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If you work in data, chances are you’re hearing a lot of buzz about AI and how it’s going to automate everything. While the headlines spell doom and gloom for knowledge worker roles like yours, the reality isn’t quite so dark. In fact, there won’t be any future of AI without people like you, who have the skills required to prepare and use data.
Right now, AI adoption is in a strange phase. The media is telling you that you could be replaced by it, but your day-to-day probably hasn’t changed all that much. You might be thinking that this all seems like a bunch of hype… and you wouldn’t be wrong. That’s because most companies’ use of AI is still in its infancy.
While 94% of companies say they are using AI today, most aren’t using it to its full potential. They’re struggling with data quality and infrastructure issues that make layering on AI nearly impossible. In the same study, almost three quarters of execs said that data issues would be the most likely reason they fail to achieve their AI goals. As it turns out, even a robot can’t make lemonade out of bad data.
This is where you, and your skills, step in to save the day. And you’re in higher demand than ever before. There’s been a 2,000% surge in roles requiring AI skills, such as data science and data analytics.
Before you breathe a sigh of relief, it’s important to recognize that your skills will need to evolve for an AI-led future. Let’s dig into exactly how.
3 Reasons Why AI Is A Friend, Not A Foe, Of Data
- AI + data pros will work together
AI will be a powerful ally, if you understand when and how to use it. Today, AI is primarily used to automate repetitive tasks, like QAing code. It has the potential to help you more efficiently explore vast, complex datasets, but can’t do it without human intelligence. For example, AI can analyze rich content, like video, much faster than a human could, but a human would still need to be in the loop to make decisions based on analysis of a video Right now, this type of technology is used for simple things like caption creating—but eventually, it could actually come up with its own ideas for films, TV shows and ads based on analysis of the most engaging or popular content. Human context would help cull and tweak those recommendations so they actually make sense.
Knowing when AI can help improve a process or uncover deeper insights will be critical in the next phase of data careers. You can get started on this now by experimenting with generative AI tools yourself. For example, you can ask generative AI to help you with tasks, and evaluate how it does. You’ll quickly realize that it’s not going to replace your job anytime soon.
- You’ll add human context that AI can’t.
Generative AI often comes up with a bunch of nonsense. While it can be incredibly fast at say, generating 50 ideas or writing up code for a program, most of what it puts out is relatively useless without additional context from a human. Even worse, sometimes, it can be biased or straight up wrong. For example, AI often struggles to understand sarcasm, meaning its understanding of someone’s sentiment when using sarcasm could be totally off.
As someone with expertise in your field and a knowledge of how these outputs would actually apply in real life, you’ll have the ability to layer on the appropriate context and tweak AI outputs to be actually useful. AI does all the grunt work, while you get the final say. Now, doesn’t that sound nice?
- People trust people – not machines
As AI becomes more powerful, and companies adopt it for more and more use cases, being able to explain just how it all works is going to be extremely important. Most people aren’t willing to blindly trust a machine’s recommendations or outputs. They still want a human expert to validate results, and explain next steps.
Remember how we just said that AI can be biased, or give you the wrong answer? Without data pros in the loop who knowhow it works, there’s really no way to mitigate those risks. This can become a huge issue when AI becomes involved in high stakes decisions, like hiring or investing. Data professionals will be relied on to understand the risks and limitations of AI models, and provide the additional context needed alongside its outputs.
If you work in data, you likely have a solid foundation of advanced statistical analysis skills, as well as knowledge of programming languages like Python, SQL and R, that will empower you to explain AI to your colleagues. You get concepts like data quality and data lineage, which will be key to AI explainability and being able to identify “what went wrong.” Consider brushing up on the basics of privacy, security and copyright rules, which are all top concerns when it comes to AI ethics.
Future-proof your career: tapping AI as your new coworker
As companies rush to adopt AI, demand for data professionals will continue to surge. In the US, data science jobs are expected to grow by 36% by 2031. Data science and AI engineering roles are also continuing to command high salaries globally, with AI engineers earning an average salary of €92,574 in Germany, $113,000 AUS in Australia, and $76,000 SGD in Singapore.
If you want to make sure you’re ready for the future of working in data, enrolling in a data analytics or data science bootcamp can help you gain AI-ready skills, quickly. Sign up for an upcoming workshop to learn more.