3. Designing an AI Pilot Program for Localization

Implementing artificial intelligence in localization can dramatically speed up translation workflows and improve consistency, but success is far from guaranteed without a well-designed pilot. An AI pilot program is a controlled, small-scale experiment that allows organizations to validate AI’s impact on their localization process before committing to a broader rollout.

In a previous article, Preparing an AI Pilot Program for Localization, we covered the foundational work—from defining objectives to selecting suitable content for testing. Building on that foundation, this article focuses on designing the pilot itself: how to structure the experiment so that meaningful and reliable data can be collected. At this stage, the goal is not to judge success or failure, but to ensure the pilot is designed in a way that makes those judgments possible later.

Throughout this guide, terminology is kept simple, and no prior localization expertise is assumed.

A successful AI pilot starts with careful design. This includes defining a clear scope, assembling the right team, selecting appropriate content and languages, and preparing the tools and data needed to observe AI behavior in a controlled environment. Good design ensures that what you are testing is intentional, observable, and comparable.

Planning Starts by Defining What the Pilot Is Meant to Prove

At the planning stage, many teams instinctively start by evaluating tools or vendors. While understandable, this approach often leads to unfocused pilots.

An effective AI pilot is designed around a hypothesis, not a technology. Planning means translating a business concern—such as cost, speed, quality, or scalability—into something that can be observed during the pilot.

For example, a marketing team may want to know whether AI can reduce turnaround time for campaign localization without weakening brand tone. A support team may want to see whether AI-assisted translation lowers cost and turnaround time without introducing content that could mislead customers. A localization team lead may want to understand whether a trained AI model produces better output than traditional neural machine translation, enabling them to renegotiate vendor rates.

Each of these questions represents a different hypothesis. Designing the pilot requires choosing one primary assumption to test.

When a pilot is built to test a single, clearly defined hypothesis, the results—positive or negative—are interpretable. When a pilot is designed simply to “see how AI performs,” the outcomes may be interesting, but they rarely support confident decisions.

Assemble a Cross-Functional Team

Although a pilot may be limited in scope, it should be treated as a real project with defined ownership.

A typical pilot team includes a localization manager or project manager to lead the effort, translators or linguists who review AI output, content stakeholders such as marketing or sales managers who represent end-user expectations, and technical staff or engineers if system integration is required. If AI usage requires approval from IT, legal, or compliance teams—such as for data privacy or security considerations—those stakeholders should be involved early.

Roles and responsibilities should be clearly defined in advance. This includes identifying who reviews AI output, who maintains linguistic resources such as glossaries or style guides, and who is responsible for tracking and validating pilot data.

Even a small pilot requires dedicated time and attention. Securing management support upfront helps ensure the pilot is not deprioritized in favor of day-to-day operational work.

Define the Scope and Process

Once the hypothesis and team structure are clear, the next step is to define a scope that allows clean data collection.

Outline exactly what the pilot will include: content types, languages or locales, volume, and duration (for example, an eight-week or three-month trial). Specificity helps prevent scope creep and ensures that data collected during the pilot is consistent and meaningful.

Equally important is deciding how the pilot will run. Many organizations use a parallel workflow, where AI-generated output is reviewed but not published until validated. Others use a sandbox environment separate from production systems. These controlled setups enable side-by-side comparison between AI-assisted workflows and existing localization processes.

At this stage, design defines the structure of observation; execution will later reveal how AI behaves within that structure.

Defining Quality Expectations

Localization service providers (LSPs) typically offer multiple workflow options based on different quality expectations. Clearly defining the expected quality level—and aligning the workflow accordingly—is critical for effective resource planning and cost control.

Different teams often have very different definitions of “quality,” and those differences should directly influence how AI and human effort are combined.

For example, a marketing team may want to maintain brand tone and creative impact while operating under a lower budget. In this case, it may be appropriate to explore training an LLM to perform transcreation, followed by human post-editing to refine tone and messaging.

A product team, on the other hand, may require new product launch content to be translated with high accuracy and clarity. For this scenario, a workflow based on neural machine translation (NMT) with full human post-editing may be more suitable, resulting in output that is very close to—or nearly indistinguishable from—human translation.

Meanwhile, a technical documentation or customer support team may prioritize accuracy and speed at the lowest possible cost. In such cases, NMT with light human post-editing can be sufficient. The post-editor focuses only on correcting mistranslations or critical errors, without adjusting style or voice.

In some pilot programs, it may also be valuable to run multiple workflows in parallel for the same content. For example, teams may test LLM output with full post-editing alongside NMT output with full post-editing to determine which approach produces better results for a specific content type. Designing the pilot to compare workflows side by side helps organizations make informed decisions about which solution best fits their quality expectations and content characteristics.

Defining quality expectations upfront allows teams to select the right AI approach, post-editing level, and workflow design—ensuring that effort, cost, and outcomes are aligned with actual business needs rather than a one-size-fits-all standard.

Prepare Tools and Data for Observation

Before launching the pilot, ensure that both the technical setup and the underlying data are ready to support structured observation.

If you are using a machine translation engine or a multilingual large language model, it should be integrated into your existing translation workflow or management system so the team can use it naturally. Friction in tooling can distort pilot results by introducing variables unrelated to AI performance.

Translation assets such as translation memory, terminology databases, and style guides should be reviewed and cleaned where possible. These resources may be used directly by AI systems or by human reviewers, and their quality has a direct impact on output consistency.

This is also the stage to prepare data collection mechanisms. For example, engineering or business intelligence teams may configure systems to automatically log metrics such as post-editing distance, segment change rates, or other indicators. At this point, the focus is on ensuring data can be captured reliably—not on interpreting what those numbers mean.

In short, the goal is to ensure that once the pilot begins, the team can focus on observing AI behavior rather than troubleshooting systems. If existing content or translation systems struggle with automation or integration, those issues should be addressed before the pilot starts.

Define Responsibilities and Timeline

With people, tools, and processes clearly defined, the program manager can then establish responsibilities and timelines for each stage of the pilot. The objective is to make efficient use of each team’s time without disrupting ongoing projects.

For example, a localization service provider (LSP) may allocate the first two days to gathering translation memory and terminology, followed by three days to cleaning and aligning language assets. One additional week may be used to fine-tune or configure an LLM using those assets. AI-generated output could then move through one week of post-editing and another week of linguist review and structured feedback collection.

In parallel, engineering teams may require approximately one week to complete technical setup and system integration. Legal or compliance teams may need a separate week to review data usage and approve AI deployment. Content stakeholders—such as local sales or marketing teams—may also need dedicated time, for example one week, to score translated content against business and brand expectations.

While these activities differ in function, many can run concurrently. Designing the pilot with a shared timeline—often documented in a Gantt chart or simple project plan—helps clarify who is responsible for what, when data will be generated, and how dependencies are managed.

By thoughtfully designing the pilot’s scope, team structure, and technical setup, you create a framework that allows meaningful signals to emerge. Everyone involved should understand what is being tested, why it is being tested, and how the pilot is structured on a day-to-day basis.

In the next article, we will focus on how to evaluate the success of an AI pilot program—how to interpret the data collected and translate pilot results into informed decisions.