Did University Prepare Me for a Career in AI? An AI Executives Journal

If I’m being honest, I didn’t see my career heading towards AI, before starting my degree, I didn’t really know much about it. Even as my understanding grew, I mostly saw AI as a tool to help me structure my work, find information and make certain tasks easier.

But the jump from a Media Communications degree to an AI Executive role didn’t feel as daunting as you might expect. I knew how to prompt ChatGPT to find different research perspectives, explain a difficult old book in simpler terms, and I even completed a module called ‘AI and Society’. I thought I knew it all.

But since starting work, I’ve realised how limited my understanding of AI was.

The things that have changed my perspective haven’t been better prompts or the latest AI tools. They’ve been integrations, workflows, databases, data privacy and governance. At university, I thought about AI largely in terms of how I could use it. I hadn’t really considered what happens when AI becomes part of how an entire business operates.

I know that my example is a little different as my company is ai focused, but I am also considering roles such as lawyers, accountants, business owners, marketing etc. Ones where AI isn’t the forefront, but most definitely available. If graduates are going to enter workplaces where AI is increasingly part of everyday operations, should every degree be doing more to prepare students to use it in the real world?

What university did prepare me for?

In the HEPI Student Generative AI Survey 2026, 68% of full-time UK undergraduates said AI skills are essential to thrive in today's world, yet only 48% felt their teaching staff were helping them develop those skills.

I am incredibly grateful for my degree, but did creating a fake influencer or researching into nostalgia have an impact on my role today, no. But learning how to communicate ideas, understand audiences, building confidence in presenting, dealing with time pressures and improving my critical thinking have all made me in the worker I am. In which I see incredible amounts of value.  

Thankfully through my AI module I had learned basic amounts about how LLMs work, what is a world model and how AI is being used in different careers. However, one my first week in my role, I was told my prompts were weak, something I believed I had already conquered and could carry with me. Looking back at where I was a month ago, I totally agree. ‘Make me this’ has now become a sequence of research prompts before my task comes into play.  

This makes me conscious of the fact that degrees can be getting stuck in the past a little. Even in a media degree, leading into an industry we know is AI heavy, allowed little understanding for the tools, uses or even how to get the best outcome through simply adapting a prompt.  

I understand a student’s work needs to be their own to demonstrate their knowledge and ability. Rules do need to be in place to avoid an increase of ai writing their work, especially when they don’t really know how to use it.  

But if a graduate is expected to use these tools a year later in their job, maybe the goal isn’t to keep it out of education. It’s teaching students where it does belong and how to use it.  

What I wish it taught me

Data and governance

A big learning curve for me has been governance issues. Occasionally at university we were told to not put your work into an agent but no reason why. Coming into an AI Executive role I hadn’t considered issues around data being shared with these tools. Maybe inserting my article for a pretend magazine didn’t cause an issue in first year, but the habit of inserting data into agents, could lead to a drastic mistake later in a career.  

Balancing AI and human input

This leads into the balance between AI and human input. It’s known that if you are using AI to help complete a piece of work you cannot use the outcomes word for word. But it is never explained what you can use, why and when and how much of that outcome needs human input.  

Building a career with AI means thinking in workflows

The biggest one, is how to use ai in a workflow, rather than for an answer. I know that if I understood the tools then as I do now, my time management skills would be higher, my stress levels would have decreased, and my work could have been a better quality. Not because I know how to get it to write me a perfect answer, it never does, but to elevate other pressures, allowing focus.  

It isn’t as easy as it sounds

Lectures are experts in their fields; AI doesn’t always slot in with this. Meaning not every course can have an increase in understanding AI. And we must consider the fact these tools are currently ever-changing, making them hard to keep up with.

But if every university had one ‘AI Expert’ that could guest lecture amongst all the courses, this might cover the bases.

And I am talking about someone who really knows the ins and outs. One lecture I had from an ‘ai expert’ just explained to me what we already knew about how to spot AI in text. Nothing about the processes, workflows or integrations.

So, did university prepare to use AI?  

Yes and no. But that yes mainly stands with my one module, which most courses may not offer.  

I don’t think every course suddenly needs to become an AI degree, nor do I think students should be encouraged to use AI for everything. But I do think there is a gap between telling students not to use AI and preparing them for a workplace where they may be expected to.

Maybe universities don’t need to teach students every new AI tool or feature. They need to teach them how to approach AI: how to question it, how to use it responsibly, how to recognise when it shouldn’t be used and, importantly, how it can support a wider workflow rather than simply produce an answer.

Employers have work to do too. The Office for National Statistics found only 11% of businesses with 10 or more have trained half of their workforce in anything AI-related.

I am only a few months into my career, and I do think my understanding of this will change again, but if there’s one thing this time has taught me, it’s that knowing how to use ChatGPT and being prepared to work with AI are two very different things. And perhaps that’s the gap universities now need to start closing.