3. Choosing the Right AI: NMT, LLMs, and Transcreation for Marketing Content

As AI becomes part of everyday marketing conversations, many organizations feel pressure to “use AI” without a clear sense of what that actually means. Machine translation engines, large language models, and AI-powered transcreation are often grouped together, as if they were interchangeable. In reality, they solve very different problems.

For marketing localization, choosing the right AI is less about adopting the newest technology and more about matching the right approach to the right type of content. The teams that succeed are not the ones using the most AI, but the ones using it deliberately.

Why “Using AI” Is Not a Strategy

It is tempting to think of AI as a single solution that can replace traditional localization workflows. This usually leads to disappointment. Different AI technologies are optimized for different outcomes. Some prioritize accuracy and consistency, while others are designed for creativity and variation.

Without clarity on what you are trying to achieve—speed, brand impact, scale, or experimentation—AI choices quickly become reactive. Marketing leaders end up chasing tools instead of building systems that support real business goals.

Understanding the Three Main Approaches

Neural machine translation has been part of localization workflows for years. It excels at producing consistent, predictable output and works particularly well for structured or informational content. Product descriptions, support documentation, and UI-related marketing content often benefit from this reliability. When accuracy and terminology consistency matter most, NMT remains a strong option.

Large language models introduce a different capability. Rather than translating word by word, they generate content based on intent and context. This makes them especially useful for marketing copy that needs to sound natural, persuasive, or emotionally engaging. However, that flexibility also introduces variability, which requires stronger guidance and review.

Transcreation sits at the intersection of marketing strategy and localization. The goal is not to reproduce the source text, but to recreate its impact for a new audience. AI can support transcreation by generating multiple creative options quickly, but human judgment is essential to ensure alignment with brand voice and campaign objectives.

Each of these approaches has value. The challenge is knowing when to use which one.

Matching Content Types to the Right Technology

Marketing content is rarely uniform, and treating it as such often leads to mismatches between technology and purpose. High-volume content that prioritizes clarity and consistency tends to work well with NMT, especially when paired with light human review.

Campaign content, headlines, and calls to action require more nuance. This is where LLMs can add significant value, producing language that feels less translated and more locally authored. The trade-off is that these outputs need stronger oversight to maintain brand alignment.

For flagship campaigns or emotionally driven storytelling, transcreation remains critical. AI can accelerate the creative process, but humans must still make the final call. In these cases, AI is best viewed as a creative assistant rather than a decision-maker.

Making Decisions Based on Risk, Not Hype

One of the most practical ways to choose the right AI approach is to evaluate content through a risk lens. How visible is the content? How closely is it tied to brand positioning or legal claims? How tolerant is the organization of variation?

Low-risk content allows for more automation and experimentation. High-risk content demands tighter controls and higher human involvement, regardless of how advanced the AI may be. This framing helps teams avoid over-engineering simple workflows while protecting what matters most.

The Role of Human Judgment in Every Model

No matter which AI approach is used, human judgment remains essential. The role of humans is shifting, but not disappearing. Instead of focusing on manual translation, teams increasingly spend their time guiding AI systems, reviewing output, and making strategic decisions about quality and tone.

This shift requires new skills, including prompt design, quality evaluation, and cross-market alignment. Organizations that invest in these capabilities are far better positioned to scale AI responsibly.

The Real Takeaway

Choosing the right AI for marketing localization is not about picking a winner between NMT, LLMs, or transcreation. It is about building a flexible model that adapts to different content needs, risk levels, and business goals.

When technology choices are made deliberately, AI becomes an enabler of better marketing, not a source of inconsistency or risk. The most effective teams are not asking which AI is best. They are asking which AI is right for this content, in this market, at this moment.