The other day, I found myself locked in an online debate with a seasoned data scientist who argued fervently that AI could never replace data analysts. His rationale? Data analytics requires creative problem-solving that is unique to human cognition, such as feature engineering and interpretation of results. “AI,” he insisted, “will never be able to replicate that kind of creativity.” It’s a comforting thought, I suppose. But as the debate progressed, I couldn’t help but notice the shaky foundation on which this argument stood. After all, AI is already disrupting art, writing, and music—fields that are undoubtedly creative. 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. “Art’s value lies in personal interpretation. It is easier for them to accept AI outputs. Data analytics, however, is more about logic and objective solutions. It’s not the same.” I remained unconvinced. If art is subjective, does that mean it’s somehow less creative? Moreover, how does one reconcile this argument when considering the mind-boggling achievements of AI in fields that are far from subjective? Take self-driving cars, for example—vehicles that navigate the chaotic tapestry of human road behavior without a constant human hand at the wheel. Or AlphaGo, which conquered the ancient game of Go through 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 leaps of creative thinking. All these accomplishments stem from AI’s ability to master highly complex, creative problem-solving tasks that require navigating ambiguity. Aren’t these activities just as—if not more—challenging than deciding which variable to drop in a regression model or which time series model to apply? The data scientist doubled down: “Those are closed-ended problems,” he argued. “Data analytics is more like open-ended exploration. It’s about uncovering hidden insights in a sea of information. You can’t automate that.” This was when I realized the crux of the issue. His argument was rooted not in the reality of AI’s capabilities but in a one-sided, narrow interpretation of what creativity means and where AI fits in the scheme of things. He, like many other professionals in this space, seemed to hold a certain disdainful reverence for the human mind’s perceived uniqueness—a view that AI will never match the human touch in some vaguely defined creative domain. This kind of thinking, however, is symptomatic of denialism—denialism that mirrors how artists reacted to photography when it first emerged. The first photographers were mocked by painters who considered photography a ‘technical trick’ that could never replace the soul of a hand-painted portrait. Over time, as photography evolved, many of these painters found themselves without commissions, grappling with a world that no longer valued their traditional skill set in the same way. What we’re seeing in data analytics today is no different. Those who maintain that AI will never be able to handle creative problem-solving in data analytics are falling into the same trap as those early painters. They’re dismissing AI’s creative potential simply because it challenges their view of what creativity looks like in their specific domain. The truth is, AI’s ability to model complex relationships, surface patterns, and even simulate multiple solutions to a problem means it’s already doing much of what data analysts claim as their domain. The fine-grained feature engineering, the subtle interpretations—AI is not just nibbling around the edges; it’s slowly encroaching into the core of what we’ve traditionally defined as ‘analytical creativity.’ I’m not saying that data scientists or analysts will be replaced overnight. But to assume that AI will never touch their domain simply because it doesn’t fit into an outdated view of what creativity means is shortsighted. This is a transformative era, one that calls for a redefinition of roles, responsibilities, and skill sets. Data analysts and scientists who refuse to keep an open mind risk finding themselves irrelevant in a world that is rapidly shifting beneath their feet. So, let’s not make the same mistake as those painters of the past. Denialism is a luxury we cannot afford. Instead, we must embrace the evolving landscape and find ways to complement AI’s strengths with our own. After all, the scariest part of this AI revolution is not what the machines can do—it’s what we might fail 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 social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com The other day, I found myself locked in an online debate with a seasoned data scientist who argued fervently that AI could never replace data analysts. His rationale? Data analytics requires creative problem-solving that is unique to human cognition, such as feature engineering and interpretation of results. “AI,” he insisted, “will never be able to replicate that kind of creativity.” It’s a comforting thought, I suppose. But as the debate progressed, I couldn’t help but notice the shaky foundation on which this argument stood. After all, AI is already disrupting art, writing, and music—fields that are undoubtedly creative. 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. “Art’s value lies in personal interpretation. It is easier for them to accept AI outputs. Data analytics, however, is more about logic and objective solutions. It’s not the same.” I remained unconvinced. If art is subjective, does that mean it’s somehow less creative? Moreover, how does one reconcile this argument when considering the mind-boggling achievements of AI in fields that are far from subjective? Take self-driving cars, for example—vehicles that navigate the chaotic tapestry of human road behavior without a constant human hand at the wheel. Or AlphaGo , which conquered the ancient game of Go through 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 leaps of creative thinking. AlphaGo All these accomplishments stem from AI’s ability to master highly complex, creative problem-solving tasks that require navigating ambiguity. Aren’t these activities just as—if not more—challenging than deciding which variable to drop in a regression model or which time series model to apply? The data scientist doubled down: “Those are closed-ended problems,” he argued. “Data analytics is more like open-ended exploration. It’s about uncovering hidden insights in a sea of information. You can’t automate that.” This was when I realized the crux of the issue. His argument was rooted not in the reality of AI’s capabilities but in a one-sided, narrow interpretation of what creativity means and where AI fits in the scheme of things. He, like many other professionals in this space, seemed to hold a certain disdainful reverence for the human mind’s perceived uniqueness—a view that AI will never match the human touch in some vaguely defined creative domain. This kind of thinking, however, is symptomatic of denialism—denialism that mirrors how artists reacted to photography when it first emerged. The first photographers were mocked by painters who considered photography a ‘technical trick’ that could never replace the soul of a hand-painted portrait. Over time, as photography evolved, many of these painters found themselves without commissions, grappling with a world that no longer valued their traditional skill set in the same way. considered photography a ‘technical trick’ What we’re seeing in data analytics today is no different. Those who maintain that AI will never be able to handle creative problem-solving in data analytics are falling into the same trap as those early painters. They’re dismissing AI’s creative potential simply because it challenges their view of what creativity looks like in their specific domain. The truth is, AI’s ability to model complex relationships, surface patterns, and even simulate multiple solutions to a problem means it’s already doing much of what data analysts claim as their domain. The fine-grained feature engineering, the subtle interpretations—AI is not just nibbling around the edges; it’s slowly encroaching into the core of what we’ve traditionally defined as ‘analytical creativity.’ I’m not saying that data scientists or analysts will be replaced overnight. But to assume that AI will never touch their domain simply because it doesn’t fit into an outdated view of what creativity means is shortsighted. This is a transformative era, one that calls for a redefinition of roles, responsibilities, and skill sets. Data analysts and scientists who refuse to keep an open mind risk finding themselves irrelevant in a world that is rapidly shifting beneath their feet. So, let’s not make the same mistake as those painters of the past. Denialism is a luxury we cannot afford. Instead, we must embrace the evolving landscape and find ways to complement AI’s strengths with our own. After all, the scariest part of this AI revolution is not what the machines can do—it’s what we might fail 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 social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com https://docligot.com
