What does AI mean for entry-level employment in geospatial?

Disclaimer: I love AI. I use it constantly, both in my work and personal life. I use it for vibe coding. I use it for feedback. I use it to write shopping lists. But I'm not naive - I know there are conversations to be had around the ethics of AI, and I want to use this blog to talk about one specifically: entry-level employment in geo.

The BBC recently shared an article about how much graduates are struggling to find professional entry-level employment. When I was working on a shop floor in 2008 - just as the recession was hitting - I think maybe 80% of my colleagues were graduates from "top" universities, so this isn't anything new... but we aren't in a recession (touch wood!).

While there are of course lots of factors which are contributing to this - and I don't want to get into anything particularly political here - I do want to take a moment to think about the risk that AI might be posing to the next generation of geospatial professionals - and what we need to be doing to address it.

Is a "rung" on the career ladder disappearing?

Entry-level roles in geospatial have traditionally been where people learn the ropes - digitizing data, conducting basic spatial analysis, creating maps, supporting quality control. I remember spending one sunny afternoon digitizing character areas for the whole of the UK. So relaxing, what I wouldn't give to have those days back... Anyway, these aren't glamorous tasks, but they’re foundational. They build the instincts and context that underpin good decision-making later on in careers.

AI tools are starting to take on more of this “entry-level” work. The implication is there will be fewer opportunities for graduates or junior professionals to gain real experience, let alone, you know... pay rent, buy food etc.

What this could mean is a shift in geo team structures. In some cases, a single experienced person equipped with the right AI tools can deliver work that once needed a small team. Instead of a manager with a team of early-career staff, we may end up with a “prompt engineer” who works alone, supported by generative tools.

This is obviously incredibly efficient and works brilliantly in the short-term. But here's the problem: if the entry-level rung on the career ladder disappears, who will be the leaders of tomorrow?

AI is Only as Good as You are

One of my most used phrases at the moment is AI is only good if you know what good looks like.

AI can be a great partner, but it still relies on human judgment to set the right direction, verify results, and apply outcomes responsibly. That judgment doesn’t come from nowhere. It’s earned through experience and failure.

If we cut off those early experiences, we risk building a future workforce that knows how to operate AI tools but doesn’t have the context or expertise to know if the outcomes are any good.

Why yes I did use AI to create this image.

The Education Problem

This changing landscape also creates a real challenge for educators at universities and beyond.

Say you were at university and studying some form of GIS-adjacent discipline from 2022-25. You were probably picking up skills in desktop GIS, maybe some webGIS, python, javascript... all the usual suspects. Then you head to your first interview, and you immediately get asked about your prompt engineering skills. Maybe you've used chatGPT to cheese a few essays, but that's probably about it, right?

How do you prepare students for an industry that’s evolving faster than a university course can keep up with? How do you train the next generation in what's“cutting-edge,” when what's innovative today might be outdated by the time a student graduates?

What can we do?

We all have a role to play in ensuring our industry continues to benefit from AI and not become hamstrung by it. Here are some of my ideas as a total non-expert in this sort of thing:

For Graduates

• Curiosity has always been the best trait in any geo professional - but it's needed now more than ever. Follow industry trends, test new tools, and explore emerging technologies.

• Attend geo events, webinars, and conferences whenever possible to stay up-to-date with industry trends. Many events offer discounts (or even free tickets) for students and those out-of-work, and increasingly events are hybrid or fully online - making them even more accessible!

• Apply your skills in real-world contexts. Boost your portfolio and network by volunteering for causes you care about or take on personal projects (appreciate this isn't a luxury everyone has).

• Don’t underestimate soft skills. The ability to communicate, collaborate, and think critically is hugely valuable, particularly as we are increasingly leveraging AI for technical work.

For Educators

• Focus more on teaching softer, more commercial skills, rather than having students box-tick pieces of software.

• Encourage students to be flexible, curious, and resilient in the face of change. Craft projects which are more open-ended, focusing on problem-solving as a skill.

• Partner with industry to give students access to real-world challenges and tools - as well as starting to build their network.

For Employers

• Where possible, continue to invest in early-career talent, even if AI seems more efficient in the short term. This doesn't have to be about literally employing people. It could be making your AI-driven software available for students for free, publishing more educational content or sending your team out to talk to universities (if they're anything like me, they'll love it).

• Create spaces where new professionals can learn, experiment, and grow alongside AI -not in its shadow.

  
  

Remember: today’s graduates are tomorrow’s leaders, product managers and technical specialists. If we don’t start to grow them now, we limit what our industry can become.