The rain came in hard sheets against the café window as Lina opened the email that would shift the ground under her career. The subject line was cheerful—New project and revised terms!—but the body read like a different language altogether. Where she used to receive a clear brief, a word count, and a rate, the message now spoke of AI assistance, MT post-editing, productivity expectations, and algorithmic quality scores. Her usual certainty—do the work well, bill the words, deliver—suddenly felt flimsy. She wanted fairness and clarity, a way to grow with the technology without being crushed by it. She also wanted to maintain the craft that had kept her clients loyal: judgment, nuance, and responsibility for the message on the page. The promise lurking between those new terms, she suspected, was that there is a way to make these contracts work for everyone. If she could understand how AI and MT post-editing reshaped the work itself, she could reshape the agreement that framed it.
When the barista called her name, Lina took the coffee and made a quiet promise to herself: no panic, only questions and structure. She would ask what the model produced, what the client expected to fix, what quality meant in measurable terms, and who owned the risk when a cheerful sentence turned dangerously wrong. Somewhere between the new tools and the old responsibilities lay a contract that could honor both, and that is where our story heads next.
When words stop being the unit of value, contracts must catch up. In human-only workflows, many agreements revolved around a per-word price and a deadline. MT post-editing changes the center of gravity. The machine produces a draft that looks fluent at a glance, inflating the illusion of speed and reducing the visible value of human expertise. But the real unit of value has shifted from typing to judgment. The work is about discerning what to keep, what to reshape, and what to throw out entirely. That change needs to show up in your agreement.
Start with expectations. Clients often ask for two flavors of post-editing without defining them: a fast polish that preserves the machine’s structure, and a deep rework that achieves near-human parity. Those are fundamentally different services, each with different risk, time, and liability. Your contract should name them clearly—call them light PE and full PE—and describe acceptance criteria in plain language. Light PE: correct meaning errors, grammar, and glaring style issues, but do not re-architect the text. Full PE: deliver a publication-ready outcome with domain consistency, tone alignment, and structural fixes. The two tiers should map to different rates, throughput ranges, and review cycles.
Next, address the myth of the 90% draft. Machines generate fluent sentences that can conceal wrong facts, misapplied terminology, or mistransferred negation—errors that are costly and sometimes dangerous. The contract must state that machine fluency does not equal correctness, and that effort scales with error density, not word count. Build in a discovery step: a small pilot where you sample the output, track edit distance, and calibrate throughput. That pilot becomes the anchor for final pricing and for change-order triggers if the live batch deviates.
Finally, name the risks that did not exist before. If the client’s system or vendor engine can retain or learn from the input, your agreement needs a no-train clause, privacy terms, and data handling rules. If the content includes personal or regulated data, define redaction duties and acceptable tools. If the work includes high-risk domains—medical leaflets, aviation procedures, legal notices—make clear that MT post-editing may be inappropriate and that human-only work or additional review is required. By surfacing these realities upfront, you align the contract with the actual shape of the work.
Clear scope, measurable effort, and ethical data rules are your new best tools. Scope begins with definitions. Spell out what is being delivered (light or full post-editing, or human-only), what the client will provide (source files, legacy termbases, style guides, engine settings), and what the workflow includes (pilot evaluation, linguistic QA, back-and-forth queries). Replace blanket per-word pricing with a hybrid: a base fee for setup and pilot, tiered rates for different edit intensities, and an hourly safety valve for segments where the machine output is unusable.
Quality becomes measurable when you add acceptance criteria. For light PE, acceptance might be “no meaning errors; grammar, punctuation, and basic style corrected; terminology aligned to the glossary when present.” For full PE, it could be “fit for publication; tone adapted to purpose; structural and idiomatic naturalness achieved; numeric and legal references verified.” Tie those criteria to review steps: a sample delivery, a client check, and a feedback loop that locks the standard after the pilot. If the client expects higher throughput due to AI, freeze that expectation to pilot evidence rather than hope. For example, “Target throughput is 1.8x the baseline established in the pilot; if edit distance exceeds pilot by 20% or more, a change order triggers.”
Data and ethics require their own lanes. Insert a clause stating that no content will be sent to public models or third-party tools without written approval, that any approved system must be configured not to retain or train on client data, and that all temporary files are securely deleted. Specify redaction procedures for personal data. Name the tools permitted for post-editing and QA, and commit to keeping edit logs on request to support audits without exposing private notes.
Liability needs nuance in the AI era. Clarify that the provider is responsible for errors introduced or missed during the defined scope of work, but that defects originating in the machine output, the source text, or client glossaries reduce or shift liability. For high-stakes deliverables such as certified translation, reserve the right to bypass MT entirely. Lastly, include governance for living documents: as the engine or domain evolves, you and the client review performance quarterly, adjust rates and workflow, and retire assumptions that no longer hold.
Turn contracts into practice with pilots, metrics, and change triggers that protect both quality and time. Begin every new AI-assisted engagement with an intake checklist. Ask for domain samples, the engine configuration if accessible, any forbidden terms, and the product’s risk profile. Request a 500–1,000-segment pilot across typical content varieties—marketing flair, procedural instructions, error messages. Measure edit distance using your tool of choice, but also tag error categories to reveal what the machine gets wrong: tense, terminology, named entities, numbers, negation, and register. These data points move the conversation from gut feel to verifiable scope.
Center the workflow on decision gates. Gate 1: Feasibility. If the pilot shows high edit distance, unsalvageable segments, or inconsistent domain handling, shift those content types to human-only and keep post-editing for the rest. Gate 2: Throughput. Convert pilot numbers into a realistic daily plan with buffer: AI sometimes stalls on idioms, units, or long lists, and your editing speed will dip during research. Gate 3: Quality assurances. Lock your style guide, glossary, and query process before bulk work begins. If the client cannot provide a glossary, build a seed term list from the pilot and get sign-off; otherwise, the machine will keep inventing near-synonyms that drain time.
On the ground, measure and adapt. Track time per batch, the percentage of segments requiring heavy rework, and the top recurring error types. Share a simple weekly summary with the client—no proprietary screenshots, just the metrics that affect schedule and cost. When you cross a threshold (for example, heavy rework exceeding 25% of segments), trigger the change-order clause you planted earlier. That clause is not a punishment; it is a fairness mechanism acknowledging that the machine’s behavior can shift with new domains or updates.
Protect the text and the humans. Use offline or enterprise-approved tools for sensitive projects, and disable telemetry that isn’t necessary. Keep examples of critical fixes—especially machine-induced meaning flips and number errors—as anonymized case studies for future onboarding. Maintain a playbook for ambiguous segments: escalate to the client with options, keep a “decision log,” and reflect those decisions in the termbase so the engine and your team stop repeating the same debate.
Finally, leave space for growth. If a client wants to ramp up volume based on apparent time savings, align that ramp with training: brief reviewers on the post-editing standard, teach project managers how to read your weekly metrics, and ensure they understand that speed comes from consistency and context, not only from the machine. The paradox of AI is that it rewards human process discipline more than ever.
The heart of this shift is simple: agreements must describe the work we actually do, not the work we did five years ago. AI and MT post-editing have moved language service contracts from counting words to managing risk, clarity, and measurable quality. When you define service tiers, ground speed promises in pilot data, and set ethical rules for data handling, you create a framework that respects craft and embraces useful technology. You also give clients something they secretly crave: predictability.
If you take one lesson from Lina’s rainy evening, let it be this: ask better questions, then write them into the contract. What is the intended use and risk of the text? Which sections are suitable for post-editing, and which require human-only? What are the acceptance criteria and change triggers? Who owns the data, who can see it, and for how long? When those answers are explicit, AI becomes a helpful colleague instead of a silent assumption.
Now it’s your turn. Review your current agreements and mark where the unit of value is still just a word count. Add a pilot, define the tiers, name the risks, and give yourself fair levers when reality shifts. Share your experiences and what clauses have served you best, and pass this along to a colleague who is wrestling with the same changes. The future of our work is being negotiated right now, one clear contract at a time.
