AI is the elephant in the room when speaking with our customers. It is cheap and widely available, so naturally pressure is growing to incorporate it. This pressure is often coming from senior levels of management. The goal is clear: reduce costs, increase turnaround speed, all while maintaining acceptable quality levels. But swapping out well-established localisation processes for a quick fix of AI does not come without complexity.
In this article, we will dive into the challenges content teams face when pressured on one side by senior management to “reduce costs” and “deliver faster,” while still ensuring “good quality.” We will highlight things to keep in mind and give you some ideas on how AI can enhance – rather than replace – your well-established localisation processes.
AI pressures and rising expectations
Senior management is increasingly tying KPIs to AI adoption. In many organisations, AI is being positioned primarily as a cost-cutting lever, with expectations of faster turnaround times and reduced reliance on human touchpoints, which is often seen as slowing processes down.
At the same time, localisation budgets for traditional workflows are being paused or reduced while companies “figure out the AI situation”. Local markets may be calling for more localised content, yet funding is put on hold in anticipation of savings from new AI-driven approaches.
In some cases, small-scale AI workflows are championed as the solution to all localisation challenges. However, these workflows are often rolled out at enterprise scale without having been designed or tested for that level of complexity, even in regulated industries such as MedTech.
AI is not a simple add-on or a universal shortcut. To deliver value without increasing risk, it must be embedded within a structured localisation framework, supported by clear governance, scalable processes, and strong cross-functional collaboration.
Communicating the cost, quality and speed trade-off to management
Prior to AI, a trade-off between cheap, fast, and good defined localisation processes.
In a recent interactive session with our customers, Jonas Steno Olsen, Strategic Solution Consultant at LanguageWire, illustrated why this trade-off becomes particularly complex in enterprise environments and how content teams can manage these complexities:
Traditionally, speed, cost and quality were like options on a menu, where you could choose only two. Fast and high-quality translations were not cheap, and fast and cheap translations were not high quality, etc.
However, the introduction of AI has reshaped these expectations. Many now assume that all three can be achieved at once, creating new tensions for managers and linguists alike.
Improvements in AI technology are driving gains across quality, speed, and cost simultaneously. Defining what “good enough” quality looks like remains a key challenge (explored further in this article), but AI is also reshaping translation workflows in more fundamental ways.
When working from a baseline of AI translation output, the starting point for translators is now significantly stronger. This reduces effort, accelerates turnaround times, and lowers production costs. At a small scale, pure AI translation can look deceivingly sufficient with tools such as ChatGPT or Copilot performing quite well in simple settings.
However, once these workflows are expanded to enterprise level, complexity increases. Governance, consistency, regulatory requirements, and brand risk quickly expose the limits of small-scale AI experimentation. Jonas’ advice is clear.
If you are facing pressure from management to translate more within the same budget, while stakeholders simultaneously raise concerns about declining quality, having a framework for differentiating your content localisation strategy becomes essential. Even though AI improves all three parameters, trade-offs still exist at scale.
The decision about where to prioritise cost, speed or quality is a strategic choice that belongs to management, not an operational burden placed solely on localisation teams.
The solution: flexible workflows to handle changing demands
One way to bring this trade-off to life is to make it visible and measurable.
By structuring content types, priorities, templates, and workflows in a simple spreadsheet view, you can clearly map how different choices impact cost, speed, and quality. Each decision is reflected in real time, showing both current costs and estimated future costs based on the selected workflows.
This makes it easier to have informed conversations with management. Instead of debating assumptions, you can demonstrate the impact of choosing a faster or cheaper workflow, and what that means for quality.
For example, imagine your company operates in both France and Poland, but due to cost pressure, you decide to deprioritise the French market by simplifying its localisation workflow. You might switch to Post-Edited Machine Translation with lighter human review than before.
By documenting adjustments to the workflow setting, and how that affects cost and expected quality, you can help management visualise priorities and their real-world implications. If the French market for example starts reporting more quality complaints, and those complaints begin to affect performance, you have something tangible to bring to management: This is what we changed, this is what we saved, and this is what it affected. How should we prioritise from here?
That gives localisation managers a practical way to show how the knobs of cost, speed, and quality are being turned, and what the consequences are. It also makes it easier to redirect quality and resources where they create the most value, without necessarily increasing overall costs.
LanguageWire can help your team make informed decisions by guiding what can be adjusted, what the implications are, and how to adapt workflows quickly without becoming a bottleneck.
If your management asks for cost reductions, you can select a simpler workflow. Similarly, if they express concerns about quality, you can show them what happens if they prioritise a more advanced workflow. The key is that these decisions become transparent, structured, and easier to justify.
This approach ensures that neither the localisation team nor LanguageWire becomes a bottleneck when demands shift. Teams can adapt quickly, adjust workflows as needed, and respond to changing priorities without slowing down delivery or compromising control.
In this way, the model does not just support operational flexibility. It also gives localisation managers a practical way to communicate trade-offs and defend their choices when expectations around AI, cost, and speed collide.
Download the localisation strategy template
Thinking about localisation in a structured way makes it easier to have constructive conversations about speed, cost, and quality.
You can map this out yourself in a document or notepad. Or you can use our simple template to get started quickly.
Try the templateAdjust content types, workflows, and levels of human review, to get an idea about how your choices affect both current and projected costs, as well as expected quality outcomes.
This gives you a practical way to move from assumptions to data; so you can have clearer, more informed conversations with management about priorities and trade-offs.
Once you’ve tested your setup, you’re welcome to share your results with us. We’ll help you translate them into a scalable workflow that fits your business needs.
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