Why Marketing AI Tools Are Creating More Problems Than Solutions – News Round Up: 09/12-09/19

by Jeff Licciardi

Marketing AI Tools Are Creating More Work, Not Less – News Round Up: 09/12-09/19

Let’s cut through the noise: marketing AI tools were supposed to free up our time, reduce costs, and unlock creativity. Instead, they’re spawning new compliance headaches, requiring specialized talent we don’t have, and producing work that still needs extensive human oversight. This week’s industry coverage reveals a pattern that vendors don’t want to admit—AI adoption is creating a second shift of work, not eliminating the first one.

According to Digiday’s latest research, while marketers are increasingly warming to AI tools, creative output challenges and mounting legal risks are dampening enthusiasm. The real story isn’t that AI doesn’t work—it’s that it works just well enough to create new categories of problems we’re now responsible for solving.

The ROI Mirage: Where AI Actually Delivers (And Where It Doesn’t)

When Ad Age investigated where marketers are actually seeing AI ROI, the results were telling. The wins are concentrated in narrow, repetitive tasks—media optimization, basic personalization, performance analysis. These are important, but they’re not the transformative use cases vendors promised.

The problem compounds when agencies get involved. Ad Age’s coverage of AI in ad tech and agencies reveals a fragmented landscape where every player is bolting AI onto existing offerings without solving for integration. Marketers now face a Frankenstein stack of AI-powered tools that don’t talk to each other, each requiring its own learning curve, data feeds, and governance protocols.

This creates what I’m calling “AI overhead”—the additional labor required to prompt, review, edit, fact-check, and legally clear AI-generated work. One creative director told me their team now spends 40% of their time “AI wrangling”—time that used to go toward actual creative development. That’s not efficiency; that’s shuffling deck chairs.

Platform Plays: When AI Becomes a Walled Garden Strategy

X’s Elon Musk recently outlined an AI-led Grok future for advertising on the platform, and it’s a masterclass in how platforms use AI to tighten their grip. The pitch is seductive: let our AI handle everything from creative to targeting to optimization. The reality is vendor lock-in wrapped in a machine learning model.

Even OpenAI, which initially positioned itself as the democratic AI for everyone, has had a complete change of heart about advertising. The company went from vocal opposition to building an ads business—because that’s where the money is. This isn’t about making marketing better; it’s about capturing budget.

Google’s latest move is particularly instructive. Their AI Mode ad launch effectively creates a new ad format that only exists within their AI-powered search experience. Brands now need to optimize for traditional search, voice search, and AI-mediated search—each requiring different strategies, creative approaches, and measurement frameworks. That’s three times the work for access to the same audience.

The Retail Media Reality Check

While AI dominates headlines, retail media is quietly experiencing its own maturation crisis—one that mirrors the AI challenges. Digiday reports that “the easy dollars are gone” from retail media as the channel faces new tests approaching maturity.

The pattern is identical: early adoption delivered quick wins, but scale reveals complexity. There’s a point of diminishing returns, and many marketers are hitting it. Managing campaigns across Amazon, Walmart, Target, Kroger, and dozens of smaller networks requires specialized knowledge, different reporting standards, and increasingly sophisticated attribution models.

Retail media is literally reshaping marketer organizations, forcing companies to hire specialized roles and build new capabilities. Sound familiar? It’s the same story as AI—a promising channel that creates organizational complexity at scale.

What This Means for Marketing Teams

The uncomfortable truth is that modern marketing requires more specialized knowledge, not less. AI tools don’t eliminate the need for expertise—they change what expertise you need. You still need great strategists, creatives, and analysts. But now you also need prompt engineers, AI governance specialists, and people who can audit algorithmic outputs for bias and accuracy.

The same applies to emerging channels. CTV, retail media, and AI-powered platforms each promise efficiency but deliver complexity. CTV keeps growing, which means more fragmentation, more buying platforms, and more reporting to reconcile.

The winners in this environment won’t be the teams that adopt every new tool. They’ll be the teams that ruthlessly prioritize—choosing fewer tools, mastering them completely, and building processes that actually reduce work rather than redistribute it. That means saying no to shiny objects, even when vendors promise revolutionary results.

The Path Forward

Here’s what smart marketing leaders should do: audit your current AI and martech stack not for what it promises, but for what it actually delivers relative to the time invested. Kill anything that hasn’t demonstrably reduced workload or improved outcomes after six months. Consolidate where possible. Build expertise in depth rather than dabbling widely.

And for the love of efficiency, stop letting vendors set your agenda. The fact that a new AI tool exists doesn’t mean you need it. The fact that competitors are using it doesn’t mean it’s working for them. Most marketing technology adoption is driven by FOMO and vendor pressure, not strategic necessity.

The next year will separate the organizations that use AI strategically from those drowning in AI overhead. The difference won’t be how many tools you’ve adopted—it’ll be how many problems you’ve actually solved.

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