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You Don’t Need to Become a Data Scientist to Future Proof Your Career

4 minute read

By Sarah Gilchriest, Chief People Officer of Workforce Learning, the group encompassing QA, Circus Street and Cloud Academy, discusses how businesses and their employees can ensure they thrive in the era of AI

Debate around the long-term impact of AI has dominated the media since the launch of ChatGPT. Much of the focus has now turned to how AI will automate many business functions, potentially leading to widespread redundancies. One recent eye-popping report from Goldman Sachs put the figure at 300 million jobs in Europe and the US alone. This seismic change in working practices appears to already be happening with BT announcing it would cut 55,000 jobs over the next seven years and replace them with 10,000 new roles in AI.

Naturally, this will make many workers fear for the future of their careers. However, it’s important to take a step back and see these changes in context.

First, this is going to be a drawn-out process, and we remain a long way from AI being capable of taking on a lot of skilled jobs. For example, many generative AI applications are currently far from flawless. Most companies would not trust them to take on important business functions any time soon. Those that do should implement comprehensive human oversight.

Second, technology driven automation has been a fact of business life since the industrial revolution. Roles constantly evolve – some become obsolete while entirely new careers take their place. According to economist David Autor, 60% of workers are in occupations that did not exist in 1940. The attention AI has may seem that this process of destruction and creation has accelerated, but developing and integrating any new technology will always take time.

Third, the impact of AI is likely to be quite uneven. Businesses and industries will adopt more advanced AI solutions at different rates, and it will affect professions to varying degrees. Goldman’s research highlighted that 46% of tasks in administrative and 44% in legal professions could be automated, whereas the figure for construction and maintenance is 4% and 6% respectively.

Finally, although the spectre of widespread automation can seem scary, AI actually provides a huge opportunity to increase productivity and remove a lot of mundane workday tasks. Used correctly by businesses, it will manifestly improve working conditions while spurring innovation, efficiency and ultimately, profitability.

Of course, this is not to say that employees should breathe a sigh of relief and ignore AI. It is still essential to take steps to future proof your career and ensure that as AI, or indeed any new major technology advancement, changes working practices you are in the best possible position to respond.

The same is true for businesses. There is a well-documented global skills gap in technology which is particularly acute in data-related fields. As AI advances, this gap is only going to increase and push the cost of hiring data-skilled workers higher. Using AI effectively within a business requires widespread data skills which are best acquired by upskilling existing teams.  


The starting point is realising that there is no one size fits all approach. Every person will have different career ambitions, potential exposure to AI and expertise required in their role. Upskilling or reskilling is often a long-term process.

For most people, quitting their current job and dedicating time to retrain in a new field is impractical and undesirable. It is also highly likely to be completely unnecessary. The reality is that you do not need to become a data scientist, analyst or engineer to always be employable. Every new technology is divided between those who develop, implement and maintain a solution and those that know how to best use, manage and innovate with it. The vast majority of professionals will fall into the latter category with AI.

Consequently, your starting point is determining how you can best use data, and by extension, AI, in your existing role. This has the added advantage of being an attractive strategy to most employers - they want you to be more effective in your current position - and as a result, may lend a lot of support to your upskilling.

Generally speaking, the best first skill to learn is basic data knowledge. Understanding how AI works and the foundations of data analysis will help you to ascertain how you can better apply data today while also getting to grips with the potential long-term impact of AI on your profession. From there you can begin to plot out how you can build on these skills and evolve your career.

A good rule of thumb is not to try to be a square peg in a round hole. People train better if they enjoy and are interested in the field they are exploring. Seeking to bend your career on a trajectory towards a role you think will have longevity but you aren’t enthusiastic about is unlikely to be successful.

Luckily there are a multitude of ways to now learn skills, from online to in person, to professional development courses to ad hoc targeted skill acquisition. Each person learns differently so do experiment with the format that suits your mentality and lifestyle the best. What is universal, however, is the benefit of applying new skills as quickly as possible to solidify learning. Ideally, this can be facilitated in your current role. Engaging with your employer on your training journey is essential to make this happen. Many businesses do offer upskilling programs, those that don’t should be willing to help you with your independent learning if you make a good commercial case.

AI will eliminate certain roles; it will also create millions of new jobs. For example, copywriters who may see gen AI take on the bulk of their writing could find that upskilling themself in search engineering on tools like ChatGPT, to produce the best possible copy, will enable them to carve out a new career. Employees and businesses alike need to ensure they are well placed to work with and benefit from the latest innovations, and the best way to achieve this is through long term upskilling and reskilling.

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