Winning your budget conversation: How to prove translation ROI and protect your spend

Insights from Gavin Grimes, VP of Language Services at Smartling, and Bryan Murphy, CEO at Smartling

Securing budget has never been easy for localization teams. Now, with AI reshaping how companies think about efficiency and cost, the conversation has gotten more complex. Executives expect faster turnarounds, lower spend, and proof of impact on growth, all at the same time. For localization leaders, that means the challenge isn’t just asking for budget, but keeping it.

In the third session of Smartling’s AI Translation 101 series, CEO Bryan Murphy sat down with Gavin Grimes, VP of Language Services at Smartling, to share real-world strategies for winning budget discussions in an AI-driven world. From avoiding common mistakes to reframing translation as an investment, they covered the frameworks and tactics teams need to defend their spend and expand their programs.

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How AI is changing budget conversations

A few years ago, budget talks were mostly about capacity and cost: words translated, turnaround times, and per-word rates. Today, with AI mainstream, executives expect two things at once: efficiency gains (faster turnaround, lower unit cost) and proof of value (impact on growth, engagement, and/or risk mitigation).

Gavin’s advice: frame AI as a controlled accelerator, not a blunt cost-cutting tool. He also suggests coming prepared with evidence of reduced cycle time, quality metrics, and business outcomes with your proposed AI-incorporated budget plans.

Key takeaway: Because executives now expect both efficiency gains and proof of business value, AI must be framed as a growth enabler, not a cost-cutter.

3 mistakes that will sink your budget pitch

Gavin highlighted three pitfalls he learned from experience:

• No baseline. Without benchmarks to measure improvements against, leadership will be less likely to buy into your requests for budget to improve those metrics.
• Asking in isolation. You’ll find more success when you tie your request to company goals, not just team needs.
• Ignoring the audience. Everyone is juggling a different set of priorities, so tailor the story for each stakeholder.

His example: a glossy 40-slide deck flopped when presented to the CFO, CMO, and CEO at the same time. The fix was three short, data-driven one-pagers: ROI for the CFO, market differentiation for the CEO, and brand speed for the CMO—the same ask, reframed, and it worked.

Key takeaway: Avoid going in blind—use data, align to company goals, and tailor your message for each stakeholder.

From cost center to business investment

Gavin put it succinctly: “The trap is being seen as a cost center.” Instead, he advises tying localization spend to business outcomes, such as:

• Revenue mechanics. For example, a team might show how localizing German marketing assets could drive ~€/$1M in incremental revenue at a 3% conversion rate.
• Buyer behavior. Research consistently shows >75% of customers prefer products in their own language, and ~40% won’t buy otherwise.
• Risk mitigation. Demonstrate how localization prevents fines, miscommunication, and/or churn.

He also recommends strategically changing how content is translated based on the importance of the content:

• Tier 1: High-visibility, revenue-driving content, like marketing assets → AI translation with a human in the loop, like Smartling’s AI Human Translation
• Tier 2: Medium-visibility content, like website pages → MTPE (machine translation post-editing) / AIT (AI translation)
• Tier 3: High volume, low-visibility content, such as help center documentation → MT (machine translation) 

Finance loves the discipline signal: invest more where it matters, spend less where it doesn’t.

Key takeaway: Reframe localization as a revenue driver and risk mitigator, using portfolio discipline and clear ROI examples.

Metrics executives actually care about

Executives don’t care about translation platform features. They care about numbers that tie directly to business priorities. Consider supporting your budget requests with the following metrics:

• Cycle time reduction: Faster launches, earlier revenue recognition.
• Revenue impact: Conversion lifts, higher attach rates, lower churn.
• Cost avoidance: Spending now to prevent bigger costs later.
• Productivity gains: Tangible increases in words/hour or quality metrics.

Bryan stressed that localization leaders should be proactive—go on the offensive and show leadership how the team is driving growth, reducing churn, and improving go-to-market with clear ROI.

Key takeaway: Show cycle time, revenue impact, cost avoidance, and productivity—then package it into a simple, CFO-friendly dashboard.

When leadership assumes “AI = half the budget”

It’s a common misconception: if AI can help us do 50% more, shouldn’t localization spend drop by 50%? Gavin’s advice: flip the conversation.

Instead of “same work with less budget,” position it as “more impact with the same budget.” AI allows teams to cover more languages, localize more content, and move faster—without cutting corners on quality.

A stage-gated approach works well here: show responsible savings on lower-risk content (like FAQs), and reinvest those gains into high-impact initiatives, such as launching into new markets.

Shift the conversation from cost elimination to value redistribution:

• Differentiate content. Don’t cut high-risk, revenue-critical content; push savings on lower-visibility/utility content (FAQs, internal docs) where AI safely excels.
• More with the same. Frame AI as doubling impact with the same budget (e.g., 20 languages instead of 10; more long-tail content; faster, parallel releases).
• Quantify revenue at risk. Cutting Spanish product description pages might “save” now but risks a large revenue impact if ~40% won’t buy in English.
• Mind brand governance. Keep human oversight where it protects brand and compliance.

Bryan reinforced this: he emphasized that CEOs hate revenue at risk—for a CEO, “saving $100k to risk $12M” isn’t a good trade.

Key takeaway: Position AI as a way to double impact with the same budget, not slash spend. Prove responsible savings and reinvest in growth.

Planning next year’s budget with AI baked in

Smart teams don’t treat AI as a one-off line item; they bake it into the operating model:

• Budget for enablement tools. Remember to budget for termbase/TM cleanup, style guides and prompt libraries, quality evaluation, training post-editors/reviewers—without these, AI won’t deliver the ROI you promised.
• Build flexibility. Include “burst” clauses with vendors to handle content spikes without emergency POs.
• Tie to OKRs. Associate your strategic plan with the key results that executives are focused on. For CFOs, that may be lower cost-to-serve (cost per release, timeline). For a CMO, it may be faster global rollout (cycle-time gains). For CEOs, it might be faster entry into two markets (capacity freed by AI).
• Stage-gate larger spends. Break larger investments into phases, and earn trust by demonstrating  measurable progress at each milestone.

Gavin noted that “the smartest teams aren’t betting the house on AI. They’re structuring their budgets with flexibility, so they can scale fast if a pilot succeeds, but contain risk if it doesn’t.”

Key takeaway: Smart teams split budgets into “run” and “experiment,” funding AI responsibly while building flexibility to scale successes.

Bundlinjen

Session 3 of Smartling’s AI Translation 101 series made one thing clear: winning the budget conversation in the age of AI requires more than cost-cutting. Leaders need to show data-driven ROI, tailor their message to stakeholders, and position localization as both a growth driver and a risk mitigator. From avoiding common mistakes to building flexible and strategic budgets, Bryan and Gavin shared practical strategies that localization teams can put into action right now.

For a deeper dive, check out our ebook Navigating the shift: Why, when, and how to adopt AI translation.

Don’t forget to watch the full session or listen on the podcast.