important question is often not, \u201cWhich AI should we use?\u201d but rather, \u201cWhat are we actually trying to improve?\u201d<\/p>\n Once that question has been answered, it often becomes much easier to decide which tools make sense \u2013 and whether AI is even part of the solution. Even with a clear objective, however, the path forward is not always as straightforward as current discussions around AI might suggest. Along the way, companies tend to encounter many of the same challenges and questions. And that is often where things become truly interesting in practice. More on that in the next article. <\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence is currently one of the most widely discussed topics in business. Yet the term \u201cAI\u201d covers a wide range of technologies, from AI assistants and analytical AI to autonomous agents. This article explains the differences, how each type of AI supports businesses in different ways, and why defining the business objective should come before choosing a specific AI solution. <\/p>\n","protected":false},"author":9,"featured_media":3815,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"footnotes":""},"categories":[222],"tags":[227,226,224,231,230,225,141,223,229,228],"class_list":["post-3824","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-ai-agent","tag-ai-assistants","tag-ai-in-business","tag-ai-use-cases-for-businesses","tag-ai-powered-data-analysis","tag-artificial-intelligence-for-small-and-mid-sized-businesses","tag-business-central-ki","tag-microsoft-copilot","tag-process-automation-with-ai","tag-using-chatgpt-in-business"],"yoast_head":"\n\n
An Example from Food Wholesale Distribution<\/h2>\n
When an AI Assistant Enters the Picture<\/h2>\n
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When AI Is Used to Detect Patterns<\/h2>\n
When AI Agents Take Action<\/h2>\n
Why the Distinction Matters<\/h2>\n
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The Real Question<\/h2>\n
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