Key Points to Consider for AI Data Collection

AI is reshaping industries and redefining user expectations, increasing the pressure to build and deploy intelligent systems. As models become more powerful, however, the risks associated with poor data quality grow just as quickly.

While volume and speed are important, effective AI data collection is really about intention: sourcing the right kind of data from the right people under the right conditions. Synthetic inputs simply can’t match up to real-world, human-sourced data that captures the nuance of how people actually interact with your products.

At Applause we’ve seen — and proven — firsthand how foundational data quality is to model performance and trust. From conversational chatbots to intelligent agents, we help some of the world’s largest companies refine or implement their data collection strategy with three key principles in mind: diversity and scale, human authenticity and harm reduction.

Here are some of the most important AI data collection considerations for building AI products that deliver great results.

1. Prioritize data diversity and scale

The most effective AI models train on data that reflects the full spectrum of intended users. Demographic diversity is key, but it doesn’t end there. We look at geographic, cultural and even professional representation. Testing a healthcare chatbot requires very different expertise than testing one for legal services.

According to Applause’s 2025 State of Digital Quality Report, 72.3% of organizations are currently developing AI applications or features. Specifically, 55.4% are building chatbots or customer-support tools and 40.1% are developing AI for predictive analytics. If inclusive testing with diverse data is not part of the strategy, there’s a real chance the end product will fall short in a real customer context.

Source contributors from both relevant domains and general user pools. Applause leverages its global community to evaluate products from all angles — including the unpredictable ones.

Consider the context beyond domains as well. Is your AI system intended for global market use? Are there cultural sensitivities or regional norms that might affect how users interpret responses? In one project, for instance, we had testers across Japan evaluate chatbot responses for tone and politeness — qualities that carry significantly different meanings across cultures. These contextual insights directly inform product localization before launch.

2. Focus on high-quality human-sourced data

Our report indicates that 65% of users have encountered issues with generative AI usage in just the past three months. In too many of these instances, quality human-sourced data has taken a backseat to quick fixes.

Synthetic data can support large-scale training but it will never replicate the nuance — or, as some might call it, chaos — of real-world human behavior. People misinterpret things. They make typos. They ask unclear or inappropriate questions. Those are exactly the kinds of inputs that expose whether the AI is ready for launch.

Human contributors bring this unpredictability to the testing process. At Applause, we regularly test with participants who have no prior product knowledge to capture unique perspectives and see what outputs they return under unclear or misleading queries. According to the same survey, 61% of organizations still rely on humans to grade AI prompts and responses, underscoring the importance of real testers.

Applause also assembles subject matter experts who know the right questions to ask. That dual approach surfaces everything from logic errors to tone mismatches; these can be make-or-break moments for brand trust.

3. Design data collection to mitigate AI harms

Even the best-trained models can cause harm if they reflect underlying biases or fail to consider how they’ll be misused. That’s why red teaming is a critical part of modern AI development, yet only 32.5% of organizations currently use adversarial testing, according to our survey. This suggests a major gap in AI quality assurance processes. At Applause, we build centralized adversarial testing teams that specialize in stress-testing AI systems within specific domains such as healthcare, finance or legal tech.

Functional bugs are one thing; they are clearly understood in the broader context of a system. Potential harms are another, and they can be difficult to spot. Could a chatbot offer problematic advice? Could it be tricked into revealing something dangerous? We work with real people from real communities to gather feedback grounded in the human experience, unveiling issues that theoretical scenarios simply won’t.

Why human testing still wins

There’s no question AI will continue to play a bigger role in both creating and analyzing test data to surface trends and cluster insights quickly. But AI can’t replace the human judgment required to interpret those insights or the lived experiences that inform real-world usage.

This viewpoint is widely shared across technology organizations. While 60% of respondents in our report say they use AI to aid testing, less than one-third actually put it to use in building test cases regularly or in test reporting. AI is improving at the task — and will continue to get better over time — but there’s still a long way to go.

What works in a prompt lab doesn’t always translate to the real world. That’s why brands all around the world continue to invest in real-world testing solutions powered by real people. Applause leverages its global community of more than one million digital experts and end users across more than 200 countries and territories. This community allows us to rapidly assemble diverse, custom teams, testing on real devices in real-world environments to help ensure applications are functional, intuitive and inclusive.

Applause’s AI Training and Testing solution is built upon a full lifecycle approach that includes data collection, human feedback and large-scale diverse AI training. This includes sourcing diverse, high-quality human-sourced datasets (audio, video, image, text, geo, AR/VR) to support training and model fine-tuning. We also specialize in human grading/annotation of prompts and responses, which is critical for reinforcement learning from human feedback (RLHF). This grading assesses model output against multiple dimensions, including accuracy, completeness, harmfulness, tone and context

Crucially, our commitment to real-world performance includes red teaming expertise. We conduct adversarial testing using experts and generalists to identify and minimize model risks. This focused testing identifies major harms like bias, toxicity, inaccuracy, hallucinations, misinformation, privacy breaches and malicious use. Testers make use of specialized adversarial methods, including role playing, overwhelming the model and technical hacks, which are designed to trick the system and bypass built-in ethical filters.

If your AI strategy depends on trust, context and quality, let’s have a chat to learn more about your data collection and validation plan.