Unravelling the AI landscape: What entrepreneurs need to know in 2025

A guide to AI terminology and its potential based on today’s technological reality

Over the past months, I’ve received many questions from entrepreneurs and students about terms from the AI world.

“What’s the difference between AI and AGI?”

“Is generative AI the same as an AI agent?”

“Should my business already invest in Agentic AI?”

These terms are popping up all over the media lately. And if you sometimes feel overwhelmed by the flood of terminology, don’t worry. You’re definitely not alone! That’s why I wrote this blog post: an attempt to explain these terms in simple English.

Traditional Machine Learning (ML): where it all began

Before diving into the newer terms, let’s go back to the beginning: traditional machine learning. Think of it as a system that learns patterns and improves over time with more experience. Unlike classic software, which follows explicit instructions (traditional programming), machine learning tries to detect patterns in data. It then uses those patterns to make predictions - without every possible scenario being pre-programmed.

For example, when you get a notification in your inbox like “This email might be important,” that’s traditional machine learning in action. The system has analysed your email habits to predict which messages you’re likely to respond to quickly.

Other examples include:

• Credit scoring models that assess whether a loan will be repaid

• Weather systems predicting tomorrow’s temperature

• Medical tools that analyse whether a tumour is malignant

• Webshop recommendation systems suggesting products based on purchase history

Traditional machine learning works well for specific, clearly defined tasks where the system learns to make predictions. Usually, it requires structured data and human guidance to define what a “good” result looks like. For instance: What’s tomorrow’s temperature? Is a tumour malignant or not?

Artificial Intelligence (AI): the bigger picture

Artificial Intelligence is the umbrella term that includes all technologies enabling computers to mimic human behaviour. While machine learning focuses mainly on making predictions, AI is much broader. It also covers reasoning, problem-solving, understanding language and perception.

In short: AI refers to computers doing things that normally require human intelligence. Think of:

• Understanding language (like giving voice instructions to your GPS)

• Recognizing objects in photos (like your smartphone automatically tagging people and places)

• Making complex decisions with many variables (like your navigation app avoiding traffic jams)

Most AI systems we interact with today are examples of so-called “narrow AI”: they perform one specific task very well but lack the broader reasoning skills of humans.

Generative AI: the creative revolution

Over the past two years, we’ve seen explosive growth in generative AI: systems that don’t just analyse data, but also create entirely new content.

When I first used ChatGPT in 2022, I was amazed at how fluently and coherently it could write about nearly any topic. Same with DALL-E: generating images from a simple text prompt - in no time at all.

How does generative AI work? By analysing massive amounts of data, it learns to recognize patterns. Then, it generates new content that follows those patterns, without directly copying the original data. It’s like a chef who’s studied thousands of recipes and now creates new dishes based on learned flavour combinations.

Examples of generative AI include:

• Writing articles, emails, or marketing copy

• Creating images from text descriptions

• Producing new pop songs in different styles

• Generating videos

What makes generative AI so powerful is its ability to amplify human creativity.

AI Agents: your digital specialists

AI agents are AI systems designed to carry out specific tasks or functions with a certain level of autonomy. You can think of them as digital specialists with well-defined responsibilities.

A real-life example: AI agents handling customer questions outside business hours. They can answer common questions, schedule appointments, or - if needed - forward complex issues to a human agent. A well-known case is KBC’s Kate assistant.

Other examples include:

• Automated trading systems in financial markets

• Customer service chatbots handling routine tasks

AI agents have clear boundaries: they do what they were trained to do, within pre-defined scenarios.

Agentic AI: more autonomous, but still within boundaries

If AI agents are specialists with a narrow role, Agentic AI takes it a step further. These systems can plan actions over multiple steps, adapt to changing conditions, and take action based on predefined goals. There’s little need for direct human input - though human oversight remains crucial, especially for setting goals and ethical boundaries.

I recently tested “Manus,” an Agentic AI system from China. It helps you with various tasks. For example: you want to do market research on house prices in your area. The system gathers info from different sources, builds a dashboard, and writes an analysis. Pretty impressive - but the output still needs to be checked and possibly enriched with extra context.

Agentic AI combines several capabilities:

• Environmental awareness (via data inputs)

• Reasoning within set limits

• Planning actions across multiple steps

• Learning: it improves over time

The big difference with traditional AI? Agentic AI can handle complex, multi-step processes, while traditional AI focuses on one clearly defined task.

Artificial General Intelligence (AGI): the holy grail

AGI is the theoretical end goal: a type of machine intelligence that equals or surpasses human intelligence across nearly all areas. Today’s AI systems are still narrow AI - extremely good within one domain, but limited in flexibility, creativity and common sense.

A true AGI would be able to:

• Combine knowledge from different domains (e.g. applying chess strategies to business strategy)

• Understand implicit context and nuance in communication

• Adapt effortlessly to entirely new situations without extra training

• Solve problems with creativity and innovation

Even though some headlines suggest otherwise, we are still far from achieving AGI. Even the most advanced models today remain domain-specific thinkers.

What does this mean for your business?

As an entrepreneur, it’s important to distinguish these terms. It helps you evaluate AI tools correctly and set realistic expectations.

In short:

• Machine learning helps you predict customer behaviour or optimize pricing

• Generative AI supports you in creating text or visual content

• AI agents can automate repetitive tasks

• Agentic AI supports multi-step processes and decision-making, but still needs your guidance

The key is to choose the right type of AI for your challenge - not to use AI just because it’s trendy and certainly not to overestimate its capabilities.

Based on my experience with companies: start with the basics. Begin with traditional machine learning and generative AI. Build a solid data infrastructure. AI needs data. Without high-quality data, it’s “garbage in, garbage out” - even with AI.

And perhaps the most important lesson: all current AI technologies have their limitations. They work best when combined with human expertise.

Dennis Houthoofd