Overview of How We Use AI in Practice

Tools speed up tasks. People protect meaning, accuracy, and responsibility. However, to maximize the benefits of AI in translation workflows, it is essential to define clear AI goals. Setting specific, measurable objectives ensures that AI implementation aligns with overall project or business objectives, guiding effective integration across translation, QA, engineering, and project delivery.

To show how this works in practice, we asked our internal teams how they use AI across translation, QA, engineering, and project delivery.

This article explains how we use AI in production work.

How Often Our Teams Use AI

use AI daily or several times per week
0 %
use AI occasionally
0 %
have never used AI or do not use it in their role
0 %

  • 71% use AI daily or several times per week
    (5 out of 7: AI described as essential, handling heavy lifting, core to quality checks, or enabling speed and scale)

  • 14% use AI occasionally
    (1 out of 7: AI used for technical help or summarising, but not for critical work)

  • 14% have never used AI or do not use it in their role
    (1 out of 7: explicitly states AI does not help in their responsibilities and prefers full human control)

The use of AI depends on content type, risk level, and client rules. It is never automatic.

Where AI Supports Our Work

1. Technology & Systems

AI supports the technology team by tools designed to help in:

  • Pre-analysis of files and formats
  • Detecting structural issues before production
  • Supporting tool configuration and integration checks
  • Flagging incompatibilities across CAT, QA, and delivery tools
"AI handles the heavy lifting and repetitive tasks, which lets us move much faster. However, we always keep a "human in the loop" to review the work. For global businesses, this is the best of both worlds: you get the speed of modern technology, but real people still ensure the message makes sense and sounds natural."
Rita Pulga
Technology Manager

Limits:
AI cannot change system rules, override configurations, or introduce new tools without human approval.

2. Project Management & Delivery

Project managers use tools powered by AI to:

  • Pre-analyse project scope and risk factors
  • Support scheduling and workload forecasting
  • Automate repetitive updates (reporting, handovers)
  • Highlight inconsistencies between inputs, assets, and instructions

Limits:
AI does not make delivery decisions, change deadlines, or override client-specific rules.

3. Quality Assurance

From a QA perspective, AI is used to:

  • Run automated consistency and formatting checks
  • Flag potential terminology conflicts
  • Support pattern detection across large volumes
"AI support is now essential because it can filter out false positives, allowing our team to focus entirely on genuine linguistic issues. This shift has refined our quality checks, moving us from manual oversight to a much more precise and efficient process."
Petar Tomic, Head of QA
Petar Tomic
Petar Tomic, Head of Quality and R&D

Limits:
AI never approves quality, validates terminology, or signs off deliverables. All quality decisions remain human-led.

4. Control, Traceability & Compliance

Control management use tools powered by AI to:

  • Monitor workflow consistency across teams
  • Support audit trails and traceability
  • Identify deviations from defined processes
"AI helps me with some technical issues with the tools I use and for occasionally summarizing findings. I don't rely on it for any critical part of the work."
Milivoje Gavrilovic
Control Manager

Limits:
AI cannot approve compliance, interpret regulations, or assume responsibility.

5. Accessibility & Compliance

AI supports accessibility work by:

  • Flagging potential accessibility risks in source files
  • Supporting early checks against standards (e.g. structure, tags, formatting)
"AI can support accessibility work by automating technical checks, detecting common errors, and enabling tools such as speech-to-text, text-to-speech, and language simplification. Tools like Adobe’s accessibility checker and apps such as Háblalo help bridge communication gaps and improve inclusion, especially for people with hearing or speech impairments. However, human expertise remains mandatory to meet accessibility standards. Automated tools often miss context, intent, and real usability. Accessibility requires understanding how people with different disabilities—especially cognitive or neurological—interact with content. Manual remediation depends on experts who can evaluate meaning, clarity, and user experience across design, authoring, and language. AI outputs, including simplified text, must always be reviewed by specialists. The most effective approach is a hybrid model: AI accelerates detection and drafting, while humans ensure accuracy, context, and true usability for all users."
Milena Spelta Parenti
KAM & Accessibility & Inclusive Language Advocate

Limits:
AI does not certify accessibility or replace expert validation against standards.

6. Sales & Client Management

Sales and key account teams benefit from AI through:

  • Faster response preparation
  • Better visibility into delivery risks and constraints
  • More consistent communication across projects
"At the beginning, clients were certainly a bit hesitant and had limited trust in AI. However, through presentations and calls, we were always able to reassure them that a human translator reviews all post-edited machine translations. In many cases, AI has helped us meet client needs that would have been difficult to satisfy otherwise, especially when clients had urgent requests or large volumes of content to translate. It often provides the right balance of cost savings, speed, and sufficient quality for their documentation needs, contributing to better outcomes that matter to the clients."
Chiara Tommasini
Sales Manager

Limits:
AI does not set expectations, negotiate scope, or define responsibility.
Clients care about outcomes, accountability, and predictability, not automation itself.

7. Vendor Management

AI supports vendor management by improving coordination, visibility, and consistency across external partners. VM team uses AI to:

  • Pre-check incoming assets for completeness and structural issues
  • Support vendor brief preparation and instruction consistency
"AI is not helping me to manage vendors, I do it by myself, human oversight is needed the most when I am looking for a vendor for a specific domain and need to check their resumes or information on other platforms, if it is not mentioned on their CVs. And AI would only focus on target words which is not always the right way."
Andjela Cekerevac
Vendor Manager

Limits:
AI does not select vendors, assess linguistic performance, approve vendor output, or manage vendor relationships. All vendor evaluation, escalation, and accountability remain human-led.

What AI Never Does Alone

AI does not:

Every project goes through human review.

Human Control Points

Human intervention exists at every stage:

  1. Project setup and asset selection
  2. Terminology validation
  3. Translation or post-editing
  4. Review and QA
  5. Final delivery approval
  6. Client feedback integration

100% of final deliveries receive human sign-off.

Impact on Quality and Efficiency

Internal feedback shows:

say AI is essential or significantly accelerates their work
0 %
report that human oversight remains a core requirement
0 %
highlight a hybrid AI + human model as the most effective approach
0 %
  • 57% say AI is essential or significantly accelerates delivery
    (AI described as critical for speed, scale, filtering issues, and handling repetitive or high-volume tasks)
  • 71% report that human oversight remains a core requirement
    (Clear emphasis on “human in the loop”, expert review, accountability, and contextual decision-making)
  • 57% highlight a hybrid AI + human model as the most effective approach
    (Respondents explicitly or implicitly describe AI as an enabler — not a replacement — stressing balance between automation and expertise)

AI tools also provide valuable insights that support decision making processes, enabling teams to anticipate challenges and optimize workflows. By leveraging these insights, teams can reach their full potential in translation quality and efficiency.

Teams also report that uncontrolled AI use lowers quality. Rules matter.

What Artificial Intelligence Best Practices Make AI Safe in Our Setup

Our safeguards include:

  • Clear internal usage guidelines
  • Centralised translation memories and terminology
  • Client-specific rules and exceptions
  • Tool transparency
  • Ongoing team training

These measures help identify and manage potential risks associated with AI use in translation.

This setup allows speed without losing control.

What This Means for You

For your projects, this approach delivers:

  • Faster turnaround
  • Cost control without quality loss
  • Terminology consistency
  • Auditability
  • Scalable workflows
  • Clear responsibility

AI supports the work. People stay accountable.

Final Take

AI in translation industry works best as a controlled tool, not an authority.
That’s how we use it every day, across teams, with human judgement at the center.

How can we help?

Tell us what you need and we’ll get back to you with the solutions.

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