DO YOU REALLY NEED THAT AI COURSE?

By Ronise Nepomuceno.

If you are a mid-career professional on LinkedIn, you might have seen that almost every day someone in your network announces the successful completion of an AI-related online course, along with a growing collection of certificates in Generative AI, AI Strategy, Machine Learning, Responsible AI and prompt engineering.

Without even needing to search for AI courses, your feeds keep rolling with algorithmic suggestions and adverts, ranging from free programmes to bank-breaking enrolments costing thousands. You can study for two hours or six months. You can even take a Master's on the subject. Wherever you are in the world, you can learn from prestigious universities, technology companies, training businesses, consultants and people whose principal evidence of expertise appears to be that they teach courses about AI.

The abundance of choice is so overwhelming that it can paralyse your decision. To complicate things further, not every course is equally transparent about who designed it, who teaches it, how recently it was updated, or what kind of competence it actually develops. Some may be carefully built by experienced practitioners. Others may be little more than repackaged public information, lightly edited slides, or content assembled quickly to meet demand. This is not new to AI.

It is only natural to feel that you risk being left behind if you don’t follow the trend. The labour market is changing, and AI-related skills are increasingly visible in employer surveys. Coursera’s 2025 Global Skills Report says that generative AI enrolments on its platform had passed 8 million, with nearly 700 GenAI courses available and an average of 12 GenAI enrolments per minute in 2025.


The World Economic Forum’s Future of Jobs Report 2025 identifies AI and big data as among the fastest-growing skills employers expect to demand between 2025 and 2030. The OECD (Organisation for Economic Co-operation and Development) also reports that AI adoption is increasing and that workers will need varying levels of AI literacy, digital competence, and specialist expertise depending on their roles.

But this does not mean that every professional needs the same AI course, or that a paid certificate is automatically the best place to start. The OECD has warned that most workers exposed to AI will not necessarily need specialised AI skills such as machine learning or natural language processing. Many will need a more general understanding of AI, stronger digital skills, better judgement about where AI is useful, and enough confidence to work with tools responsibly.

The online course industry has always responded quickly to trends. New and emerging fields often struggle to find a skilled workforce and, as such, offer attractive rewards for the right candidate. And as the saying goes, “where there is demand, there is a product”.

Not long ago, Project Management, Agile and any courses with the word Transformation in their title were the belle du jour, followed by UX design. But there is something different about the current AI training boom. The subject itself is changing rapidly. A course developed six months ago may still teach useful principles, but its examples, tools, assumptions or recommended workflows may already have moved on. Training providers benefit from the opportunity that this creates. There will always be another development to learn, another capability to master and another acronym to add to the vocabulary. The finish line keeps moving beyond the horizon.

For those in mid-career, the first step is not to buy the first course you see on your social media feeds. It is to take stock of which skills and experience can already be transferred as valuable, and which missing skills are not necessarily AI skills at all.

Take, for example, the mastery of Excel documents or even knowing the difference between automation and what AI can do. Many professionals still use spreadsheets as list containers and are not even aware of how to build sortable tables, automate functions, create pivot tables or use macros. In those circumstances, AI may not be the right solution. It can be slower, more expensive, less reliable and less transparent than simple automation. It can also produce inaccurate results if the original document is not clean, structured and properly formatted.

The second step is to assess how AI can make your work more efficient. Where does your time actually go? Which tasks are repetitive? Which require judgement? Which involve summarising, drafting, classifying, searching, comparing or transforming information? For these first two steps, free online courses, public tutorials and careful experimentation may be enough.

The next step is to decide whether you need to learn how to use AI, work with people who build AI, or make decisions about AI. These are very different capabilities, yet they are frequently packaged under the same broad promise of becoming “AI-ready”. A marketing professional experimenting with generative AI does not need the same knowledge as a programme manager overseeing an AI implementation. Maybe the course they need is one that teaches them some aesthetic evaluation, or simply helps them develop at least a basic level of good taste. A senior leader approving investment would need something different from a data scientist developing models.

Before paying for a course, look at the curriculum and ask a simple question: which decisions will I be better equipped to make after completing it? If the answer is difficult to identify, the certificate may be clearer than the learning outcome. Only then does it make sense to consider investing significant money.

A more expensive course can be worthwhile when you need something that the free resources struggle to provide. That includes a carefully designed curriculum, rigorous assessment, access to knowledgeable instructors, meaningful feedback, exposure to different professional perspectives and a network of people working through similar problems.

However, it is important to bear in mind that price and institutional prestige should not be confused with educational value. Before spending hundreds or thousands of pounds, those considering enrolling should try to find out about who actually developed and teaches the programme, how recently the material was updated, whether your work will receive meaningful feedback, whether the assessment tests real understanding, and what previous participants went on to do with what they learned. Needless to say, prices and packages should be transparent and easy to find before requiring any signing up or subscription.

The fundamental question is not whether an AI course, or any professional development course,  is expensive or prestigious. It is whether you can explain, before enrolling, what you are paying to acquire.

We tend to talk about courses as though their purpose is simply to transfer knowledge. That is only part of the transaction. People buy courses for different reasons. Some want knowledge. Others need structure. They could learn the same material independently, but want somebody else to decide what they should learn first, what comes next and what matters. Others want the credential. There is nothing necessarily wrong with that.

Mid-career professionals operate in a labour market where signalling matters. Putting the name of a respected institution on your CV or LinkedIn profile may help establish that you have made a serious attempt to understand a new field. It may help you pass an initial screen, support an internal conversation or give others a reason to take your interest seriously. But taking a course only for the sake of certification won’t guarantee that you’ll keep your job, change your career or get a promotion.

A certificate can signal interest, effort or exposure to a topic. It does not automatically prove competence.  Certifications that rely primarily on multiple-choice quizzes can be poor evidence of learning outcomes, especially when many assessments can be completed with AI tools. This doesn’t mean that every quiz-based certificate is meaningless. It means you should ask what the certificate actually represents.

There are also some people who are simply buying access. A good course can introduce you to professionals from different industries and countries who are wrestling with similar problems. For an experienced professional, those conversations can sometimes be more useful than another lecture explaining what a large language model is. The difficulty begins when we confuse these things. Knowledge, structure, credentials and networks have different values. A £20,000 course may not contain thirty times more knowledge than a £ 15,000, £ 10,000 or £100 course. You may be paying for the institution’s reputation, assessment, access to other participants, academic structure and, inevitably, the name on the certificate. That can still be worth what it is worth, if that is what you are paying for. But you should know exactly what you are buying.

The best AI course is not the one that looks most impressive. It is the one that clearly matches your needs, your role and the decisions you need to make. So before you enrol, be clear about what you need it to do.The main question is whether the course you are choosing is worth it for you and for the purpose you need it to serve.