1. Evaluating AI Requests in Localization: Balancing Opportunity and Practicality

Introduction

Artificial Intelligence is increasingly used in localization to speed up translations and manage multilingual content. AI is no longer just a side tool – it’s now at the heart of many successful global localization strategies. However, that doesn’t mean every idea involving AI will work out. Not every AI project is destined for success or will deliver real value; a poorly chosen AI use case can easily waste time and resources. Localization teams and their managers, therefore, need a way to separate the promising ideas from the impractical ones. Before jumping on the latest AI trend, it’s crucial to step back and evaluate if a request is truly “AI-worthy” – in other words, whether using AI for that problem makes sense in the real world

The Goal of Evaluation

Why evaluate AI requests at all? The main goal is to decide if a proposed AI initiative is worth pursuing in the first place. This means confirming three things:

(1) it is technically possible to do,

(2) it aligns with your business needs, and

(3) it justifies the effort and cost.

In simple terms, an evaluation helps you figure out if the idea makes sense. Many organizations have chased AI initiatives without a clear understanding of feasibility or business value, so a structured evaluation acts as a safeguard. It ensures the project aligns with your core objectives and that your organization is prepared to tackle it. By evaluating upfront, you can make an informed decision about whether to greenlight the request, refine it, or politely decline it. The outcome of this evaluation is essentially a go/no-go decision: should this AI idea become a project or not?

Technical Considerations: Feasibility Checkpoints

On the technical side, consider a few key questions to determine if the idea is practical to implement:

Data Availability: Do you have enough language data or examples to power the AI? AI systems learn from data, so if your localization project lacks bilingual text, translation memory, or other relevant data, the AI might not work well. Insufficient or poor-quality data will lead to weak results – in other words, garbage in, garbage out. Successful AI requires a sufficient volume of clean, relevant data to train or fine-tune models. If such data isn’t available (or cannot be obtained), the request may not be technically feasible.

AI Capability and Limits: Is the task something AI can realistically do today? AI is very good at certain jobs – for example, machine translation, basic content summarization, or answering common questions – especially using modern pre-trained models. But for more specialized or nuanced tasks (like capturing subtle cultural humor or applying a very specific style guide), AI might struggle without extensive custom training. It’s important to be realistic about AI’s current limits. Remember, AI isn’t a magic button that instantly replaces human expertise. In practice, teams that try to fully automate complex localization tasks often discover they still need human review and adjustments, which can erase the expected time savings and even hurt quality. Make sure the request doesn’t assume AI can do the impossible.

Integration with Tools: Will the AI solution fit into your existing workflows and tools? Consider how it would connect with your content management system (CMS), translation management system (TMS), or other software you already use. A stand-alone AI demo might look impressive, but getting it to work in your production environment is another story. If the AI can’t plug in smoothly, you might face complicated development work or process changes. In fact, promising AI pilots have stalled simply because integration wasn’t planned – if a tool doesn’t fit your systems, it can end up costing extra time, budget, and momentum. Look for solutions that offer APIs or integrations with your platforms, and involve your IT team early to gauge the complexity. Technical feasibility isn’t just about the AI itself, but also how well it can operate within your company’s tech ecosystem.

Business Considerations: Value and Impact

Evaluating an AI request isn’t only about tech specs; it’s equally about the business side. Here are the key business questions to ask:

Real Problem and Priority: Does the request solve a real pain point or address a significant business need? In other words, why do this at all? A good AI project should target a specific, impactful problem – for example, “reducing translation turnaround time by 30%” or “improving consistency in product descriptions across all languages.” If the proposal’s goal is vague (like “let’s use AI to improve efficiency” without details) or not tied to a clear business objective, be cautious. Every AI initiative should be grounded in a concrete use case that matters to your organization. Otherwise, it can become a solution looking for a problem. Make sure the idea aligns with your company’s priorities (e.g. improving customer experience, lowering costs, speeding up releases) and that success can be measured in a meaningful way.

Time and Cost Benefits: Will this AI solution save time, cut costs, or add measurable value? After all, an AI project is an investment – there should be a return. Estimate the potential ROI (return on investment) by asking what the outcome would be if it works. For example, could it automate a repetitive manual task and free up your team’s time? Will it allow you to handle more volume without additional headcount? Perhaps it can reduce expensive errors (like catching translation mistakes before they go live) or improve speed so you get products to market faster. Quantify these benefits as much as possible. For instance, “if AI can pre-translate 50% of content, our linguists can focus on the rest, potentially saving X hours per week.” The more tangible the benefit – such as labor hours saved, faster turnaround, higher translation quality, or cost reduction – the more it justifies the effort. On the flip side, consider the costs and effort required to implement the AI: if it demands a lot of money or time to develop, those costs need to be outweighed by the benefits.

Ease of Adoption and Maintenance: Think about practicality: Will the AI tool or process be easy for your team to use and for your organization to maintain over time? A solution that is overly complex or user-unfriendly can fail because the team simply won’t adopt it in their daily work. Consider the learning curve – do users need special training to use the AI tool? Ideally, it should integrate into their workflow without causing frustration. Also, plan for the long-term maintenance of this AI solution. Unlike a simple piece of software, AI systems need ongoing care. Language models can “drift” or become outdated as language and content evolve. If nobody updates or monitors the AI, its performance will degrade over time. In fact, treating an AI system as a set-and-forget asset is a common reason such projects fail – when the model’s output quality silently decays, users lose trust and stop using it. So, ask who will own the upkeep: Do you have the resources (or a vendor) to regularly update the model or data? Ensure there’s a plan for support and improvements if the AI goes into production. A project that needs constant expert intervention or expensive upkeep may not be worth it in the long run, no matter how impressive the initial demo.

A Simple Evaluation Checklist

Bringing the above points together, here’s a straightforward checklist you can use to evaluate AI-related requests from your localization team. This can help you decide systematically whether an idea should move forward:

Define the Need Clearly: What exactly is the request asking for, and what problem is it trying to solve? Make sure the localization pain point or goal is specific. Write down the desired outcome (e.g. “automate QA checks for translations to reduce manual review time by half”). If you can’t easily explain the problem and why it matters to the business, the idea might not be well grounded.

Check Technical Feasibility: Ask your technical team or do a quick feasibility scan. Do we have the necessary data (bilingual text, past translations, glossaries, etc.) to support an AI solution? Is there known AI technology that can do this task (such as existing translation engines or AI models for quality checking), or would it require novel research? And can this solution integrate with our current tools and workflows without excessive development work? Essentially, can we build or buy this AI, and will it work in our environment?

Assess the Business Impact: Weigh the expected benefits against the costs. What do we gain if this works (time saved, faster turnaround, better quality, cost savings, happier customers)? Is that gain significant for our team or company? Also consider the cost/effort: how much time, budget, or new expertise would we need to get the AI up and running? If an idea promises, say, a 5% efficiency boost but requires a huge investment, it may not be worth it. Look for a positive ROI – the value added should exceed the resources spent. If possible, prioritize requests that solve pressing issues or offer big wins in efficiency or quality.

Evaluate Usability and Maintenance: Think about implementation and beyond. How will the localization team use this AI solution day-to-day? Does it fit naturally into their workflow or will it add complexity? Ensure it’s something the team will actually embrace (sometimes a simpler tool that people use is better than a fancy one they avoid). Next, decide who will manage the solution after launch. For example, if it’s an AI that auto-translates content, who will monitor its output and adjust it if needed? If it’s a custom machine learning model, who will update it with new data over time? If you don’t have clear answers here, you might need to scale back the idea or plan for additional support. A sustainable AI solution should be easy to use and maintain given your team’s capacity.

Make an Informed Decision: Finally, bring all these considerations together. If the request passes the checks – it’s technically feasible, solves a meaningful problem, has good ROI, and is practical to implement – then it could be a strong candidate to pursue. You might approve it outright or perhaps start with a small pilot project to validate the idea on a limited scale. Starting small can manage risk while proving the concept’s value. If the evaluation reveals major red flags (for instance, no available data, unclear benefit, or lack of maintenance plan), it’s reasonable to pause or decline the project. Not every idea, even if interesting, should move forward. In such cases, provide constructive feedback to the team: explain why it’s not feasible right now – maybe you need to collect more data first, or the technology isn’t mature enough yet, or other projects take priority. This way, the decision is transparent and based on clear criteria.

By following a checklist or a similar structured process, you create a consistent way to vet AI proposals. It turns subjective “gut feelings” into an objective evaluation. Over time, this helps the localization team learn what a viable AI project looks like and encourages them to focus on ideas that truly make sense.

Conclusion

In the fast-evolving world of AI, it’s easy to get caught up in the excitement and want to try everything. But the reality is that not every AI idea should become a project – at least not without careful consideration. Successful adoption of AI in localization is about balancing opportunity with practicality. On one hand, you want to seize the benefits of AI where it can genuinely improve your processes. On the other hand, you must be mindful of real-world constraints like data, integration, user adoption, and maintenance. The best approach is a balanced one: look beyond the hype and ensure any AI initiative checks out on both the technical and business fronts. By pairing the potential business value of an idea with a sober assessment of its feasibility and effort, you take a holistic view. In doing so, you’ll be able to confidently champion AI projects that are likely to succeed – and just as importantly, know when to say “no” (or “not yet”) to those that aren’t. In short, evaluating AI requests rigorously but fairly will help your organization invest in what truly matters, keeping your localization efforts innovative and grounded in reality.