In 2024, "AI-powered" became the marketing claim every agency added to their homepage. Most of them mean they have a ChatGPT subscription. Here's how to tell the difference. And what genuinely integrated AI looks like in practice.
← Back to InsightsThey use ChatGPT to write first drafts of social captions. Sometimes blog posts. The output goes through minimal review and gets published.
The result is content that sounds like ChatGPT: generic, fluent, indistinguishable from a thousand other pieces of AI-generated content, and ranking for nothing in search. Google has become increasingly effective at identifying thin, low-value content. Volume without quality signals isn't a strategy; it's noise.
Using AI to generate copy you publish directly is not AI-powered marketing. It's AI-generated content with a human skimming it before posting. There's a meaningful difference between using AI as a production tool inside a rigorous editorial process and outsourcing your content operation to a language model with no brief, no brand voice and no quality standard beyond "sounds fine".
The agencies that add "AI-powered" to their homepage and mean this are not lying, exactly. They're just describing something much smaller than the phrase implies.
There are specific places where AI creates genuine leverage in a marketing workflow. They're worth naming precisely, because the vagueness around this topic is exactly the problem.
Campaign optimisation. Google's Smart Bidding and Meta's Advantage+ algorithm are both AI systems. They process signals at a scale no human media buyer can replicate, adjusting bids, targeting and placements in real time based on conversion probability. The skill isn't using these systems; it's knowing how to configure them correctly, set the right objectives, govern their decisions and prevent them from optimising toward the wrong outcome. An agency that understands this is doing something meaningfully different from one that turns on automated bidding and calls it AI.
Keyword research. AI tools cluster search intent at scale, identify semantic relationships between terms and surface low-competition opportunities faster than manual research. A process that used to take two or three days can be done in a few hours with better coverage. The human still has to interpret the output and make editorial decisions. But AI dramatically accelerates the research phase.
Content production. AI accelerates drafting and variation testing. The human directs the strategy, sets the brief, reviews the output and makes it sound like an actual brand. This is not replacing the content process; it's compressing the time it takes to get from brief to editable draft.
Audience research. AI analysis of customer reviews, competitor content and search data surfaces the language and pain points real audiences use. Language that manual analysis would miss or take much longer to find. This feeds directly into copy, messaging hierarchy and ad creative.
Performance analysis. AI surfaces anomalies and patterns in campaign data that a human analyst reviewing a spreadsheet would miss, or find three weeks later when the damage is already done. Faster identification of what's working and what isn't means faster iteration.
AI language models are trained to be plausible, not accurate. They hallucinate statistics. They produce confident-sounding sentences about things that aren't true. They default to the average of their training data. Which means, structurally, they produce average content.
For a brand that wants to be distinctive, useful and authoritative, AI-generated content that hasn't been heavily edited is a liability, not an asset. It reads like AI content because it is. It carries the hallmarks of the model's defaults: the same sentence structures, the same hedging phrases, the same vague generalities dressed up as insight.
More concretely: if an agency is producing eight blog posts a month using unedited AI output, they are publishing eight pieces of content that dilute rather than build your authority. Search engines are getting better at identifying this. More importantly, your actual readers are getting better at identifying it too.
The agencies that use AI responsibly use it to accelerate production of work that's then reviewed, directed and improved by someone who understands the brief, the brand and the audience. The AI handles the structural draft. The human handles the thinking, the voice, the specificity and the accuracy check. That combination is faster than writing from scratch and better than publishing the AI draft unedited.
It looks different depending on the service. That itself is a signal. If an agency describes their AI use the same way regardless of whether you're asking about paid search, content or social, they're giving you a talking point rather than a workflow description.
In paid media: AI governs bidding, targeting expansion and creative testing, with a human setting the strategy, reviewing performance weekly and making decisions about what to test next. The AI optimises within parameters the human sets. The human adjusts those parameters based on business context the AI doesn't have access to.
In content: AI produces structured first drafts from detailed briefs. A human adds the voice, the specificity, the opinion and the accuracy. What gets published has been through an editorial process. It doesn't read like AI because it isn't, by the time it's published.
In research: AI processes large data sets (search terms, competitor content, customer feedback) to surface insights. A human interprets and acts on them. The AI handles the volume; the human handles the judgment about what matters and why.
In reporting: AI flags anomalies and generates narrative summaries of performance data. A human contextualises them against business goals and recommends what to do next. The report becomes a decision-making tool rather than a data dump.
The through-line is the same across all of these: AI handles volume and pattern recognition. Humans handle strategy, quality and judgment. Neither replaces the other. The value comes from the combination.
"Can you show me exactly where AI sits in your workflow for this specific service?"
An agency that genuinely uses AI well can answer that question in specific, concrete terms. They can tell you which tools they use, what inputs they provide, what the human review step looks like and what the quality standard is for output that gets published or acted on. They can describe the process before the AI step and after it.
An agency that can't answer that question in detail, that gives you a vague answer about "leveraging the latest AI tools" or "using AI throughout our process," is telling you everything you need to know. The vagueness is the answer.
The same applies to any vendor, any platform, any tool claiming AI capability. Specificity is the test. What does the AI do? What does the human do? What is the quality gate between AI output and published or deployed work? If those questions don't get specific answers, the claim isn't worth much.
AI is genuinely useful in marketing when it's used for the right things, in the right places, with the right human oversight. That's a narrower claim than most agencies are making. But it's an honest one.
Mode uses AI as a production multiplier and optimisation tool. Not as a content machine running unsupervised.
SEE HOW MODE USES AI