Automotive & mobility companies are under pressure from two directions. On one side, it’s a tough market with increasing pressures to compete and to provide growing volumes of documentation across markets. AI brings a promise to scale localisation faster. On the other side, localisation in the automotive & mobility industry has never been just about speed. The content needs to be accurate, consistent, appropriate for the market where it will be used, as well as compliant with legal regulations.
A few years ago, most discussions focused on the promise of AI. Today, automotive leaders are asking: How do we use AI to reduce costs and increase return on investment without creating new risks or long-term challenges?
I have worked with automotive companies on localisation for many years and have seen the industry navigate both growth periods and challenging market conditions.
Through that experience, I've seen what works and where organisations often struggle when introducing new technologies like AI into their localisation strategy. In this article, I'll share my perspective on how to build an AI localisation strategy that delivers measurable value, the questions organisations should be asking, and why the right partnership often determines whether or not AI becomes a long-term success.
How to add the horsepower needed to turn AI into ROI
Automotive companies are under constant pressure to improve return on investment and reduce costs.
The challenge is not so much about getting the AI engine running - automotive companies are excellent at that. The challenge is building the systems, controls, and expertise needed to make it perform reliably at scale. The R&D you've invested in so far is valuable. Now you just need to bring the horsepower to make it work at scale!
The companies that achieve the best results focus on building a localisation ecosystem that can evolve alongside their business and the technology itself.
10 questions to test whether your AI localisation strategy is built to last:
How well does the solution fit into our existing ecosystem and technology stack?
Can it integrate seamlessly with our current systems and workflows?
Can it adapt as content volumes increase or decrease?
Can new technologies be introduced without redesigning the entire process?
What mechanisms are in place to monitor and maintain quality and consistency?
How is quality monitored, measured, and improved over time?
Can we adjust the balance between AI and human expertise depending on quality expectations?
How well does the solution support user and stakeholder requirements?
Does our partner have a clear roadmap for future development
Are we building a localisation setup that supports the business we expect to be in five or ten years from now, rather than only meeting today's needs?
The answers to these questions will often reveal more about future success than any individual AI technology.
Automotive teams need to make a clear plan of how they’re going to maintain the infrastructure, governance, and quality controls they’ve built. They need to be experts on AI localisation as well as their core business. And as we've all experienced with AI, consistency is not naturally its strong suit. AI sometimes struggles with hallucinations - and you need to control it carefully. Changes in technology or environment can alter how well your model performs, even if performance was initially strong. Evolving content volumes may also put pressure on the model.
You can improve precision by training the model, but you can also bring in localisation technologies such as termbases and translation memories.
In a nutshell: Maintaining AI models with the right expertise and building all that linguistic experience completely in-house is difficult when localisation is not part of your automotive core business.
My advice is, if your goal is to reduce costs and increase ROI, managing AI localisation internally is not the most effective approach.
How to reduce the risk of AI in localisation
When automotive companies tell me they are concerned about AI, the concern is rarely about AI itself.
More often, it comes down to a lack of visibility. Leaders want to understand how quality is measured. They want confidence that terminology is being used correctly and assurance that regulatory and compliance requirements are being respected.
They want to know how decisions are made inside the workflow and whether adjustments can be made when requirements change.
This is also why localisation should be viewed as a partnership rather than a transaction. Success depends on having access to specialists who understand the automotive sector and can help manage complexity over time.
When markets, technology and content volume inevitably shift over time, it’s important to be able to move with the current without falling behind.
The value of having the right partner
The successful automotive companies I work with don't want localisation to become another area they need to manage internally.
Automotive managers have to keep on top of a myriad of challenges. They have to understand how the EV market is evolving in comparison to combustion engines, so they can make the best sales strategy possible. They have to tackle geopolitical uncertainties that may change almost daily and can seriously affect their capability to have an impact on global markets. My point is: Automotive leaders already have plenty on their minds. Localisation is usually not their first priority.
That's why many leaders want a partner that can take responsibility for AI and continuously develop it as a cost-saving and ROI-driven function within the business.
Automotive manufacturers should be able to fully concentrate on building cars while their localisation partner delivers the full package of what is needed to communicate globally. The role of the automotive team should be to occasionally review the results and confirm that they made the right decision.
In the end, senior management does not want to hear complaints about translation quality or localisation processes. If the phone stops ringing and no one is raising issues anymore, then the localisation partner is doing a brilliant job. That's the benchmark. They are not just looking for a better workflow or CMS. They're looking for a solution that will help them stay competitive, also ten years from now on.
AI is an important part of that equation, but AI alone is not the answer. What creates long-term value is the combination of technology, proven workflows, quality management, linguistic expertise, and years of experience. Those capabilities cannot simply be replaced by the latest AI model or an AI agent running in the background.
If you'd like to discuss how to best bring AI into your localisation setup, feel free to reach out. I'd be happy to share my experiences and explore what could work for your organisation.
About the author
Florian Steinert-Niewierra is a localisation industry leader with more than 25 years of experience helping global brands engage customers across languages and markets. Throughout his career, he has held leadership roles spanning project delivery, entrepreneurship, account management, and business development. With deep expertise in the automotive sector, he has worked closely with OEMs and suppliers on multilingual customer experience, product communication, and digital transformation. At LanguageWire, he helps global brands turn AI-powered localisation into a strategic advantage for international growth and customer engagement. He believes that the strongest brands are those that truly speak their customers’ language.
Connect with Florian on LinkedInThis article is the October 2026 edition of Lost & found in translation, a monthly newsletter sharing insights, opinions, and reflections by real people who work in localisation. Subscribe here to get notified when the next edition comes out.
