I have been asked this question more times in the past six months than in the entire five years before it.
"Do I need a certification to get into AI?"
My honest answer is: it depends on what you mean by need. And I think that distinction matters more than most people realise.
The Certification Boom Is Real
There is no denying it. AI certifications are everywhere right now. Google, Microsoft, AWS, DeepLearning.AI, Coursera, IBM. Every major platform has launched something with AI in the title. Some are free. Some cost hundreds of pounds. All of them are selling the same promise: complete this, and you will be ready for the AI economy.
I understand why people are buying in. The AI job market is genuinely growing fast. It feels urgent. And when things feel urgent, structure feels safe. A certification gives you a clear path: do this, then this, then this, and at the end you have something to show for it.
That feeling is valid. But it is also worth questioning.
What a Certification Actually Proves
A certification proves you completed a course and passed an assessment. That is not nothing. It shows discipline, it shows you made the time, and in some cases it shows genuine foundational knowledge.
But here is what it does not prove.
It does not prove you can solve a real problem with AI. It does not prove you can take a messy, undefined business situation and figure out where AI fits and where it does not. It does not prove you can build something, debug it, improve it, and explain it to someone who has never heard of a large language model.
Those things are what employers actually want. And they are built through doing, not through completing.
The Portfolio Beats the Certificate Almost Every Time
I have spoken to enough people hiring for AI roles to know this: a GitHub repository with three real projects will get more attention than any certification badge on a LinkedIn profile.
Why? Because projects show thinking. They show how you approach a problem, what choices you made, what you struggled with, what you learned. A certificate shows you were a good student for a few weeks.
In AI specifically, the field moves so fast that a certification from twelve months ago may already be teaching approaches that have been superseded. What does not go out of date is the ability to learn, apply, and build.
That ability is demonstrated through work, not credentials.
But I Am Not Saying Certifications Are Useless
Some certifications genuinely teach you things. If you are brand new to machine learning and you do Andrew Ng's Deep Learning Specialisation, you will come out with real conceptual grounding. That matters. You cannot prompt your way through understanding how a model learns without some foundational knowledge.
And in certain industries, particularly regulated ones like healthcare, finance, and government, certifications carry weight because they signal that someone has met a documented standard. That is a legitimate use of credentials.
The problem is not certifications themselves. The problem is when people treat the certificate as the destination rather than the starting point.
The Real Question to Ask Yourself
Instead of "should I get certified?", try asking this: "What problem do I want to solve with AI, and what do I need to learn to solve it?"
That question leads somewhere productive. It leads to specific knowledge, practical experiments, and real output. It also leads to a much clearer understanding of whether a structured course would help you get there faster.
Sometimes the answer is yes. Do the course, get the certificate, and then immediately go and build something with what you learned. The certificate alone means almost nothing. The certificate plus the project means something real.
What Actually Gets People Hired Right Now
From everything I have seen, the people getting into AI roles in 2026 have a combination of three things.
They understand the basics. How models work at a conceptual level. What prompting is and is not good for. When to use an API versus a fine-tuned model. When AI is the wrong tool entirely.
They have done something real. Built a tool, automated a process, created something someone else could use. Even small. Even imperfect. The act of building is what develops real judgement.
They can communicate clearly. AI without communication is just a hobby. The person who can explain what they built, why they made the choices they made, and what they would do differently is the person who gets hired, gets promoted, and gets trusted with more.
None of those three things require a certification to develop.
My Actual Recommendation
If you are completely new to AI, a structured course from a credible provider is a good starting point. It gives you vocabulary, context, and a map of the terrain. Do it. But do not stop there.
If you already have some experience, stop collecting certificates and start building things. One working project will teach you more than five courses.
And if you are trying to decide whether to spend money on a certification or spend the same money on compute credits to run experiments and build, spend it on the experiments.
The AI world does not reward people who have studied it. It rewards people who can actually do something useful with it.
There is a difference. Make sure you are developing the second thing, not just the first.