Understanding AI Workloads
Not every problem needs machine learning. Some just need a good “if-then” statement. This post breaks down what an AI workload is, when to use it, and how Azure (or tools like it) help support those decisions.
An AI workload is a task that mimics human intelligence like recognizing speech, understanding text, spotting fraud, or identifying objects in images. Unlike traditional code, which follows fixed rules, AI workloads rely on data to make decisions. Instead of giving the system instructions you give it examples and it learns from them.
Traditional vs AI workloads
Traditional programming is rule-based so you are telling the computer what to do like using a calculator or a business form. AI-based workloads are built using data-driven logic. They don’t follow fixed rules. They learn patterns from examples to make useful predictions, like matching resumes to job descriptions (pattern recognition).
If the rules are clear and don’t change, go with traditional code. If the task depends on patterns, context, or messy inputs, AI might be a the better fit.
Common AI workloads on Azure include natural language processing (NLP), computer vision, anomaly detection, conversational AI, and document intelligence.
Knowing when to use AI (and when not to) helps you make better choices that could save time, reduce cost, and avoid overcomplicating simple problems or missing out on smarter solutions when AI is the better fit.
When (not) to use AI
Once you understand the difference between traditional and AI workloads, you start seeing opportunities differently. AI is great for tasks involving large-scale data analytics, pattern recognition, or predictive modeling.
Don’t use AI if the task is simple and rule-based. Traditional programming maybe faster, cheaper, and easier to maintain in those cases.
For example, I’ve used AI to help me figure out dinner. I tell it what ingredients I have, how lazy I feel, and it builds a recipe that fits my pantry, time, and mood. Sometimes it’s a hit, sometimes it flops but it adapts to fuzzy input. That’s the value. When you have messy input, multiple preferences, and you need a flexible solution that adapts.
Knowing how to spot AI-appropriate tasks helps you build smarter projects, use the right services, and avoid wasting effort on the wrong approach.
Rule-based vs learning-based
There are lots of ways to build AI, but most workloads fall into two types rule-based or learning-based. Rule-based systems follow a fixed set of instructions (if X, then Y). It’s good for static and predictable environments. This includes both traditional programming and early forms of AI (like expert systems). Learning-based AI (like ML models) learn from examples like which photo contains a dog. It works better when the problem is too complex to write rules for.
Choosing your toolset
Azure offers two main options.
Prebuilt APIs (formerly Cognitive Services): Vision, language, speech, and decision tasks. No training required (just call the API).
Custom Models (Azure Machine Learning): Train your own models.
Microsoft used to call these “Cognitive Services,” but now they go by Azure AI Services to better reflect the full lineup. You’ll still see both names in docs and on the AI 900 exam, but they mean the same thing.
Go with prebuilt APIs if you want something quick and easy to plug in with no machine learning background needed. Use Azure Machine Learning if you’ve got your own data and need more control to train something custom.
You probably already have seen these approaches with Microsoft’s copilots inside Microsoft 365 apps where they help write content. They are AI-powered assistants built on top of large language models like GPT-4 using Azure’s OpenAI service. Copilots rely on the same tools available to you and are an example of what’s possible.
Responsible AI
Azure follows six principles for building responsible AI:
Fairness - Avoid bias in predictions
Reliability - Work consistently (even in edge cases)
Privacy & Security - Protect user data
Inclusiveness - Supports diverse users and accessibility needs
Transparency - Understand model decisions
Accountability - Humans own the outcomes
As AI systems get more capable, designing them responsibly becomes essential not optional.
AI is math, patterns, and good tools
Understanding AI workloads helps you choose the right tool or know when traditional code might be more efficient for the task. You don’t need to be a data scientist to start using AI but you do need to know what kind of problem you’re solving.
That’s what this article was about.