Global enterprise adoption of AI is expected to surpass 80% this year, and one thing is becoming increasingly clear to organizations: AI systems are only as powerful—and as global—as the data behind them.
In our recent Chinese and English webinars on “Driving High-Performing AI with High-Quality Multilingual Data”, our Customer Success Director Jill Huang, Program Director James Li, and Content Manager Jake Syropoulo unpacked the rapidly shifting AI landscape and explained why high-quality multilingual data is now the cornerstone of high-performing, inclusive AI.
If you missed the live sessions, here are the key insights.
1. The Global AI Boom and Why Multilingual Data Now Matters Most
In the English webinar, Jake opened the session with a look at the unprecedented pace of AI adoption. Gartner now projects global AI spending to exceed USD $2 trillion by 2026, reflecting explosive year-over-year growth. But Jake emphasized a critical shift: companies are no longer experimenting with AI. They’re deploying it to solve real operational problems.
As enterprises expand AI across more markets, they’re facing the new reality that AI needs to function accurately in every language it encounters. Jake highlighted that multilingual data has become the “backbone of accurate and inclusive AI,” shaping not only language understanding but also fairness, cultural alignment, and global usability.
2. The Multilingual Divide: A New Barrier to Global AI Adoption
Jake highlighted a concerning trend: a language-driven digital divide. As he explained, AI adoption isn’t lagging because global interest is low; it’s lagging where language support is weak. Markets with strong language models (like South Korea) are pulling ahead, while others struggle due to insufficient local-language coverage.
Jake also explained a structural challenge shaping this divide: English accounts for up to 90% of AI training data in frontier models. As a result, performance drops significantly in other languages because the models lack sufficient exposure and cultural grounding.
3. Data Quality: The Real Bottleneck Preventing Better AI Performance
Across both sessions, Jake and James reinforced a key message: data—not model architecture—is now the primary barrier to AI performance. According to James, as large models plateau in capability, organizations are discovering that high-quality, well-annotated data determines whether AI succeeds or fails.
Jake cited research showing that data preparation can consume up to 80% of the total project effort, far exceeding the time spent training the model itself. He echoed AI pioneer Andrew Ng’s conclusion that “now that models have advanced, we’ve got to make the data work as well.”
In the Chinese session, James expanded on this by breaking down the key challenges:
- Inconsistent annotation quality
- Lack of cultural and linguistic expertise
- Bias or incorrect interpretation
- Scarcity of trained linguists, especially in low resource languages
- Growing compliance and security requirements in regulated industries
The consequences are unavoidable as poor data produces weak models and elevated risk, especially in sectors like finance and healthcare.
4. The Data Services Landscape Today — and Why Clearly Local Is Uniquely Positioned to Lead
James shared an unfiltered look at the current state of the data-services industry, a sector undergoing profound disruption.
The Status Quo: A Market in Turbulence
He highlighted several forces reshaping the field:
- General-purpose data is saturated. Most leading AI companies have already built their foundational datasets.
- Vertical industries are increasingly self‑sufficient, generating real-world annotation data through user interactions.
- Automatic AI-driven annotation is improving, reducing demand for labor-heavy human labeling.
- Intense cost pressure is pushing quality down, leading to rushed, low-fidelity outputs that harm model performance.
- Competition now comes from domain-specific software providers with proprietary data and industry credentials.
The result? A crowded sector where generic annotation work is declining, and true expertise—not scale—is becoming the real differentiator.
Why Clearly Local Is Built for This Moment
James explained that Clearly Local is uniquely aligned with what the market now demands:
- Deep multilingual and low-resource language coverage
- Long-standing, ISO-aligned quality systems
- Proven expertise in third-party evaluation and model auditing
- Strong cultural insight across global markets
- A specialization in customized, high-stakes, client-specific solutions
- Mature project management capabilities inherited from years of localization excellence
This combination perfectly positions Clearly Local to solve the next generation of multilingual AI challenges.
5. How Clearly Local Powers High-Quality Multilingual Data for AI
With this foundation in place, Jill then walked attendees through how Clearly Local translates these strengths into real, measurable impact for AI clients. She outlined how Clearly Local is becoming a strategic collaborator that supports clients across the entire AI data lifecycle.
Our Core Multilingual Data Capabilities
Jill walked through the four pillars of our data services:
- Data Collection & Generation
Including human-written training data, content expansion, and multilingual scenario creation.
- Data Annotation
Text, audio, image, and video annotation with cultural and linguistic precision.
- Data Validation
Scoring, comparative evaluation, error classification, and fine-grained linguistic review.
- Prompt Engineering
Systematic testing to reduce hallucinations, enhance safety, and improve multi-turn interaction reliability.
Real Project Examples Shared
Jill highlighted cases where:
- Clearly Local produced over one thousand pieces of 100% human-written English training data within a few weeks to ensure uncontaminated model inputs.
- Linguists evaluated LLM outputs across multiple languages using both binary yes/no and granular scoring systems.
- Teams validated AI-generated chat responses based on relevance, tone, and accuracy using structured criteria.
Technology & Workflow Excellence
Jill also showcased Clearly Local’s internal annotation platform, supporting multimodal data, configurable QC (quality control), and scalable workflows, which improve efficiency by up to 30% compared to spreadsheet-based processes.
6. High-Quality Multilingual Data Is the Path to Global, Inclusive AI
The message from our speakers was this: As AI moves from experimental to essential, the organizations that prioritize high-quality multilingual data will build systems that are more accurate and ready for global use.
By investing in the right data infrastructure—supported by expert linguists, rigorous workflows, and culturally grounded testing—you set your AI up to serve everyone, not just English-speaking markets.
Whether you’re building advanced multilingual AI products or navigating your first data preparation workflow, Clearly Local is ready to support you.
