The other day I found myself stuck in an online debate with a veteran data scientist who fervently argued that AI could never replace data analysts. His justification? Data analysis requires creative problem solving unique to human cognition, such as feature engineering and interpretation of results. “AI,” he insisted, “will never be able to replicate this kind of creativity.”
It’s a comforting thought, I suppose. But as the debate progressed, I couldn’t help but notice the shaky foundations on which this argument rested. After all, AI is already disrupting art, writing and music, undoubtedly creative fields. And unlike the deterministic world of data analysis, these creative disciplines are defined by their fluidity and subjectivity.
When I pointed this out, our data scientist quickly retorted that art and music are inherently subjective. “Of course AI can create art,” he said dismissively. “The value of art lies in personal interpretation. It is easier for them to accept the results of AI. Data analysis, however, is more about logic and objective solutions. It’s not the same.
I’m not convinced. If art is subjective, does that mean it’s less creative? Furthermore, how do we reconcile this argument when we consider AI’s staggering achievements in areas that are far from subjective? Take self-driving cars, for example: vehicles that navigate the chaos of human driving behavior without a constant human hand at the wheel. Or AlphaGowho conquered the ancient game of Go with creative strategies that baffle even the best human players. And let’s not forget AI’s contributions to protein folding, a previously intractable scientific problem that relied on advances in creative thinking.
All of these achievements stem from AI’s ability to master highly complex and creative problem-solving tasks that require navigating ambiguity. Aren’t these activities just as difficult, if not more so, than deciding which variable to exclude in a regression model or which time series model to apply?
The data scientist doubled down: “These are closed problems,” he argued. “Data analysis is more like open-ended exploration. It’s about discovering hidden information in a sea of information. You can’t automate this.
That’s when I realized the crux of the problem. His argument was not rooted in the reality of AI’s capabilities, but in a one-sided and narrow interpretation of what creativity means and AI’s place in the scheme of things. Like many other professionals in this field, he seemed to have a certain dismissive respect for the uniqueness of the human mind – a view that AI will never match human contact in a vaguely defined creative field.
This type of thinking, however, is symptomatic of denial, a denial that reflects the way artists responded to photography when it first appeared. The first photographers were mocked by the painters who considered photography a “technical trick” it can never replace the soul of a hand-painted portrait. Over time, as photography evolved, many of these painters found themselves without commissions, struggling with a world that no longer valued their traditional skills in the same way.
What we see today in data analysis is no different. Those who argue that AI will never be able to creatively solve problems related to data analysis are falling into the same trap as these early painters. They reject the creative potential of AI simply because it challenges their view of what creativity looks like in their specific field.
The truth is that AI’s ability to model complex relationships, surface patterns, and even simulate multiple solutions to a problem means it already does much of what data analysts claim to be their domain . The fine engineering of features, the subtle interpretations: AI doesn’t just nibble at the edges; this is slowly encroaching on the heart of what we have traditionally defined as “analytical creativity.”
I’m not saying that data scientists or analysts will be replaced overnight. But assuming that AI will never reach their field simply because it doesn’t fit an outdated view of what creativity means is short-sighted. We are living in a time of transformation, which calls for a redefinition of roles, responsibilities and skills. Data analysts and scientists who refuse to keep an open mind risk finding themselves irrelevant in a rapidly changing world beneath their feet.
So let’s not make the same mistake as the painters of the past. Denial is a luxury we cannot afford. Instead, we must adapt to the changing landscape and find ways to complement AI’s strengths with our own. After all, the scariest part of this AI revolution isn’t what machines can do: it’s what we risk failing to do if we refuse to adapt.
About Me: 25+ year IT veteran combining data, AI, risk management, strategy and education. 4x Hackathon Winner and Data Defender Social Impact. Currently working to revive the AI workforce in the Philippines. Learn more about me here: https://docligot.com
