Autonomous technology has been polarizing throughout the 2010s. This phenomenon typically stems from fears like the threat of human job dissolution and the possibility of machine error, but while these broader anxieties are beginning to be dispelled by increased understanding and awareness, one myth has remained reasonably prominent: autonomous technology — namely that rooted in machine learning — has come at the expense of human creativity. 

While flawed, the logic behind this misconception is easy to understand; automation has become synonymous with convenience, and as it streamlines countless complex processes, it has helped online marketers reduce -overhead and focus their energy on other aspects of their campaigns. So how can creativity be a constructive option within this process? 

In reality, machine learning has liberated creativity, creating new paths for creative ideas rather than eliminating them outright; this continues a consistent cycle within our acceptance of new technology: we start out feeling collectively uncomfortable and skeptical; then we begin to identify emerging creative directions and their broader implications, and finally, we embrace these methods as a new norm that is subsequently disrupted by the next technological advancement. 

As noted by Dawn Winchester, CEO of Publicis North America, machine learning is following in the footsteps of Photoshop and desktop film editing software in “creating new waves of creation and innovation that we now almost take for granted.” 

In a marketing sense, machine learning is hardly robbing us of our creative freedom. When boiled down, the creative facet of ad creation is largely rooted in aesthetics — “what looks good? What colors play well together? What elements of an image or TV spot are appealing to consumers?” Machine learning has simply fine-tuned otherwise lengthy and tedious parts of this process; in the past, marketers have relied heavily on intuition and trial-and-error data stockpiling as a basis for creative direction, but today, machine learning can help campaign teams make faster and more efficient decisions in this regard. This benefit is magnified by other useful features like contextual image and video analysis  — all of which strive to foster human creativity, but in a more seamless manner. 

With all of this in mind, it is important that we are patient with machine learning in a creative sense, taking the time to embrace and master its potential before it is inevitably replaced by a more advanced successor.