Marketers are using more AI, but their tech skills aren’t keeping pace

AI is moving deeper into everyday marketing work. That much is clear. The latest Digiday+ Research signals that marketers are increasingly using AI tools, even as a different part of the stack looks stuck: their technical skills.

That split matters more than it may sound. It suggests the industry is getting comfortable with AI at the surface level — for faster production, ideation, optimization, and workflow support — while still struggling with the deeper technical fluency needed to make those tools really deliver.

In other words, adoption is rising. Readiness is a more complicated story.

For adtech and martech teams, that gap is becoming harder to ignore. AI tools are relatively easy to trial. Teams can plug them into copy generation, audience insights, media planning support, reporting summaries, or internal process automation. But scaling that usage into something reliable, measurable, and well-governed is another job entirely.

That requires people who understand systems, integrations, data quality, experimentation, and how different parts of the marketing stack actually connect. If those skills are not advancing at the same pace as AI adoption, marketers risk building a shinier workflow on top of a shaky foundation.

The result is a familiar pattern: more tools, more use cases, and more pressure to show progress — without the internal capability always matching the ambition.

That does not mean marketers are getting AI wrong. It means the market is entering a more mature phase, where simply saying a team uses AI is no longer enough. The harder question is whether that usage is strategic, repeatable, and tied to real outcomes.

There is also a practical issue beneath the hype cycle. AI can lower the barrier for certain tasks, but it does not automatically replace technical understanding. If anything, broader AI access can make that understanding more important. Teams still need to know how to evaluate outputs, protect brand standards, manage data inputs, and connect AI-driven activity to measurement frameworks that leadership can trust.

This is especially relevant as marketers face ongoing pressure to do more with existing budgets and teams. AI looks attractive in that environment because it promises speed and scale. But speed without operational discipline can create new inefficiencies: messy workflows, inconsistent outputs, weak governance, and reporting that is harder to defend.

For employers, the signal is straightforward. Hiring and training plans may need to shift from generic AI enthusiasm toward more concrete capability building. That could mean stronger investment in data literacy, marketing operations, analytics, automation logic, and stack-level troubleshooting — not just tool familiarity.

For vendors, the findings point to a challenge too. Ease of use remains a selling point, but customers are likely to need more support around implementation, education, and integration if they want AI features to stick. A product that demos well is not the same as a product that a stretched marketing team can operationalize cleanly.

There is a broader industry implication here as well. The winners in the next phase of AI adoption may not simply be the teams using the most AI. They may be the teams that pair AI experimentation with stronger technical muscle — the ability to turn pilots into process, outputs into decisions, and automation into accountable performance.

The headline is not that AI momentum is fading. It is that the industry’s talent and skills model may be lagging behind the tools now flooding into marketing. AI adoption is easy to announce. Building the technical depth to use it well is the harder part — and likely the more important one.