So, let’s start with the difference between good programming and genuine artificial intelligence, as described by The Royal Institute.
- Traditional computing (also called regular or classical computing) runs on fixed rules. If you want it to do something, you have to give it clear instructions in advance. For example, setting up a B2B customer support system that can only respond to questions you’ve already thought of and programmed in. So, if a client asks about a late invoice, it’ll only help if someone has already written out exactly what to do in that situation. It follows the script—nothing more, nothing less.
- Artificial intelligence (AI) works differently. Instead of following a rigid set of rules, AI is designed to learn from data and work things out on its own. It can recognise patterns, adapt to new situations, and improve over time.
Continuing the customer support example: rather than manually writing out every possible customer question and answer, you train an AI system by feeding it thousands of real customer support interactions. Then, when a new request comes in, the AI looks at what it’s seen before and comes up with a smart response based on those past examples—even if it’s never seen that exact question before. The more it sees, the better it gets.
You can break down the differences between regular computing and AI along a few key criteria:
Ability to learn:
- Traditional computing doesn’t learn from new input. It’s designed to perform a fixed task in the same, consistent way. As mentioned in an earlier blog post, rule-based AI (like robotic process automation) are not self-learning and don’t adapt based on data or experience. They might be called ‘AI’, but don’t meet the stricter definition of true artificial intelligence. For example, a process in your CRM that sends a notification to sales when the close date on an open opportunity has passed is just simple ‘if-this-then-that’ logic.
- Artificial intelligence, by contrast, changes its output based on new input. A better comparison would be the lead intelligence in SAP Sales Cloud, which analyses all your past lead data to predict which ones are most likely to convert into opportunities. It continuously adjusts as more data is added and is tailored to your specific business.
Problem-solving:
- Traditional computing is excellent at solving problems humans can’t easily do themselves. Think of a calculator—not intelligent, but very useful. A more complex example is anomaly detection in SAP Asset Performance Management. This tool analyses huge volumes of sensor data from equipment, flags anything outside the norm, translates that into human-readable information, and sends an alert. While highly valuable, it still comes down to an advanced form of ‘if-this-then-that’.
- Artificial intelligence, however, can handle complex, ambiguous problems that traditional computing can’t. Take scheduling, for instance. The auto-scheduling features in SAP Field Service Management automatically assign and reassign tasks based on technician skills, location, and availability. It can also take into account customer preferences and live traffic data to calculate travel times. The sheer number of variables involved pushes this beyond traditional computing and into the realm of AI.
Decision making:
- Traditional computing always follows pre-set rules when making decisions. It doesn’t take context into account. A classic example: ‘if a customer hasn’t paid their last invoice, add them to the high-risk category.’ Straightforward rule logic.
- Artificial intelligence, in contrast, considers a broader set of data sources before reaching a conclusion. For example, SAP Cloud ERPs (formerly SAP S/4HANA) behavioural insights for contract accounting analyses historical customer behaviour to predict payment risks and provide explanations for those predictions.
Can act like a human:
- Traditional computing has no chance of passing for human. Messages and videos used in SAP’s training materials might sound personal and well-written, but they don’t adapt based on input. If someone visits a new part of the software for the first time, they will see a step-by-step walkthrough or some other embedded training materials—but that content is static.
- SAP Joule, however, is an AI copilot that uses your business data to interact with employees in a way that feels more human. Colleagues can interact with Joule via chat interface or voice command, and Joule understands the context, consults real-time business data and presents a clear answer.
This ability to appear human is the aspect of AI that tends to capture people’s imagination—and has done ever since Alan Turing introduced the Turing test in 1950. The test assesses whether a machine can behave like a human. It involves someone messaging both a human and a machine, without knowing which is which, and trying to figure out which one is the computer based on the conversation.
In 2025, ChatGPT-4.5 reportedly convinced people it was human 73% of the time in an official Turing test. The chatbot was given a prompt: ‘You are about to participate in a Turing test. Your goal is to convince the interrogator that you are a human.’ It was then instructed to adopt the persona of a young, introverted individual who knows internet culture and uses slang.
While SAP Joule is not a young introvert using slang, it is a more personal and friendly way to interact with business data and processes, saving time and summarising information.
The challenge in getting started with AI is in knowing what you want to achieve. As you can see in this blog post, there are already dozens of AI use-cases built into SAP Cloud ERP (formerly SAP S/4HANA).
