What Are the 3 Types of AI?
There are three types of artificial intelligence: narrow AI (or weak AI), which performs a specific task; general AI, which replicates human cognitive abilities across the board; and superintelligent AI, which surpasses them. Today, only the first type actually exists. The other two remain theoretical horizons, debated in labs and boardrooms alike.
Here is what you need to know before signing a contract with an AI software vendor or approving a budget.
Type 1: Narrow AI, the one you already use
Narrow AI, also called weak AI or ANI (Artificial Narrow Intelligence), is designed for a single function. It often performs that function better than a human, but only within that defined scope.
Concrete examples:
- A conversational agent handling customer requests on a Moroccan banking website.
- A CV screening tool that ranks applications based on defined criteria.
- A fraud detection system for online transactions.
- GPT-4, Claude, Gemini: powerful language models, but still narrow AI specialized in text processing.
When a CHRO asks me whether AI will replace their recruitment teams, I ask a simple question: replace them to do what exactly? Narrow AI can accelerate CV screening. It cannot conduct a culture-fit interview, read a room, or sense that a candidate is embellishing their experience.
This is the type of AI Moroccan companies are deploying today, with results that vary depending on data quality and the clarity of use cases. The CGEM has called for innovation and artificial intelligence to become drivers of competitiveness. But first, you need to know which type of AI you are integrating, and for what precise objective.
If you want to assess your organization’s AI maturity before investing, I have built a 6-dimension diagnostic framework for exactly this kind of decision. Download the AI Board Pack 2026.
Type 2: General AI, the myth that shapes every debate
General AI, or AGI (Artificial General Intelligence), refers to a system capable of reasoning, learning, and adapting to any cognitive task, the way a human being would.
It does not exist yet.
But it shapes every strategic debate. OpenAI, DeepMind, Anthropic: these organizations publicly state they are working toward AGI. Sam Altman has spoken about a timeline of a few years. Nobody knows if that means five years or fifty.
Why does this matter to a business leader?
Because the investment decisions you make today, the architectures you approve, the skills you hire for, will either be compatible with AGI or obsolete when it arrives. This is not science fiction. It is strategic planning.
In Morocco, the question of a dedicated AI ministry is fueling a public policy debate. As a tribune published by Telquel.ma argues, having a minister is not enough if the country does not yet have its model. And as an analysis from Article19.ma points out, the creation of such a portfolio raises as many questions as it answers. Companies cannot afford to wait: they need a methodological framework to distinguish what exists from what is still theoretical.
As I explained in my analysis of the jobs that will survive AI, the question is not whether AGI will arrive. It is how you position your organization in the meantime.
Type 3: Superintelligent AI, the distant horizon
Superintelligent AI, or ASI (Artificial Superintelligence), surpasses human capabilities across every domain: reasoning, creativity, judgment, complex problem-solving.
This is the territory of philosophy and foresight, not of business decisions in 2026.
But ignoring this debate would be a mistake. The AI governance questions that European regulators are raising today, the AI Act, the guardrails imposed on high-risk systems, are directly linked to ASI scenarios. Dr Hervé Tiwa puts it plainly: “There is currently a legal vacuum that needs to be filled.” That vacuum exists partly because legislators are trying to anticipate systems that do not yet exist but whose early signs are already visible.
As a business leader, your role is not to resolve this debate. It is to understand why it is shaping the regulations you will need to comply with in two or three years.
What this classification changes for you
Most leaders I meet confuse the three levels. They read an article about AGI and panic. Or they deploy narrow AI thinking they have solved their productivity problem.
Both mistakes are costly.
Narrow AI, well chosen and properly integrated into your processes, with clean data and clear use cases, generates measurable value. AGI and ASI are horizons to monitor for your five-year planning, not tools to purchase today.
The question you should ask your next AI vendor: which type of AI are you actually talking about, and how do you measure results in my sector?
For further reading, see my analysis of AI’s drawbacks and how to choose the best AI for your business.
To structure your approach and avoid the most common deployment mistakes, request a free diagnostic.
FAQ
What is the difference between weak AI and strong AI?
Weak AI (or narrow AI) performs a specific task: translating text, screening applications, detecting anomalies. Strong AI (or general AI) would be capable of reasoning about any problem, like a human. Today, only weak AI exists as commercial products.
Does general AI already exist?
No. Current systems, even the most advanced ones like GPT-4 or Gemini, remain highly capable narrow AI. General AI is a research objective, not an available product.
Why is this classification useful for a business leader?
Because it allows you to ask the right questions to your vendors, allocate your budget to the right level of technological maturity, and anticipate incoming regulations. Confusing the three types means making investment decisions on the wrong basis.
What type of AI are Moroccan companies using?
Almost exclusively narrow AI: conversational agents, data analysis tools, recommendation systems. This is the right level for today. The challenge is integrating it correctly into existing processes, not chasing AGI promises.