Act 1: The Snapshot

We begin with our feet firmly planted in the present and reflect on where the language services industry currently stands (spoiler: there’s no way to avoid this thing called GenAI).

But do we understand where we are and where we are headed? Are we asking the right questions, or should we just start with the “Why”? 

There is a sense of urgency that powers the varied market players. We explore new pathways, test new solutions, learn from the mistakes made along the way, course-correct, and start again.

The industry is evolving, and we are in motion. In the following pages, we examine what this movement looks like. 

Etgar Bonar

Chief Marketing Officer at Lokalise

Etgar Bonar is CMO at Lokalise, with prior leadership roles at Rapyd, Taboola, and Amazon. He specializes in global marketing and growth strategy, holds an MBA from London Business School, and enjoys being by the sea, whether running, cycling, or diving.

AI has forced a reckoning in localization. For some teams, AI is helping deliver content faster, localize with better quality, and prove ROI faster than ever before. For others, it’s exposing just how fragmented, under-resourced, or outdated their workflows have become. 

The result is a widening divide, not just between large and small companies, but between teams that have a clear strategy and those who can’t get out of reactive mode. Some are scaling their efforts through smart automation. Others are still stuck trying to justify basic investments or untangle manual processes. 

The reshaping is already happening. The ones moving now are already pulling ahead. The rest are falling behind.

Where teams are falling behind

1. No dedicated ownership

Many smaller companies don’t have a dedicated localization lead. Instead, marketing managers, product owners, or engineers are expected to manage localization on top of everything else, often without training, support, or tools. As a result, decisions get delayed, are made in isolation, or are disconnected from broader business goals.

2. No data, no budget

Teams without proper tools and processes often can’t track turnaround times, reuse rates, or translation costs in any meaningful way. That makes it hard to demonstrate ROI, and even harder to justify budget improvements. The cycle repeats: underinvestment leads to inefficiency, which leads to more underinvestment.

This aligns with our research: while 74% of surveyed companies recognize localization as a revenue driver, 62% struggle to execute it successfully. These teams are stuck in the middle, under pressure to show impact, but unable to make the case with data. 

3. Enterprise red tape

Large companies may have localization teams and tooling, but often face a different kind of barrier: compliance. AI and tool adoption stalls under layers of procurement, legal review, and security risk assessments. Months can pass before a pilot gets approved, and by then smaller competitors may already be shipping localized content faster and at scale.

4. Too risk-averse to experiment

Across company sizes, hesitation is slowing down progress. In our research, 49% of teams cited quality concerns around AI, and 37% were worried about data privacy. But with over 80% planning to implement AI in the next year, many are already behind the curve, and not experimenting now means losing even more valuable time to test, learn, and iterate.

5. Tooling perceived as too complex

More than half of companies reported that localization tools are too complicated to implement. That perceived complexity creates friction before a pilot even begins, especially for teams with limited technical resources. The result: delayed adoption, fragmented systems, and workflows that never scale.

How to start moving

1. Pick projects that prove value fast

If you’re struggling to get buy-in, skip the risky flagship campaigns and start with lower-visibility content: internal docs, long-tail web pages, or support content. Use these as test cases to measure cost, speed, and quality, and then show what changes when you localize with the right tools and processes in place.

2. Assign clear ownership

You don’t need a headcount to get started, you just need clarity. Assign a specific person to track performance, define processes, and flag blockers. If no one owns localization, nothing improves. Even part-time ownership gives you a foundation to build on.

3. Start with AI where it’s easiest to control

AI doesn’t need to be rolled out everywhere at once. Start where the stakes are lower: internal tools, bulk content, or file prep. Use these areas to evaluate performance, build internal trust, and learn how to improve output. Early experimentation makes you faster and smarter later.

4. Use tools that are easy to adopt

Implementing new tools stalls when teams assume every solution will take six months and a custom integration. Look for platforms that connect you to the tools you already use, offer clear onboarding, and don’t require a lot of developer input. Localization tools are often not as complicated as people think, but projects are often delayed because people assume they are. 

5. Make localization visible across teams

Localization often operates behind the scenes, which means wins can go unnoticed. When a pilot achieves faster turnaround, lower costs, or improved quality, make sure stakeholders across product, marketing, and leadership hear about it. What gets seen gets supported, and visibility builds momentum. Building cross-functional awareness helps reduce friction later and reinforces that localization is worth investing in.

Momentum compounds

Small steps in localization can have a snowball effect: a successful pilot leads to a better process. A clear result unlocks new investment. Assigning ownership turns plans into progress. The gap between teams falling behind and those moving ahead isn’t permanent. It’s the product of daily decisions, and it can start to close as soon as teams commit to moving.

Read the full 132-page Global Ambitions: (R)Evolution in Motion publication featuring vital perspectives from 31 industry leaders on the ongoing AI-spurred (r)evolution.

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