The most significant changes AI is bringing to international marketing production are not about speed and cost. The efficiency gains are real, but the capability gains are where it's getting very interesting.
Here are some of the things that have shifted across production stages at Freedman.
Translation now runs as a two-stage process. AI handles the first pass; a human post-editor refines and corrects. That has cut translation timelines by almost half while quality has stayed consistent, because the review layer is still in place.
The quality of the output still depends directly on what the model is trained on. A 2026 Appen study testing seven leading language models across 15 language-locale combinations confirmed what we are seeing in our own multi-model testing and continue to see in practice: AI fluency is no longer the problem but cultural nuance still is. That is why every prompt we run carries not only the market-specific rules and client guidelines, but also the client's translation memory and brand glossary. With the right knowledge in place, the human post-editor can focus on fine-tuning the details rather than starting from scratch.
Traditional automation required precise, rigid inputs. If a text string was five words in one language and ten in another, the tool would paste in the ten-word version and leave the layout broken. What changes with AI is the way it handles ambiguity. It understands context well enough to make sensible decisions about line breaks, truncation and format constraints without being given an explicit rule for every scenario.
We have built an internal tool at Freedman that uses AI to interrogate a brief, structure the information and generate deliverables lists for complex multi-market campaigns. What used to take days takes hours.
A growing proportion of the voiceovers Freedman produces are now generated in-house using AI tools. Our producers direct the AI model, applying editorial judgement they have developed from years of briefing and reviewing talent. For many commercial use cases, the quality stands on its own, and is getting better year on year.
Working with lip-syncing tools, we have built a workflow that takes this further. AI lip-syncing adjusts the lip movement of on-screen talent to match the local language voiceover. Previously, this kind of task would require weeks of extensive VFX work and consume significant localisation budget, so it was rarely considered. Now, it can be completed in around two hours. Together, AI voiceover and lip-syncing mean a video asset can carry a global campaign message while feeling made for each market.
Using generative imagery tools, locally relevant visuals can now be produced at scale, built to brief, without a photo shoot or stock licensing. Rather than a generic visual carrying the right language, clients running assets across multiple markets can generate imagery that reflects a particular market: ethnicity, clothing, surroundings and landscapes. For example, this could look like swapping obvious markers of US-originated creative, such as traffic lights, for local market equivalents. It removes a cost and time barrier that previously put this level of local relevance out of reach for many brands.
AI is only as good as the insights and information it is given. What that means in practice for global marketing production is that the human input required to make AI work well is still significant.
Feed it a brief that says "make this feel German" and it will default to the dominant cultural averages of its training data: broadly accurate for a generic audience, wrong for the specific one you are trying to reach. Training data will always be a step behind real human culture. The knowledge architecture around the tool, translation memories, audience profiles, brand voice documentation, market-specific rules, still requires genuine market understanding to build and maintain.
The work has shifted, not disappeared. The teams that will get the most from AI are the ones investing in what goes in, not just the tools themselves. Knowing precisely what the tool needs and feeding it with the right inputs to get results that actually work.
Production volume has increased, but review and approval processes, in most organisations, have not kept pace. That gap is where quality failures form: brand consistency drifting market by market, timelines being compressed, checks being skipped.
The fix is to redistribute where human attention sits. Front-load the knowledge input and boundary-setting when the campaign is still in development. Use AI within the QC layer to flag what falls outside agreed parameters: brand rules, market requirements, regulatory constraints. Have human reviewers focus on exceptions rather than every asset. When that infrastructure is in place, the production cycle can move at the speed AI makes possible. When it is not, faster production compounds the problems rather than removing them.
Regardless of how a team is organised, the campaigns that hold quality at scale consistently have one thing in common: a single person or function with clear authority and accountability across the full production chain.
AI accelerates production significantly, but it does not replace the judgement calls about what gets prioritised, what gets escalated, and what gets stopped. It does not manage the relationship between global, regional, and local stakeholders. It does not resolve the tension when a market pushes back on a creative decision at the approval stage. Without a central lead who can make those calls and is accountable for the outcome, faster production creates more coordination problems, not fewer. That accountability structure is a human design decision, and AI makes it more not less necessary.
AI has changed more than the speed of international marketing production. It has changed what is possible. AI voiceover, lip-syncing adapted to local language, locally generated imagery at scale: services that were not commercially viable two years ago are now part of what a well-run international campaign operation can deliver. What has not changed is the human input required to make those tools perform: the market knowledge, the governance infrastructure and the central accountability that determines whether the output actually works in each territory. The brands getting the most from this new wave are the ones investing in the infrastructure supporting AI and humans to perform at their best.
AI tools are accessible to every team now. The gaps that hold performance back are in the infrastructure around them. Freedman's Global Campaign Health Check identifies where those gaps sit in your operation. Start here.
AI is changing international marketing production on two levels. The first is efficiency: translation timelines cut by almost half, briefing and versioning work running considerably faster, deliverables generation automated for complex multi-market campaigns. The second is new capability: at Freedman, AI voiceover now handles a significant and growing proportion of commercial use cases, AI lip-syncing has moved from a task that was rarely commercially viable to a two-hour workflow, and generative imagery allows locally relevant visuals to be produced at scale without a photo shoot. The capability shift is the more significant of the two.
Three things remain unchanged regardless of how much AI a team adopts. The human knowledge input required to make AI perform: market context, audience specificity, brand rules and translation memory that have to be built and maintained by people with genuine market understanding. The review and approval infrastructure, which has to be redesigned to match the volume AI now makes possible, not left as it was. And the need for a central lead with authority and accountability across the full production chain. AI accelerates what a well-structured operation can do. It does not substitute for the structure itself.
AI has opened service categories that were previously too expensive or too slow to offer commercially. AI lip-syncing, voiceover at commercial quality, and generative imagery at scale are now deliverable without the cost structures that previously made them impractical for most campaigns. That makes these capabilities accessible to brands that would not previously have had access to them. The quality of what any team produces using these tools depends on the knowledge and production architecture built around them. The tools are widely available. The infrastructure that makes them perform well is not.