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2026-02-17

Can AI Fully Automate Marketing Translations Yet? Insights From the Latest Research

AI can create content at scale. The question is whether it can protect your brand, trust, and performance in multiple languages.

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AI Marketing Automation Isn’t "All or Nothing"

AI marketing automation is already here - and it’s easy to get swept up in the hype. But how far can AI really take marketing teams today - and what still needs human judgment to protect targeting, credibility, and performance (especially across languages, SEO, and GEO)?

At Acolad, we're lucky to have an expert in multilingual AI, our Global Solutions Manager Leena Peltomaa, who has set out to academically tackle these crucial questions.

At the Haage-Helia University of Applies Sciences, Leena thoroughly investigated the topic in her thesis AI and the Generation of Marketing Content – A Comparative Study of Certain AI Writing Assistants

In her primary research, she put widely used tools like Jasper, Writesonic, Grammarly, and ChatGPT to the test with a very marketing-shaped question: can they produce multilingual content that’s not just readable, but actually fit for SEO and GEO.

More than 90% of surveyed marketers had used AI for content creation - and almost all had tried ChatGPT. The reason is simple: it speeds up work.

But the same research also shows the limit. AI can draft fluent text fast, yet it often misses the ingredients that make marketing work: targeting, credibility, and a tone people trust. That gap matters even more when you publish in multiple languages and compete in both classic search (SEO) and generative answers (GEO).

Here we'll investigate more of Leena's key findings - and what they mean for marketing and localization leaders deciding how far to automate.

Key topics covered:

What the research tested (and why it’s a realistic benchmark)
How marketers use AI today - and what they don’t trust it to do
Where AI performs well, and where it breaks marketing quality
Multilingual output, SEO, and GEO: what’s still hard to automate
A practical operating model for AI + human content at scale

What the Research Actually Tested

A Realistic use case: Publish-ready Marketing Content

The thesis asks a practical question: How well can AI writing assistants create multilingual marketing content that is SEO- and GEO-optimized? That’s a higher bar than “can it write a paragraph.” It’s closer to what your team actually needs when you ship campaigns across markets.

To make the testing realistic, prompts were used that mirror common B2B demands: authoritative tone, buyer-specific targeting, trust building, and a clear CTA. The evaluation also included multiple languages (English plus Finnish, Swedish, and German), because global marketing rarely lives in English-only.

The Tools and Data Sources

The study compared three dedicated AI writing assistants - Jasper, Writesonic, and Grammarly - and used ChatGPT as a general-purpose baseline.

The findings come from four angles: hands-on output testing, a LinkedIn survey (18 international marketers), four semi-structured interviews, and analysis of user reviews on platforms such as Capterra and G2. It’s a snapshot of what worked in practice, at the time of testing.

How “Good Content” Was Judged

The tools weren't scored on grammar alone. Marketing quality was assessed using:

The 4Ws framework (who, where, why, what) to check positioning clarity
Depth and impact (moving past surface-level “feature + adjective” copy)
Google’s EEAT thinking (experience, expertise, authoritativeness, trustworthiness

Those lenses match how buyers decide and how search ecosystems reward content.

How Marketers Are Using AI Today

AI Is Mainstream - But Mostly for Drafts and Ideation

Across the survey and interviews, a clear pattern emerged: AI is used heavily, but rarely trusted as the final voice. Marketers described AI as an everyday tool—almost like an extra team member—yet they still expect to edit before publishing.

That’s not reluctance. It’s quality control. Most teams want speed, but not at the expense of brand credibility.

"AI tools are used more in the ideation phase than for actual content generation. Marketers often prefer to create their own prompts and to fine-tune the process, for instance by adding brand guidance as context."

"You always have to localize and review - you never can fully trust it"

Speed Is the Big Win

The strongest “yes” in the data is about efficiency. Marketers use AI to move from a blank page to a workable first draft in minutes, then generate multiple angles without rewriting from scratch.

That speed becomes even more valuable in real workflows - when a product message shifts, legal feedback lands late, or sales asks for a sharper value prop. Instead of restarting, teams use AI to spin up new versions for different audiences (for example, procurement vs. technical buyers), channels (landing pages, email, paid social), or even small experiments like headline and CTA variations - so the campaign keeps moving while humans focus on the final judgment calls.

"Speed is definitely the biggest win for me. These tools allow us to quickly bring ideas to life, test different approaches, and adjust on the fly. That kind of flexibility is a huge time-saver and really supports creative workflows."

Prompting Skill Is the Hidden Dependency

The research also highlights something many leaders underestimate: output quality depends on user skill. Several respondents stressed that prompting and real-world domain knowledge are essential. Without strong context, AI falls back to generic patterns.

So, while AI can reduce writing time, it can also increase “review time” if your team lacks prompting maturity or subject expertise.

"The user has to master prompt engineeering and know the real-world context and facts in order to be able to evaluate the usefulness of the output."

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Where AI Performs Well in Marketing Workflows

Drafting in English Is Generally Strong

In the study, AI writing assistants (and general-purpose chatbots) were relatively good at producing fluent, readable English. That’s why they’re useful for first drafts, outlines, and campaign variations.

This strength also explains why adoption rose so quickly: if you’re creating high volumes of content, a solid draft generator changes your throughput.

AI performs well at generating English-language content, but the content still needs editing to perform well as marketing content. Many respondents mentioned that without editing, AI outputs feel high-level, generic and not tangible enough for their target audiences.

Proofreading and Polishing Can Be a Real Productivity Gain

Grammarly stood out in survey feedback as a practical support AI tool for review and refinement - especially among native English speakers and professional copywriters.

This aligns with how many mature teams use AI: not as a replacement writer, but as an accelerator for editing, consistency, and clarity.

"The majority of the native English speakers and professional copywriters saw it [Grammarly] as a good or very good tool."

Where “Full Automation” Breaks Marketing Quality

Generic Output Is the Recurring Failure Mode

The most common issue in the tool tests is not broken language. It’s generic marketing language that sounds plausible but says little. The thesis describes repeated “AI mannerisms,” vague adjectives, and formulaic structures that reduce reader appeal.

In a B2B setting, that’s dangerous. Generic copy doesn’t just fail to persuade. It can signal low expertise.

"Raw AI-generated content frequently shows AI mannerisms in style and often remains on a high level. Hence, the output, typically, lacks the trustworthiness and authoritativeness that would be important for well-performing marketing content."

Targeting and Positioning Are Harder Than Phrasing

One of the most valuable insights is how often AI misses the “who” and “why” in positioning.

AI can usually describe what a product or service does. But it often struggles to speak to a specific buyer intent, anticipate objections, and prove credibility with tangible evidence.

In one example cited in the testing undertaken within the study, even when the prompt specified procurement professionals as the target audience, the output often stayed broad and non-committal.

Tone of Voice Is Still Hard to Automate

Interviewees consistently described tone-of-voice as a pain point—especially when brand voice is distinctive or culturally grounded. One quote captures it perfectly: if your tone is something like a “knowledgeable tractor mechanic next door,” you can’t rely on AI to reproduce it without heavy human shaping.

This is the core limit of automation: brand voice is not just word choice. It’s judgment.

"They all [AI models] lack nuance, can be stylistically rigid, and tend to 'overwork' e.g. applying tone of voice to copy: these tools will most often provide a more aggressive or enthusiastic application of the guidelines in ways which a skilled human writer would not."

SEO and GEO Support Is Uneven

The research shows mixed confidence in AI for Search Engine Optimization (SEO) or Generative Engine Optimization (GEO). Roughly 60% of survey respondents saw AI as useful for optimization, while about 40% did not.

In tool testing, some assistants could propose reasonable keyword ideas. But the deeper work - search intent alignment, topic selection, proof-building, and local-market keyword mapping - still required human strategy and validation.

Writesonic is a good example of the trade-off. Its structured workflow and “humanizing” features improved the feel of a blog draft, and it added external sources to strengthen credibility. Yet it also produced operational issues: it struggled with length limits, could be slow, and some keyword suggestions were irrelevant.

"When tested [...] identified SEO keywords and approaches to GEO optimization were far from convincing. The lack of cultural awareness and context was clearly visible in AI outputs. AI does not, in other words, remove the need for proper SEO/GEO research by a human marketer, even if it can make the process significantly faster."

Multilingual Marketing: The Automation Gap Widens

English Is Strongest; Other Languages Are Less Reliable

A consistent finding is that output quality drops outside English. And the problem is not just grammar. It’s commercial naturalness: phrasing that feels translated, tone that doesn’t land, and terminology that doesn’t match local expectations.

In the tool tests, multilingual versions often required extensive editing before they would be publish-ready, and respondents had strong doubts about the quality of automated translation tools integrated with AI writing assistants.

The translations of the AI-generated content frequently showed clumsy, non-fluent and non-natural wordings and grammatical structures. Most paragraphs would require extensive editing to perform well.

General-Purpose Models May Translate Better, but Don’t Solve Content Quality

Interviewees often preferred general-purpose models such as ChatGPT (and mentioned Claude and Gemini positively for translations). In testing, ChatGPT produced relatively natural translations compared to some specialized assistants.

But the bigger point remains: better translation does not automatically mean better marketing. If the original content is generic, translating it well still leaves you with generic content - just in more languages.

Why Localization Can’t Be “One Prompt → 20 Markets”

Multilingual marketing is not a pure translation task. Different markets respond to different claims, different proof, and different levels of directness.

This is where a hybrid model becomes a performance strategy, not just a safety net. Human experts validate whether the message is persuasive in that market, whether claims are compliant and appropriate, and whether keywords and topics match local search behavior.

"AI tools may ignore some specifics in the prompt. Important traits of well/performing content marketing are usually missing in the outputs: the result is not as credible, deep and targeted as marketers would hope. The typics solution to this issue is to engineer better prompts and to provide the models with more context [...] but sometimes that is not enough."

A Practical Operating Model for AI + Human Marketing Content

So what does this look like in practice? The research points to a simple pattern: let AI do the heavy lifting on speed and structure, then use human expertise to lock in accuracy, proof, and brand voice.

The lessons below translate those findings into practical steps marketers can apply across content types and markets.

Start With a Clear Definition of “Publish-Ready”

If you want automation, define the finish line. In the research, quality came down to credibility, targeting, and authority -not just readability.

For many teams, “publish-ready” means the draft must pass a few simple checks: is the buyer and intent unmistakable, does the copy include tangible proof, does it sound like your brand, and can you stand behind every claim

Build Guardrails That Reduce Rework

The research shows that AI drafts drift without context. That’s why guardrails matter.

Give the model your brand rules. Provide target audience details. Add required proof points. Then enforce human review where it matters most: regulated claims, technical accuracy, and market nuance.

Measure What Matters and Don’t Confuse Volume With Impact

One important point in the thesis is worth highlighting: AI can increase output volume, but quality can decline if humans stop reviewing. If that trend grows, content fatigue grows with it.

A better approach is measurement that reflects outcomes: revision rate, time-to-publish, organic visibility, qualified traffic, engagement, conversions by market, and brand consistency across languages.

So, Is AI Ready to Fully Automate Marketing?

Automate the Repeatable. Protect the Differentiating.

The research doesn’t argue against AI. It argues against over-trusting it.

AI writing assistants are already valuable for speed, drafting, and iteration. But in this snapshot, they were not consistently able to meet the full standard of strong marketing content—especially in multilingual contexts and in credibility-driven B2B use cases.

The near-term winning model is clear: AI for scale, humans for trust. That’s how you move fast without sounding generic, risking compliance, or weakening your brand in global markets.

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Can AI Fully Automate Marketing Translations Yet? Insights From the Latest Research