Only as good as its data? Why human truth is essential for AI success

Some sections of the media give the impression AI is poised to solve pretty much every business and consumer-related challenge in the known universe. At the risk of raining on their parade, it ain’t necessarily so - for one fundamental reason: data. 

AI needs the right data in order to achieve its full potential. Without diverse, representative, unbiased human data that mirrors the richness of the real world, the raw power of an AI model is largely irrelevant. We’re all familiar with the cliche “garbage in, garbage out” in this context, but like so many cliches, it contains more than a grain of truth. 

Real world data means real world relevance

In fact, AI data doesn’t have to be garbage to impact a model’s results. Often what’s missing is simply real-world relevance. 

Without this, the model risks missing the subtle cultural shifts, consumer intentions, and real-world behaviors that add nuance to responses - with the model becoming less accurate, less useful, and less profitable in the process. 

The reason for this is that AI platforms powered by conventional data don’t cope particularly well with fine detail, ambiguity, or gradation. That might not matter in a highly structured area like maths, but for anything concerning consumers, it opens the door to error. 

That’s because, like it or not, humans are inherently messy, contradictory, and surprising creatures, which means results based on neat synthetic data can be generalized, biased, or obvious. 

For example, a colleague at GWI recently asked an AI model (which shall remain nameless to save it embarrassment) to create an image of what a senior solutions consultant manager looks like. This was the result:

In reality, the colleague - who is a senior solutions consultant themselves - is female and proudly adorned with tattoos and piercings. So it’s fair to say there’s something of a gap between the AI’s conception and reality. 

The reason for this is simple: LLMs source their data from internet platforms which don’t necessarily capture the broad range of human truth. 

The solution is equally straightforward: use a better data source, one that adds - rather than subtracts - meaning to the LLM’s output.

And who’s got that data? We have.

Solving “garbage in, garbage out” with GWI human data

In this context, GWI functions like a plug-in that gives AIs human insight, introducing much-needed dimensionality by supplying context and nuance. 

Our human data - think of it as a Large Human Model designed to complement Large Language Models - is the enabler that helps AIs create relevant, precise, and actionable outputs.

We see three big benefits that GWI’s structured, bias-resistant human data makes possible:

• Keeps AI anchored in reality. Real data, collected from real individuals, delivers real understanding and produces real results. For example, a leading retail AI tool recently improved conversion rates by 40% using AI-powered customer segmentation trained on GWI data.

• Ensure regulatory compliance. This helps avoid the risk of reputational damage - or worse - from questionable AI-generated content. Using our GDPR and CCPA-compliant, zero-PII data means enterprises can scale confidently without worrying about regulatory scrutiny. This isn’t an abstract issue: 73% of AI projects are delayed due to privacy and PII compliance issues (Gartner 2024).

• Stay bang up to date. While many AI models use outdated training material, we continuously refresh our data to capture changing consumer behavior and support effective personalization. For one client, AI-driven ad personalization using GWI data delivered 50% higher engagement rates across global markets.

We already provide the largest source of real, human data that powers today’s business decisions. That same human data is now helping revolutionize tomorrow’s co-pilots, chatbots, and LLM tools, giving them on-demand access to real-world data gathered from real-life individuals around the world.

The message we’d like you to take away from this blog is simple: it’s never been more important to have a rock-solid human insight layer. Without that, everything else AI makes possible is built of flimsy foundations.

Instant human insights with GWI Spark

So if that’s the case for human data in AI, the natural next question is, “How can I make use of it in real life?”. The answer is GWI Spark, our AI assistant. 

Whether you’re prepping a pitch, creating a campaign, or just curious about an audience, GWI Spark provides the rock-solid, real-world insights you need, fast. Type in a question using everyday language, and you’ll get an instant answer based on our global survey of nearly a million real consumers.

Need stats to power pitches, campaigns, strategies, and sales calls? Just jump onto GWI Spark and grab them, zero technical expertise required. Got a hunch? GWI Spark has the hard evidence you need to be sure your idea will work in the real world, easily accessible by anyone.

In short, GWI Spark is a goldmine of ideas and real-world consumer intelligence. Forget waiting for researchers to get back to you; with GWI Spark all your people can find exactly the insights they need, the nanosecond they need them.

Three use cases where human data makes a decisive difference

So far we’ve made the general case for human data within AI. Now let’s get specific, with three real-world use cases that show exactly what this makes possible.

Content marketing

Real-world psychographic and behavioral data makes it easier for AI to create content that connects. No more doubt or delay - instead AI can generate messaging, imagery, and campaigns that resonate with audiences.

Human data also lets AI create and shape highly nuanced audience personas directly within workflows. From tone of voice to interests, values, and motivations, the result feels personal, relevant, and easy to engage with.

Similarly, understanding which formats, channels, and tones will resonate most with different audiences means your AI can optimize tone by segment, generating email copy, banner content, and blogs that perfectly reflect the preferences of different groups. 

Finally, human data helps fine-tune prompts for maximum effect, something that’s particularly important for image generation - as we saw in the example above where an AI was asked to imagine a senior solutions consultant manager. 

Social media

Whether you’re powering a content calendar, a smart scheduler, or a social assistant, human data is the smart way to give audiences the tailored content they crave and make every post a scroll-stopper.

You can tailor posts to reflect real-world audiences, generating multiple post variations to reflect audience attributes and care-abouts. 

Need to understand when your audience is active, which platforms they prefer, and what formats they engage with most? Feed the right human data directly into your AI scheduler or content recommender to optimize timing, tone, and creative execution.

Lastly, stop worrying whether your audience prefers Reels, Stories, static posts, or carousels. Human data enables your AI tools to choose the right message, for the right audience, in the right format - every time. 

LLMs

Human data gives your AI the ultra-dependable input it needs to generate trustworthy, contextually relevant responses that reflect what your real-world users care about.

Enrich your tool’s prompts and responses with psychographic traits, interests, and values. Whether it’s answering a product query or drafting an email, your LLM can shape its output to reflect the preferred tone, expectations, and motivations of your users.

Many LLMs default to vague or catch-all responses that don’t inspire user confidence. Human data makes your tool’s responses credible and trustworthy, from tone and product positioning, to value framing.

Ultimately human data is about giving your AI a human edge. Suddenly your digital assistants and agents will be able to faithfully recreate the language patterns of specific audiences discussing specific topics - for example, how Gen Z might talk about sustainability - so your AI customer support, sales bots, and vertical-specific agents sound natural.

Last words: Future-proofing your AI starts with human truth

AI success is about more than just the model itself; equally important - arguably more so - is the data the AI uses. Unless this includes human truth the results risk missing out on what really matters.

For AI companies human truth means relevance. Many widely used AI models were originally trained on pre-2021 datasets, leaving them blind to evolving consumer behaviors, shifting preferences, and cultural nuances.

For enterprises, it means AI-generated content that reflects real audiences, not biased assumptions. Synthetic data tends to amplify biases rather than correcting them, making personalization less effective and outputs less reliable. 

And for investors, human truth means sidestepping the risk of bad data. AI hallucinations can be costly, with 65% of AI failures stemming from poor data quality. Mistakes with AI data can lead to eroding consumer trust and regulatory risk. 

Connecting tomorrow’s co-pilots, chatbots, and LLM tools to this sort of data gives them on-demand access to real-world data gathered from flesh-and-blood individuals around the world. The bottom line is that human truth is the rocket fuel that will power the next generation of AIs.

FAQs

Why is data quality critical for AI?

AI outcomes depend directly on input data. Poor data leads to inaccurate insights, biased results, and ineffective decisions.

What is human  data?

Human data refers to insights collected and validated directly from real people, ensuring accuracy, representativeness, and contextual depth that purely algorithmic data collection can’t provide.

How does GWI ensure data accuracy for AI?

GWI’s global scale, robust weighting, detailed quotas, and depth of human-validated profiling points ensure high-quality data ideal for powering reliable AI-driven decisions.