jeff genovich


When ChatGPT exploded onto the scene in late 2022, everyone scrambled to answer the question: Is generative AI going to put my job at risk? 

Concerns surrounding automation and labor displacement are nothing new. But as artificial intelligence (AI) grows in capability and popularity, the concern is becoming more prominent and is impacting many different industries, job levels, and professional fields. 

So as we dive deeper into the impact of artificial intelligence on recruiting, let’s address the elephant in the room: which jobs and careers are actually at risk? And what can you, as a job seeker, do to AI-proof your future prospects?

Understanding the two branches of AI

Before we dive into the implications of how AI will change how people work, let’s discuss the two branches of AI and how they impact recruiting: 

  • Predictive AI makes predictions or forecasts based on patterns and historical data. Think personalized shopping reocmmendations or spam filers. 
  • Generative AI creaes new data that resembles the patterns found in training data—like text generation, deep fakes, etc. 

Although there’s the risk of overgeneralization, predictive AI will mostly impact low-skill work, while generative AI will impact high-skill work. This is part of the reason why generative AI caused such a stir among high-skill professionals—because it was the first window into the fact that these jobs could be at risk.

Among the purported victims of generative AI, depending on which articles you read, are lawyers, writers, artists, videographers, coders, software engineers, customer service agents, journalists, financial positions, etc. In other words—a wide swath of the economy.

However, as we will discuss below, reports of the death of the modern professional have been greatly exaggerated.

How AI will change expectations of high-skill professionals

Despite the bleak claims that AI will replace high-skill professionals, your job isn’t at risk—so long as you adapt to the new reality. Rather than replace the modern professional and high-skill employees, AI-powered tools, when leveraged correctly, can help you achieve the same results as before with greater speed and efficiency. 

But that’s good news, right? It means that employees can provide the same value and work product, but in less time. Shouldn’t this open the door to shorter work weeks, more flexible scheduling, etc.? 

Well, we’ve done this song and dance before—in fact, every time there’s a major technological advancement, people start talking about the utopian upsides. AI is just the latest in the series.

The great economist John Maynard Keynes, writing nearly a century ago, anticipated that technological advancements, the workforce would become more advanced and, as a result we would all be working 15-hour weeks. Of course, we all know how that worked out—it didn’t!

The reason: Keynes underestimated the human desire to compete. Rather than work a mere 15 hours, we’d rather keep the same 40 hours and increase our output, thus making ourselves more valuable to the employer. It’s hard to imagine this tendency changing in the face of AI.

The shift of core competencies: from technical to strategic

But as AI develops, will human employees continue to be necessary? The short answer: yes, but human core competencies will need to change.  

Especially in highly tech-forward fields, AI can’t function on its own. It needs a human being to operate it. There are a number of reasons why that’s the case. 

First, data hallucinations are real. AI doesn’t have a perfect track record when it comes to generating information, and it can be difficult to pin down a typical accuracy or error rate. Knowledgeable human users will need to sift through generative AI outputs to determine which is accurate or not. Indiscriminately accepting AI outputs can lead to error and, by extension, business risk. 

Additionally, AI is limited in its capabilities by virtue of its functionality. To understand why this is the case, we need to dive a little deeper into how exactly AI works, specifically the difference between machine learning and deep learning

  • Machine learning is based on algorithms that are trained on data. These algorithms detect patterns and learn how to make predictions and recommendations by processing preexistent data and experiences.
  • Deep learning uses neural networks—based on the ways neurons interact in the human brain—to ingest data and process it through multiple iterations that learn increasingly complex features of the data. 

Considering both of these concepts at their base level, it’s clear that AI’s functionality is dependent entirely on the data fed into it. It can only anticipate scenarios it’s already been exposed to or can extrapolate from existing data. 

So while AI will likely become more than capable of designing, optimizing, and testing individual components, professionals’ key differentiator will be in how they think about systems more broadly. Traditional tasks will give way to more strategy, high-level thinking.

The specifically implications of this reality, of course, will vary across industries and fields: 

    • Engineers will be less experts in individual components, but more broadly in systems and use of AI-powered tools to achieve more complexity in design
    • Finance professionals will be able to achieve risk assessments, fraud detection, and forecasting more efficiently than before, able to focus their time on customer care and creative solution-building
    • Administrative professionals will be able to streamline customer service, process automation, schedule optimization, and more, and can focus on more personal, human-centric tasks

These, of course, are merely examples. Different industries, disciplines, and organizations will apply these principles differently. But the broad change is that humans will leverage AI to achieve more menial, rote tasks, and can focus on more complex, creative work.

What can job seekers do?

So as a job seeker, what steps can you take to future-proof your career? Here are a few that you can start implementing now:

  • Educate yourself on AI in as much detail as possible—specifically looking at how it relates to your work
  • Understand your role in building and managing systems, not simply in becoming an expert in individual components 
  • Don’t look for niches where AI hasn’t gone already—look for niches where AI can’t go

This may involve some minor pivoting—or major pivoting, depending on your situation. But at the end of the day, responding proactively to AI is going to be a much bigger benefit to your career than simply crossing your fingers and hoping it goes away.

If you’re unsure of what specific steps you should take or niches you should consider, it’s helpful to have someone in your corner who knows you and can be a trusted advisor for your career. Contact Brightwing to start a conversation with one of our recruiters.

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