AI Strategy

Cutting costs today could cost you customers tomorrow.

Nowadays, AI is often used as an umbrella term for different technologies.

Machine Translation (MT)

MT is built for one purpose: turning text from one language into another as quickly and consistently as possible. It follows established patterns and terminology and is optimized for translation tasks. In localization, MT is the engine that accelerates volume, but it still depends on human oversight to ensure accuracy, tone, and brand alignment.

Process Automation (PA)

Process Automation doesn’t work on the text itself — it works on the steps around the text. It moves files, triggers approvals, connects tools, and reduces repetitive manual work. In localization, it keeps projects flowing smoothly so people and technology can focus on higher-value tasks.

Large Language Models (LLM)

LLMs handle language in a broad, flexible way. They can draft, summarize, reorganize, or interpret content, but they are not built specifically for translation. In localization, they are useful for idea generation, content exploration, or supporting linguists with suggestions — as long as their output is reviewed and guided by clear instructions.

They scale the system you already have.

LLMs, MT, and PA can reduce effort, increase throughput, and improve consistency — but only when the underlying framework, systems, processes, workflows, and linguistic assets are healthy enough to support them.

Most AI initiatives in localization fail because the technology is introduced at the wrong layer.

A company sees translation cost, turnaround time, or volume pressure and tries to solve the problem at the workflow level: add MT, add an LLM, automate routing, reduce human effort. But the workflow is only the visible path through the system. If the framework behind it is unclear, and the system around it is unstable, AI will not reliably create efficiency. It will multiply whatever the operation already produces.

If source content is inconsistent, terminology is unclear, translation memories are contaminated, review feedback is not reused, vendor profiles are incomplete, and quality expectations are undefined, AI does not simply simplify the operation. It makes the consequences harder to predict. Without a clear understanding of where the operation is strong and where it is weak, it becomes difficult to know whether a new AI initiative is more likely to create a net efficiency gain or a net efficiency loss.

Machine Translation can accelerate volume, but it also increases the cost of poor assets. Low-rate post-editing can push linguists into less critical engagement with machine suggestions, creating authority drift, confirmation bias, and gradual quality erosion.

LLMs can support term extraction, content clustering, alignment, drafting, and analysis. But without governance and feedback loops, they can also introduce hallucinations, unstable tone, and corrections that do not persist.

Process Automation can reduce administrative work. But if workflows are not actually adopted by the people using them, automation makes the wrong process faster.

That is why we assess AI readiness through Language Operations maturity. We look at how language work is positioned in the business framework, how the system connects tools, roles, responsibilities, and assets, how workflows are followed in practice, and whether the linguistic assets produced by those workflows are clean enough to support automation.

The question is not whether AI can be added.

The question is whether the operation is stable enough for AI to multiply the right things.

AI isn't a quick add-on.

It's a force multiplier, scaling both efficiency and error.

Scaling Success with MT

Conditions:   Clean Linguistic Assets and Fair Rates 

Effects: The system generates drafts that respect brand voice and domain terminology. Linguists have the time to critically engage with the Machine suggestions.

Scaling Failure with MT

Conditions:   Poor Linguistic Assets, Low Rates & Content Agnostic Application

Effects: The system generates drafts that don't respect brand voice, domain terminology, and context. To maintain their earnings, linguists are forced to engage with MT suggestions less critically than they should. This leads to various cognitive bias loops such as authority drift, confirmation bias, or cognitive ease spirals, which continue to erode post-editing quality over time.

Scaling Success with LLMs

Condition: AI is applied to structured, reviewable tasks like term extraction, TM alignment, and content clustering.

Effect: It handles scale while humans validate and interpret the results. Asset Quality continously improves — downstream automation becomes safer and smarter. Read more here.

Scaling Failure with LLMs

Condition: AI-based post-editing is used without governance or feedback loops.

Effect: Models hallucinate, fluctuate in tone, and forget prior corrections.Read our article on the use cases of AI-based post-editing here.

Scaling Success with PA

Condition: Pre-translation pipelines automatically pull source-text updates and cluster/route content automatically.

Effect: Projects start in larger batches leading to reduced cost.  Less admin effort, fewer reworks, and faster turnaround.

Scaling Failure with PA

Condition: Automated vendor selection runs on incomplete vendor profiles and/or without Vendor-KPI tracking.  Content Cluster KPIs are set up too strict or lenient, allowing for too many or too little linguists in the pool.

Effect: Mismatched linguists leading to additional review cycles, downstream quality issues and additional PM hours  Consequence: Workflow Efficiency on paper becomes operational friction in practice.

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Understanding AI, MT and Process Automation readiness

The success of all measures aiming to increase Workflow Efficiency (Process Automation, Machine Translation, Large Language Models, etc.) depends on looking at your Language Operations as an essential part of your Content Operations.
Processes create, use, and maintain workflows and linguistic assets that accumulate over time. The quality of these workflows and assets reflects the maturity of the system — and ultimately determines how effectively AI, MT, and automation can be integrated into the system.

Framework

The overarching architecture that defines how translation fits into a business — not as a production service, but as a core function that supports strategy, customer experience, and operational consistency. It connects people, technology, and quality goals into one scalable structure.

System

The organized environment where the framework becomes operational — translating strategy into coordinated activity. A system combines tools, workflows, and clearly defined roles into a functioning whole, ensuring that information, accountability, and quality control flow smoothly between all contributors.

Process

A repeatable and traceable sequence of actions within the system that transforms inputs into outputs while maintaining quality and consistency. Processes define how work actually happens — from project intake and translation to review, validation, and asset updates. Each process is measurable, improvable, and guided by clear ownership.

Workflow

The structured path that operationalizes each process step through a defined sequence of human and automated actions. Workflows translate processes into executable routines — specifying who does what, in what order, with which tools, and under which conditions. They govern how content, decisions, and linguistic assets move through the system.

A Diagnostic View of Your Language Operations

By tracing issues through the framework, system, process, and workflow layers, we reveal the root causes of unhealthy assets, inconsistent execution, and low workflow adoption.

Key Indicator: Asset Health

The key indicator of MT and LLM readiness is Asset Health.

Your source text quality, translation memories, termbases, and style guides are the visible outcome of your entire Language Operations. If they’re inconsistent, outdated, or misaligned, the problem rarely starts at the asset level — it’s a symptom.  Poor asset health traces back through workflows (how content is handled), processes (how work is structured), systems (how technology is configured), and ultimately the framework (how translation fits into your business).  That’s why we start every readiness assessment by examining the quality, structure, and usability of your linguistic assets.

The Root Causes of poor Asset Health

Every weak asset tells a story about an earlier breakdown:

At the framework level, translation may not be aligned with business or quality goals.
At the system level, poor integration or tool sprawl leads to data fragmentation.
At the process level, unclear ownership or missing feedback loops prevent consistency.
At the workflow level, uncontrolled edits or manual handoffs introduce contamination. 
We trace each issue back to its origin — to understand not only what went wrong, but why it keeps happening.

Key Indicator: Workflow Adoption

The key indicator of Process Automation readiness is Asset Health.

Workflow adoption shows how closely day-to-day work aligns with the processes your organization believes it is running. High adoption means teams consistently follow designed steps, use tools as intended, and rely on the same checkpoints and quality controls. Low adoption means teams improvise, bypass systems, or rebuild workflows from memory — a clear sign that the documented process does not match operational reality.
To understand MT, LLM and PA readiness workflow adoption is a critical leading indicator: automation can only improve what people actually do, not what the workflow diagram claims they should be doing. We measure adoption to reveal whether your workflows are truly embedded, partially followed, or quietly abandoned in favour of informal workarounds.

The Root Cause of low Workflow Adoption

Low workflow adoption rarely stems from resistance — it stems from misalignment. 

Most breakdowns appear when workflows are designed in isolation from the reality of the system and the people operating it. If tools don’t support the required steps, if ownership is unclear, if instructions contradict actual constraints, or if feedback loops are missing, teams will default to whatever gets the job done. Over time, these workarounds solidify into the real workflow, leaving the documented one as a fiction.
In localization, this misalignment becomes especially visible: source inconsistencies, unhealthy assets, poorly integrated tools, unclear review paths, and pressure to deliver quickly all push teams away from the intended process. The result is fragmented execution and structural vulnerability — the exact conditions under which MT, AI, and automation amplify problems instead of solving them.

Framework Analyses

We start by mapping how translation fits into your organization.

Does the framework clearly define ownership, governance, and quality targets? Does it connect language operations to your business strategy, or is translation still treated as a production service taking place on the workflow level? For stable and scalable MT, AI and Language Process Automation, translation needs to be considered at the Framework level.  Our framework analysis identifies whether the structural design of your language function supports — or limits — your readiness for AI, MT, and process automation.

System Analyses

Next, we examine the system — the operational environment where strategy becomes daily work. 

We assess how your tools, roles, and data flows interact, and where breakdowns occur.  Typical weaknesses include poorly configured TMS platforms, siloed QA tools, or workflows that exist only in people’s heads.  System failures often reflect gaps in the framework (missing governance) or over-reliance on manual processes that prevent scalable automation.

Process Analyses

We analyze how your processes translate structure into action.

Are steps documented, measurable, and repeatable — or reinvented for each project?  We look for missing checkpoints, unclear ownership, and inconsistent feedback loops.  Process breakdowns often occur when systems are poorly aligned with real workflows, or when the framework sets unrealistic expectations that can’t be met in practice.

Workflow Analysis

Finally, we study how work actually moves through your system — across tools, teams, and languages.

We track file routing, task assignment, review cycles, and communication patterns.  Workflow issues often expose deeper misalignments: systems that don’t reflect defined processes, or processes that no longer fit the framework’s intent.  By observing what people really do, not just what the documentation says, we uncover the friction points that prevent your automation and AI initiatives from creating real value.

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