The hype and the hesitation

AI has become the most over-promised marketing intern in history. Everyone’s hired it; few know what to do with it. The hype is relentless, automated ads, self-optimising content, predictive everything,  yet the reality is more hesitant. For most eCommerce marketers, the issue isn’t enthusiasm. Its execution.

Businesses start with grand intentions: automate the catalogue, generate smarter ad copy, predict what shoppers will want next Tuesday. But before long, the excitement turns to confusion. Teams are left tinkering with ChatGPT for meta descriptions and calling it digital transformation. Given the recent news over the overvaluation of AI companies and an imminent crash due to generative AI underperforming for business, we believe that more C-Suite executives will become sceptical. But, but - here’s the truth: 

AI requires the kind of discipline marketing isn’t famous for: clear goals, clean data, and cross-departmental buy-in. Without that foundation, the tools are just noise.

Where AI projects go to die

Across the industry, the same pattern repeats. Someone in senior management buys an AI tool, the team runs a few pilots, and the results are inconclusive. Nobody wants to say the project’s gone nowhere, so it lingers, half-used, half-understood.

It’s not that AI doesn’t work; it’s that it doesn’t know what you want. Without a testing framework, success metric, or integration plan, adoption fizzles. In time, “AI adoption” becomes a tick-box line in the strategy deck rather than a working advantage.

Turning theory into traction

At SearchUp, we’ve found the solution is translation. Not between languages — between curiosity and commercial logic. AI isn’t a silver bullet; it’s a signal finder. It shows you where the hidden efficiencies live: in keyword modelling, creative testing, content velocity, and predictive analytics.

Start small, measure ruthlessly, and stop pretending AI will replace the team. It won’t. What it can do is illuminate the 20% of activity that drives 80% of results. That’s where the ROI lives — in better focus, not more technology.

What the winners do differently

The successful AI adopters treat it as infrastructure, not magic. They define the question before the model. They test, adapt, and feed results back into their systems. They see AI not as automation, but as augmentation.

Because the question isn’t “what can AI do?” It’s “what’s worth doing at all?”

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