Why Shiny Tools Keep Replacing Understanding

Marketing has developed a persistent habit of mistaking novelty for progress.

Right now, nearly every serious marketing conversation ends at the same promise: large language models will fix relevance, AI-driven personalization will fix engagement, automation will close the gap between insight and execution. Salesforce, Adobe, Google, and every major platform reinforce the idea that understanding your audience can be licensed, integrated, and scaled.

Yet the fundamentals remain strangely undefined. A recent McKinsey survey of 233 senior marketing and tech leaders found that few could clearly quantify the ROI of their martech spend, yet over a quarter still planned to increase investment by up to 25% in the next 3–5 years.¹ In other words, organizations keep piling on new platforms and features based on hype or FOMO, without pausing to define success upfront. The result is predictable: bloated stacks, wasted time, and “progress” that mostly shows up as new dashboards.

It is a convenient story. It demos well. It turns uncertainty into capability and hard questions into procurement decisions. Most importantly, it allows marketing to sidestep the uncomfortable reality that many of its persistent problems were never technical to begin with.

What rarely gets examined is why this cycle repeats even when the promised gains fail to appear.

When Tools Replace Responsibility

The explanation is straightforward, even if it is uncomfortable. Shiny tools make selling easier. Procurement cycles, vendor incentives, and internal signaling often reward adoption and visibility over outcomes, making tool acquisition feel like progress even when nothing meaningful changes. Tools smooth conversations. They spare everyone from having to articulate difficult truths about audiences, tradeoffs, and uncertainty. Once a platform becomes the centerpiece, clarity about who the work is for and why it matters fades into the background.

Capability quietly displaces understanding.

The consequences show up quickly and repeatedly. Organizations accumulate sprawling stacks that no one fully understands. Teams spend more time maintaining systems than making decisions. Dashboards fill with metrics that describe motion without producing clarity. Marketing grows louder, faster, and more complex, while becoming less useful to the people it is meant to reach. For audiences, this shows up as more noise, less relevance, and a growing sense that marketing is something to tune out rather than engage with.

The Hidden Cost of Capability

Technology itself is rarely the problem. Many of these tools are sophisticated and well built. The problem emerges when they are asked to stand in for judgment.

Understanding people resists scale. It moves slowly. It depends on attention to real behavior rather than reported intent. It comes from listening to sales calls, sitting through reviews, watching someone struggle with a form, noticing where confusion appears, and where enthusiasm fades. It requires ownership of the outcome, not just participation in the process.

Activity Is Not Insight

Measurement explains what happened. It struggles to explain why. Optimization becomes powerful only after the problem has been clearly defined. Otherwise, optimizing a flawed process just helps you mess up faster.

As one MarTech commentary put it, “automation only accelerates what already exists.” If your underlying marketing process or targeting logic is shaky, technology will simply “help you fail sooner and stronger”.² In the context of marketing or behavioral design, this means rushing to A/B test, personalize, or “optimize” campaigns without truly understanding the customer or problem can amplify poor decisions. Dashboards may turn green and tasks get faster, but you might just be automating bad assumptions; for example, spamming customers with AI-generated content that actually undermines sales. The lesson: without strategic clarity and human insight, more tech speed just accelerates the wrong outcome.

That judgment remains a human responsibility.

Judgment Comes From Consequences

Judgment grows out of experience. It is pattern recognition built over years of seeing similar ideas succeed in one context and fail in another. It is knowing when a number is technically accurate but practically misleading. It is recognizing when simplification will outperform sophistication for a specific audience, at a specific moment, under specific constraints.

Constraints deserve more respect than marketing usually gives them: regulation, budget, organizational reality, attention span. Technology often promises to dissolve limits. In practice, limits reveal priorities. They force choices. They make tradeoffs visible. When everything appears possible, clarity disappears.

Distance from consequence distorts judgment in especially visible ways with LLMs. People closest to the work tend to be the least impressed. Engineers notice confident but incorrect code. Marketers notice generic language that collapses brand voice. Subject matter experts see shallow pattern matching where real understanding is required. Those further from outcomes often see only fluency and speed, and mistake those qualities for insight.

This divide shows up repeatedly in research and practice. In surveys, a majority of marketing professionals report that AI generated content falls short of human work in quality and authenticity.³ In technical communities, platforms like Stack Overflow have banned LLM generated answers because they appear plausible while being wrong, overwhelming experts tasked with correcting them.⁴ Studies across finance, medicine, and knowledge work consistently show the same pattern.⁵ Novices over trust AI recommendations, while experts override them quickly.⁶ What looks impressive from a distance looks fragile up close.

Why Experienced Work Subtracts

Over time, experienced marketing moves against expansion. It subtracts: fewer messages, fewer segments, fewer platforms, fewer systems. Clarity tends to arrive through removal rather than accumulation, by eliminating what creates motion without meaning.

Technology earns its place only after conviction exists. Conviction here is not branding language. It is a grounded belief about what matters to a particular group of people and why. Once that belief is clear, the right tools become obvious and often unremarkable.

A simple test helps: can you name the decision this tool will change? Can you define success in behavioral terms, not just platform metrics? Can you identify the harm if you get it wrong (cost, trust, churn, regulatory risk)? If the answers are vague, the tool is not a strategy. It is a substitute for one.

Systems that do not shorten decision cycles, reduce harm, or improve understanding rarely justify their complexity.

Lessons Learned the Hard Way

We have lived these dynamics ourselves. We have adopted tools that promised insight and delivered noise. We have learned, sometimes slowly, that adding capability without sharpening judgment increases complexity rather than clarity. The improvements that lasted were rarely the most exciting ones. They were the ones that made the work easier to understand, easier to explain, and easier to own.

The Unfashionable Advantage

Resisting hype carries an unfashionable advantage. Teams that prioritize attention over novelty build systems they actually understand. They make decisions they can defend. They treat marketing as a human practice first and a technical one second. Over time, that restraint compounds.

Technology will continue to advance. That much is certain. The real question is whether marketing will keep using shiny tools as substitutes for understanding, or whether it will insist that tools earn their role by serving it.

Marketing has no shortage of capability. What it lacks is sustained attention.

References

1. McKinsey & Company, Rewiring Martech: From Cost Center to Growth Engine (October 22, 2025)
   https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/rewiring-martech-from-cost-center-to-growth-engine
2. MarTech.org, Automation Doesn’t Fix Broken Processes (V. Ceric, 2023)
   https://martech.org/why-process-only-or-tech-only-fixes-never-solve-your-toughest-problems/
3. Salesforce, State of Marketing Report (9th Edition)
   https://www.salesforce.com/ap/resources/research-reports/state-of-marketing/
4. Stack Overflow: Temporary Ban on ChatGPT Generated Answers
   https://www.businessinsider.com/mckinsey-study-cmo-cant-measure-roi-martech-stacks-2025-10
5. Micaela, et al., Trust and reliance on AI — An experimental study on the extent and consequences of AI advice, Computers in Human Behavior, 2024.
   https://www.sciencedirect.com/science/article/pii/S0747563224002206
6. Gary Marcus, essays and commentary on LLM reliability
   Summary of Marcus’s published views on generative AI limitations and “fluent but unreliable” output