Stop the Bleed
Talent Is Walking
The translation industry is losing its talent base. Seniors are burning out, mid-career translators are drifting into other work, and graduates are thinking twice before joining.
The signs are obvious: per-word MTPE [Machine Translation Post-Editing, red.] rates that barely cover rent, brittle workflows that pile up edits, briefs with no context, and KPIs that reward speed over outcomes.
The result?
Work slows down under the weight of extra reviews, inconsistency creeps in, and risk grows quietly in the background.
If this were a project plan, it would live forever in the column marked “next sprint.”
Root Causes – It’s Incentives And Workflow Design
- Rate compression into MTPE pennies
- Context starvation in briefs
- Speed-only KPIs
- Seniors turned into machine babysitters
- Tools that break formatting and lose voice
The drain doesn’t come from AI itself; it comes from how companies have chosen to deploy it. Translators are still paid by the word, as if every word had the same value. Where once you might see 30 cents per word for professional localisation, now you’re offered three cents to post-edit machine output – with the same expectation of pride and craft. Spoiler: you don’t get it.
Briefs arrive stripped of context: no audience, no glossary, no idea what “good” looks like. KPIs glorify speed, so rework moves downstream where it’s usually more expensive(!). Senior linguists, who should be leading terminology and quality, get reduced to spellcheckers with a pulse. And the tools? Still breaking tables and losing voice in 2025. No wonder people leave.
Takeaway
When incentives reward the wrong things, the best people walk.
Fix the design – rates, context, metrics, roles, tools – and you start fixing retention.
The Hidden Business Cost Of Attrition
- Extra rounds of editing inflate time and cost
- Terminology drifts across markets
- Brand tone suffers and conversions drop
- Compliance exposure grows in regulated sectors
Losing talent doesn’t just mean fewer freelancers. It means your output quietly deteriorates while costs creep up in places you don’t track. Editors spend twice the time fixing rushed first passes. Terminology drifts until your German brochure and your French web copy describe the same product in completely different ways – and not in a good way! Customers notice the wobble in tone, and trust erodes. In regulated industries, no one wants to sign off on “the model’s” translation, so risk multiplies.
The invoice may look lean. But the hidden costs are fat.
Takeaway
Cheap quotes hide expensive realities. Attrition shifts spend to do-overs, inconsistency, and risk – all of which cost more to fix later.
The New Talent Stack
- Terminology and governance centralised
- QA led by risk, not volume
- Orchestration: model + data choice per task
- Data curation improves first-pass quality
- SMEs own meaning and speed up decisions
Teams that thrive put experts at the centre, but in roles that go beyond word counts. Terminology isn’t just a glossary; it’s a governance layer across every market. QA is no longer a tick-box at the end, but a risk filter applied where it matters most. Orchestration means choosing the right model and dataset for each task instead of pretending one size fits all.
Add in data curation – keeping style guides and examples fresh – and you set up both humans and machines for a stronger first pass. Subject matter experts (SMEs) step in quickly, making high-impact decisions that save hours of debate.
And here’s the kicker: none of this works without the right tools. If your pipeline still explodes a PowerPoint table, you’ve lost before you’ve started.
Takeaway
An expert-centred stack, supported by the right tools, keeps talent motivated while delivering consistency, speed, and trust.
A Five-Point Playbook To Stop The Bleed
- Pay for expertise, not keystrokes
- Human-in-the-loop done properly
- Replace junk KPIs with outcome metrics
- Fix the tooling to reduce friction
- Offer visible senior career tracks
So how do you actually keep talent? Start with pay. Link compensation to risk and difficulty, not flat word prices. Redefine “human-in-the-loop” so experts prevent rework instead of rubber-stamping it at midnight. Retire vanity KPIs and measure what matters: first-time-right, cycle time to approval, and incident rate.
Fix the tooling so linguists aren’t spending half their day reformatting. And give people a future: reviewer-in-chief, terminology lead, knowledge miner. People follow opportunity.
Takeaway
Retention isn’t mysterious. Pay fairly, measure outcomes, fix the tools, and offer a path forward.
Do that, and the bleed stops.
Future-Proofing Translation Talent
The bigger story isn’t just stopping the bleed – it’s building a system where talent thrives. An expert-centred marketplace isn’t a patch; it’s a shift in how value flows.
Enterprises get transparency: who did the work, what decisions were made, and how risk was handled. Linguists get paid for expertise instead of keystrokes. And the organisation gets continuity: decisions captured once, reused across every market.
That’s how you keep talent, scale quality, and adapt to whatever AI throws at us next.
Exfluency In Practice
This is why Exfluency was built the way it was. We saw where the industry was heading: lower per-word rates, demotivated experts, and endless rework hidden in the margins. Our answer was to create a marketplace that flips the model.
Hourly pay instead of per-word.
Machine translation can stay per-word, but the moment a linguist or SME steps in – to enhance, proofread, or trust-mine – the meter switches to hourly. Clients pay for time spent and value delivered, not arbitrary word counts. Our system predicts how long a project will take a given expert, so there are no ballooning invoices. It’s predictable, flexible, and fair.
Workflow automation.
Projects don’t sit in a manager’s inbox waiting for assignment. Our system automatically selects the right linguist or SME based on subject expertise, track record, and cost profile. That means faster turnaround, lower overhead, and proven quality – without the bottlenecks.
Client choice built in.
Not all material needs the same level of polish. For critical texts, clients can select senior experts; for less sensitive work, they can choose a cheaper option. Either way, they know exactly what they’re paying for, and why.
Even more flexibility.
Our platform also works if you’d rather insource the whole workflow. You can access the same professional-grade tools we use: neural MT engines, an online editor, and an AI Assistant that checks grammar and consistency as you go. You can even mix and match: translate and enhance in-house, then send only the high-stakes jobs to a certified specialist for the final touches. Outsource everything, insource everything, or land anywhere in between – the control is yours.
The outcome? Hundreds of thousands of euros saved for clients, not through smoke and mirrors, but by removing waste: fewer re-edits, leaner workflows, and talent working where it adds the most value.
Experts do expert work. Machines do machine work. Clients see faster approvals, lower costs, and more consistent quality. That’s what future-proofing looks like in practice.
How To Spot Talent‑Hostile KPIs
- Words per hour as the only hero metric
- Review time counted as “waste” rather than “risk control”
- Zero time budgeted for glossary and style decisions
- One fixed model for all tasks – because what could possibly go wrong?
- “Success” defined as fewer human minutes, not fewer incidents
Buyer Checklist
– Lift And Use
- Do we pay differently for high-risk or high-visibility content?
- Do experts own terminology and final meaning across markets?
- Are our KPIs about outcomes – extra editing avoided, cycle time, incident rate?
- Does our pipeline preserve formatting, tables, and voice by default?
- Do senior linguists have recognised roles and rates?
- Can we choose the right model and data source per task?
- Are decisions captured once and reused everywhere?
Book a meeting
If you’re wondering how our marketplace works in practice, you can read more about our approach.