Translation RFPs were designed for a market in which the principal variable was human capability: language pairs, subject-matter expertise and turnaround time. That variable remains relevant, but it is no longer sufficient on its own.
Language service providers now incorporate AI at some stage of the workflow as standard practice, which shifts the central evaluation question from whether a provider uses AI to how transparently and accountably that use is governed. Transparency (in model choices, data handling, quality processes, and decision-making) runs through each of the six pillars below and is, in practice, the clearest single signal of a provider’s maturity.
This framework is intended to close that gap: a structured approach for enterprise buyers to evaluate AI-enabled language service providers against the factors that determine how AI use creates value or introduces risk.
The framework is intended as a starting point. Sections should be adapted to reflect an organisation’s specific risk tolerance, regulatory context and content types.
The Six Pillars of an AI Translation RFP
1. Model Strategy & Transparency
What this assesses: whether the provider has a deliberate, documented rationale for its choice of AI/MT engines.
Sample questions to include:
- Which AI/MT engines are used, and on what basis were they selected?
- What process governs the evaluation and validation of a new model prior to its use on live client work?
- Is a single engine used, or a mix, and on what basis is that choice made per project (language pair, content type, risk level)?
- Will material changes to underlying models be communicated to us, and how is “material” defined in this context?
What to watch for: Providers should be able to articulate the rationale for their model selection, or the process used to validate new models, and demonstrate a mature and deliberate approach to AI governance.
2. Quality Assurance & Human Oversight
What this assesses: where and how human expertise is applied within AI-enabled workflows, and whether quality remains governed by recognised standards and measurable frameworks rather than informal or ad hoc processes.
Sample questions to include:
- Which recognised quality standards does the provider operate to — for example, ISO 17100 (translation services), ISO 18587 (post-editing of machine translation output), or ISO 9001 (quality management) and how is AI usage incorporated within these standards?
- Is a structured quality evaluation framework applied, such as MQM (Multidimensional Quality Metrics) or an equivalent error-typology model? How are error categories and severities defined and scored? Can these be customized?
- What qualifications and certifications are required of linguists and post-editors involved in reviewing AI-generated output?
- How is the level of human review calibrated to content risk (for example, regulatory or legal content versus lower-risk marketing content), and what review tiers are offered?
- What sampling methodology is used to evaluate quality across a programme of work?
- What quality thresholds or acceptance criteria trigger escalation, rework, or a change in process?
- How are the outcomes of quality reviews fed back into future work — for example, updates to terminology, style guides, or model tuning?
What to watch for: Providers should be able to reference recognised standards or a structured quality framework, be capable of describing quality assurance in detail, and have an auditable,mature and customizable QA process.
3. Data Handling, Confidentiality & Intellectual Property
What this assesses: what happens to content once it enters the provider’s systems, and how confidentiality, data residency, data privacy and intellectual property are managed and governed across the workflow.
Sample questions to include:
- Where is data processed and stored, and what data residency commitments can be provided?
- What safeguards govern cross-border data transfers, and what legal mechanisms (for example, standard contractual clauses) apply where relevant?
- Is any of our content used to train, fine-tune, or otherwise improve AI models the provider’s own or a third party’s — and can this be contractually excluded?
- How is data segregated between clients, and what controls prevent cross-contamination of content or terminology?
- What personal data protection measures are in place, including the handling of special category data, and how do these align with applicable data protection law in our jurisdictions?
- Is a data processing agreement in place, and are sub-processors (including any third-party AI model providers) disclosed and bound to equivalent obligations?
- What intellectual property rights are retained by us in source content and in the resulting translated output, and how is this addressed contractually?
- Where source materials may contain third-party intellectual property, how is the associated risk allocated between the parties?
What to watch for: Providers should be able to provide a detailed description of how data is stored, processed and transferred across the workflow, with a strong understanding of contractual and regulatory commitments relating to confidentiality, data protection and intellectual property rights.
4. Regulatory & Compliance Posture
What this assesses: whether the provider maintains a considered, current view of the wider regulatory environment for AI, recognising that this landscape continues to develop across jurisdictions globally, not solely within the EU.
Sample questions to include:
- How does the provider monitor and respond to developments in AI-related regulation across the jurisdictions in which we operate?
- What governance processes are in place to ensure services remain adaptable as regulatory requirements evolve?
- What compliance documentation or attestations can be provided today, and how are these kept current?
- How does the provider approach jurisdictions with differing or emerging regulatory frameworks for AI, recognising that this is an early and still-developing area globally?
What to watch for: Overstated claims of regulatory compliance in an area where legislation and guidance remain under development should be treated with caution. Providers shouldt demonstrates an active monitoring process and, where applicable, provide evidence of compliance.
5. Governance & Accountability
What this assesses: where responsibility sits when issues arise, and whether the provider can demonstrate (and evidence) sound governance of the AI-enabled process, including auditability.
Sample questions to include:
- Who holds accountability for quality outcomes when AI forms part of the workflow, and how is this documented?
- What is the escalation process in the event of a systemic quality issue?
- Can audit trails or documented records be provided evidencing decisions made within the workflow — for example, model selection, review outcomes, or changes to terminology?
- How would the provider support us in the event of an external audit or regulatory enquiry relating to the use of AI in our content?
- Is AI-enabled translation offered as a managed service (with model selection, quality calibration, and risk management undertaken on the provider’s side) or is this expected to be specified and monitored by us?
What to watch for: Providers should be able to describe how decisions are recorded and made available for audit, take a proactive approach to transparency and demonstrate governance maturity.
6. Commercial Structure, Reporting & Scalability
What this assesses: how pricing and reporting accommodate both established and AI-enabled ways of working, and whether the commercial structure remains appropriate as volumes and requirements change.
This includes the transition many organisations are navigating from traditional pricing metrics (for example, translation memory leverage) toward models reflecting AI-enabled workflows — a transition clients may wish to approach at different paces depending on their own risk appetite.
Sample questions to include:
- How is pricing structured, and how does the use of AI affect pricing relative to traditional translation memory-based models?
- For organisations transitioning from a traditional pricing model to one reflecting AI-enabled workflows, what options exist to manage that transition, and how is flexibility accommodated for clients who prefer a more conservative pace of change?
- Is pricing, including any foreign exchange terms, fixed via an agreed schedule for the contract term, or subject to variation?
- What reporting is provided on a regular basis (for example on quality outcomes, volumes, turnaround times, and model or process performance) and in what format?
- How does the commercial model scale as volume, language coverage, or content types expand?
What to watch for: Providers should be able to explain how pricing and reporting accommodate a transition from traditional to AI-enabled ways of working, at a pace appropriate to the client.
Red Flags Checklist
The following patterns, observed across vendor responses, typically warrant further scrutiny:
- Unable to articulate the rationale for AI model selection or the process for validating new models
- Claims of full human review without a credible explanation of how this is resourced
- Inconsistent or evasive responses regarding the use of client content for model training
- Overstated claims of regulatory compliance for legislation that remains under development
- No clearly identified owner of quality accountability within the AI-enabled workflow
- Limited or no audit trail supporting quality and governance decisions
- Inflexible commercial or reporting structures that do not accommodate a transition from traditional to AI-enabled pricing models
Simple Scoring Matrix
Weights should be set according to the content and risk profile of the organisation; a marketing function and a regulated-industry legal function will typically weight these pillars differently.
| Pillar | Weight | Score (1-5) | Weighted Score |
|---|---|---|---|
| Model Strategy & Transparency | |||
| Quality Assurance & Human Oversight | |||
| Data Handling, Confidentiality & IP | |||
| Regulatory & Compliance Posture | |||
| Governance & Accountability | |||
| Commercial Structure, Reporting & Scalability | |||
| Total | 100% |
Closing Thought
Providers able to address each of these six areas with clarity and specificity are generally those that have invested in a genuinely managed approach to AI-enabled language services, rather than treating AI as a peripheral tool. Transparency is the common thread across all six (in model choices, data use, quality processes, and governance decisions) and is frequently the single most reliable indicator of which type of provider is under evaluation. A well-constructed RFP is one of the most effective ways to test for it directly.
