Surviving the AI Marketing Revolution: How to Go From an Onlooker To Riding the Wave of Change

Anyone who is watching industry trends knows that we are at the forefront of witnessing how artificial intelligence is gaining more prominence in the management of marketing strategies.

Those include simplifying tasks, automating tedious processes, unlocking new ways to scale test creative, ads, and segmentations. Then there is the fearful eye anticipation of a threat to jobs, leaving us in an abyss of uncertainty.

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How can we navigate this revolution without losing our way? Moreover, who will be the fittest to survive a revolution as dizzying as the one we are living through? The short answer – there is no one answer.

Accepting the inevitable present

AI is already immersed in most advertising and marketing-related platforms. According to research by Statista, in 2023, 37% of people working in advertising or marketing in the U.S. had used AI at work. But we can assure that near 99% of marketers leverage AI tools, even if they were not aware of that.

Analytics tools generate predictive smart audiences with those users who show behaviors more likely to make a purchase.

Self-managed bidding platforms offer AI powered campaigns like Google’s Performance Max or Meta’s Advanced+ that require less human configurations, and more freedom to optimize against a specific goal.

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Copy generation engines, such as Jasper.AI, assist in creating relevant ad versions.
AI is already here. We can’t stop it, we can’t delay it. We need to embrace it.

Recognizing its limitations

We need to understand how AI works in order to get the most out of it.

AI models only perform as good as the quality of inputs and parameters they are given. Missing key signals from our website or e-commerce behavior will lead to inaccurate predictions on how each audience will react to our ads.

From a creative perspective, if we don’t give our AI copy or design creator good references and parameters, it will fall into biased concepts or variations.

And finally, we need to acknowledge ethical and privacy boundaries, leveraging users data only to the extent for which it was captured and for their own good.

Read: Taking Generative AI from Proof of Concept to Production

The best approach is to look at the most relevant inputs to offer: invest in first-party customer data management, integrate enough amounts of historical data in a secure environment, and provide a sufficient amount of visual references, assets, design manuals and criteria to AI creative engines of what we want and don’t want to see associated with our brands. Training AI is a process similar to onboarding and coaching a new team member. Its future performance will be as efficient as the quality of guidelines, standards and examples we provide to it.

Leveraging it beyond the basics

Artificial intelligence can help us automate tedious and time-consuming marketing tasks. Create reports more quickly, expand the resizing of banners across platforms in minutes, summarize the attributes of a product to include them in the tab of your e-commerce page, or generate hundreds of versions of creatives with small variants to run a/b testing experiments.

This is only the output when we ask AI to do faster what humans already do more poorly. And most likely, we will tend to make comparisons that highlight the errors or biases in which the machines fall into, judging them with an even firmer eye than we would our human colleagues.

The real differential value that AI produces is when we understand that machines can not only be faster than us, but also carry out complex intertwined processes that we could not solve from our own side.

For example, finding visual patterns across thousands of creative pieces that perform best to enhance our ads analysis. Creating propensity models based on all the attributes of all users who convert more frequently or have a higher average ticket. Or building conversational interfaces that allow us to dialogue, ask questions and get answers on a data set to capture rich patterns and insights. AI not only can speed up processes but also unlock new opportunities.

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Survival of the fittest

So, taking into account these considerations, what is the best way to join this revolution and be much more than witnesses, protagonists of change?

To a large extent it is a matter of assimilating that technology is evolving, and so must we.

Our mindset has to change, leaving behind biases, preconceptions and recipes for success based on experience. In the AI era, everything is questionable, and everything can be improved.

As marketing professionals we have two key courses of action:

1. Find concrete use cases and business problems we could best solve with AI. For
example, increase the Lifetime Value of the most valuable consumer segments, predict the Churn rate of our loyalty program members, or increase the recall of our campaigns.

2. Focus on getting not as much data or creative references as we can, but the minimal sufficient and reliable data, accompanied by the right parameters and examples to guide the machines and make their output as refined as possible.

Those who want to apply AI in all processes are likely to invest more than they generate in return.

Those who only witness this unprecedented technological revolution will most likely see their own business become obsolete.

Survival will be reserved for brands, agencies and marketers best able to adapt their thinking, and continue to find the balance between the human spark and machine driven power.

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