5 AI Certifications to Avoid in 2026 (And What to Get Instead)

If you are about to start an AI certification, or trying to decide which one to go for, this will save you time and money.

I am not talking about obscure certificates nobody has heard of. These are from well-known companies that people trust — which is exactly why so many people fall for them.

Here are five AI certifications to avoid in 2026, and the ones actually worth getting instead.

1. Andrew Ng's AI courses

Technically these are courses rather than a certification play, but you receive a certificate of completion, and a lot of people are putting them on résumés and LinkedIn profiles.

Two problems.

A certificate of completion is not a certification. There is no proctored exam under timed conditions, which means nothing verifies you did the work yourself. In 2026, when AI can complete those assignments for you, that undermines your credibility rather than supporting it. When thousands of people submit the same course certificate, it stops carrying weight.

The content teaches something different to what the industry needs right now. These courses cover how AI models work under the hood — building neural networks from scratch, the mathematics underneath. Five years ago that was the right thing to learn. But the models already exist. Claude, GPT and Gemini are here. The skill that matters now is knowing how to use these models and build applications on top of them.

So people spend four to six months learning theory while the world moved on to building.

It becomes productive procrastination — passively consuming a course, ticking boxes, and receiving a badge that changes nothing about your employability.

2. Cisco's AI certifications

This one is tricky, because people get sensitive about Cisco.

To be fair: if you want to be a network engineer, Cisco's networking certifications provide genuinely good structured learning. CCNA and CCNP introduce the right concepts.

But in 2026 Cisco rebranded much of its certification portfolio around AI — AI Technical Practitioner, AI Infrastructure Specialist, a lot of it pushed out free.

Ask a simple question: is Cisco the best place in the world to learn AI? Or is this a company that needs its ecosystem to look AI-relevant?

What is actually inside the training is AI through the lens of Cisco infrastructure — AI workloads on Cisco data centre hardware, AIOps within Cisco network management. If you already work in a Cisco environment, take the free material as a bonus on top of your CCNP. Fine.

But as a way to learn AI and get hired? I have never seen "Cisco AI Technical Practitioner" in a job description.

3. IBM's AI certifications

Same logic. IBM has an AI Engineering Professional Certificate and a set of certifications around its watsonx platform.

So ask the same question: is IBM where AI is actually being built in 2026?

The frontier models come from Anthropic, OpenAI and Google. The deployments happen on AWS, Azure and Google Cloud. The hardware running all of it is NVIDIA.

When you take an IBM AI certification, what you are learning is AI through the lens of watsonx. And watsonx has very little footprint in the job market. Go to any job board and count the roles asking for watsonx experience.

The IBM name is famous. But the people hiring for AI roles are not looking for watsonx on your résumé. You end up with a certificate from a big name covering a platform nobody is using.

4. CompTIA's AI certifications

CompTIA has AI Essentials and AI Fundamentals. AI Essentials is a two-to-three hour course giving you a competency certificate. AI Fundamentals is similar, aimed at non-technical learners.

Neither is a real certification. No proctored exam. You complete the course, you get the badge. In 2026, a badge saying you completed a two-hour AI course means nothing to a hiring manager.

One exception worth knowing: CompTIA also launched SecAI+, which is a properly proctored exam testing real AI security knowledge. But it is aimed at existing cybersecurity professionals and designed to stack on top of Security+ and CySA+. It is not an entry point into AI.

5. ISC2's AI credentials

ISC2 is a well-known name in security, so when they released AI credentials people got excited.

What they actually released matters. The Building AI Strategy Certificate is six on-demand courses, roughly sixteen hours, no exam, and a badge at the end. People are listing it as if it were a verifiable credential. It is a course-completion badge.

Separately, ISC2 has been weaving AI content into existing certifications like CISSP. But those are designed for senior security professionals already deep into their careers who need to add AI knowledge to what they already do — not for someone breaking in.

The pattern

Look at all five and one theme runs through them:

• A course-completion badge with no real exam, or
• A vendor trying to look AI-relevant, or
• A credential designed for people already deep into their careers

None of them prove you can actually do anything with AI.

What to get instead: proctored exams from companies that build AI

AI has made hiring harder. Screening is noisier, portfolios are easier to fake, and take-home assignments prove less than they used to. Which means verifiable proof that you sat an exam under supervised test conditions is a real signal.

Certifications alone will not bridge the gap — you still need to build things. But if you are new and figuring out where to start, a proctored certification is a solid entry point.

Here are the ones worth your money.

Anthropic's Claude certifications — with an important catch

In March 2026 Anthropic launched its first proctored credential alongside a major investment in the Claude Partner Network. As of July 2026 the programme has grown to four live exams delivered through Pearson VUE:

All four run 120 minutes and pass at a scaled score of 720 on a 100–1000 range. They are strictly proctored — no Claude, no external tools, no documentation during the exam.

The content is genuinely current: architecting production systems with Claude, multi-agent design decisions, tool selection, integrating AI into workflows, and deciding when an agent should escalate to a human rather than act autonomously. That is what people working with AI actually do every day.

Now the catch, and it is a big one.

Registration requires a partner email address on a recognised company domain. Personal email addresses will not work. Certification is currently available only to people at organisations in the Claude Partner Network — customers and unaffiliated individuals cannot register.

You will find a lot of sites selling Claude practice exams that do not mention this. If you are a career switcher on a personal Gmail address, you cannot sit these exams today, no matter how much prep you buy.

What you can do: joining the Partner Network is free for organisations actively bringing Claude to market, so if your employer qualifies, that is the route. Anthropic has signalled eligibility will widen later in 2026 — so this is worth tracking, not paying for prep on yet.

NVIDIA's AI certifications

A lot of people do not know these exist. NVIDIA now runs a full certification programme covering generative AI, large language models, AI infrastructure and agentic AI, with associate-level exams for beginners and professional-level for the experienced. All proctored, all available online.

The associate-level Generative AI and LLMs exam runs about an hour with 50–60 multiple-choice questions, and sits around $125 — check the official page when scheduling, as pricing varies by region.

This is the company whose hardware runs essentially every AI workload on the planet. And unlike the Cisco certifications above, a large majority of the generative AI content is platform-agnostic. You are not being locked into one vendor's ecosystem — you are learning how AI actually works. That knowledge transfers.

AWS AI and cloud certifications

Here is something people miss when choosing AI certifications: where does AI actually get deployed?

In the cloud. Every company building with AI runs it on AWS, Azure or Google Cloud. So if you want AI skills employers can immediately use, learn AI on the platforms where it gets deployed. And it makes sense to start with the largest provider.

AWS Certified Cloud Practitioner + AWS Certified AI Practitioner — do these together. Cloud Practitioner gives you the foundation: how the cloud works, core services, how things get built and deployed. AI Practitioner sits on top and teaches AI in the context of real cloud services. AI runs on cloud infrastructure, so understanding the platform and how AI runs on it is a genuinely strong base. Both are beginner-level proctored AWS exams at $100 each.

AWS Certified Machine Learning Engineer – Associate — for going deeper into how models are trained, tuned, deployed and managed. If you are interested in the model side rather than building applications on top of models, this is the one.

AWS Certified Generative AI Developer – Professional — for the more advanced, especially software engineers. Tests whether you understand building AI-powered applications in the cloud. If you are already technical, this keeps you ahead of the curve.

How to choose in one line

The honest summary

The five to avoid share one flaw: nothing verifies you personally demonstrated anything. A badge for finishing a two-hour course, or a credential teaching a platform the market does not hire for, does not move you closer to a job.

The ones worth taking are proctored exams from companies that actually build AI — Anthropic, NVIDIA and AWS. Just go in knowing that the Anthropic ones are, for now, gated behind partner organisations.

And remember the certification is the entry point, not the destination. It gets you past screening and gives your self-study structure. What gets you hired is being able to talk through something you actually built.