Why AI Spending Isn’t Boosting Marketing

A recent GrowthLoop survey highlights a critical disconnect in how companies invest in AI for marketing, revealing why significant spending isn’t yielding expected results.

The main issue, termed the “Information Asymmetry Loop,” shows executives and marketing teams operating with mismatched priorities.

While 51% of executives believe marketing cycles are fast, only 28% of non-executive marketers agree, exposing a gap that misdirects AI investments toward flashy tools like personalization engines and content generators, rather than addressing operational bottlenecks.

This misalignment stems from executives focusing on strategic metrics, such as conversion rates, which obscure inefficiencies like delays in data analysis or campaign execution.

As a result, investments prioritize visible AI solutions that are easy to showcase but fail to address underlying issues.

When these tools underperform due to unresolved bottlenecks, measurement systems struggle to pinpoint causes, perpetuating a cycle of poor ROI and further misaligned spending.

The survey suggests that companies with faster marketing cycles achieve better AI ROI, underscoring the need to prioritize operational efficiency.

The significance of this finding lies in its call for a strategic shift. Instead of chasing trendy AI applications, businesses should assess and resolve operational bottlenecks—such as slow data processes or manual campaign tasks—before investing in AI.

PwC’s 2025 research supports this, noting that while 49% of tech leaders see AI as integral to business strategy, only 33% integrate it into products and services, often overlooking its potential to streamline operations.

Fixing these “potholes” in marketing processes could amplify the impact of AI tools, delivering the strategic outcomes executives seek.

For businesses, realigning AI investments could enhance marketing efficiency, improve campaign velocity, and boost ROI.

For users, faster marketing cycles could mean more timely, relevant content and promotions. To break the cycle, the article recommends conducting bottleneck assessments, revising ROI metrics to value efficiency gains, increasing executive visibility into daily operations, and prioritizing AI tools that address upstream issues, like customer data management.

FAQ

Why aren’t AI investments improving marketing results?

AI investments often focus on visible tools like personalization, ignoring operational bottlenecks like slow data analysis, which limits overall effectiveness.

How can companies improve AI ROI in marketing?

Companies should assess and fix operational inefficiencies, align AI investments with these fixes, and track efficiency gains to ensure better ROI.

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