Everyone talks about AI search as if it’s already universal – as if we’ve collectively evolved, users have changed, and discovery has changed for everyone. But the reality is much less simple.
As AI research develops rapidly, it is not adopted uniformly. The gap is increasingly shaped by something we rarely discuss in research: household income.
My agency has been tracking how people search since early 2025. Looking at household incomes, we saw a clear and significant divide. Overall, about 27% of people say they use ChatGPT regularly. But when you break this figure down by income, the situation changes dramatically.
- Homes £25-30,000: around 18% utilization
- Households of £50-60,000: around 30% usage (the average UK household income falls into this bracket based on the financial year ending 2024)
- Households £70-80,000: ~49%
- Households over £100,000: ~48-58%
In other words, higher-income households are more than twice as likely to use generative AI tools. This is not a small variation. This challenges one of the most important assumptions shaping research strategy: AI adoption happens at the same rate for everyone.
We are seeing the emergence of a new type of digital inequality in the way people access information and make decisions. This divide does not exist in isolation.
Across the UK, FutureDotNow has found 52% of working age adults cannot perform all essential digital tasks required for the job. AI adoption adds to an existing digital skills gap, one that already determines who can confidently access, evaluate and act on information.
As writer William Gibson said: “The future is already here – it’s just not distributed fairly.” »
AI adoption is about more than access to tools. It is shaped by human behavior, in particular:
- Access.
- Ability.
- Trust.
Access: Who is exposed to AI in their daily life?
If you work in a digital, enterprise or knowledge-based role, you are much more likely to be encouraged or expected to use AI. It’s part of your workflow. This is reflected in our data, where industries like IT and business consistently lead adoption, reinforcing how workplace exposure accelerates behaviors.
If not, your exposure may be limited to headlines, media stories, or second-hand experiences. This creates a very different starting point.
Ability: Do you know how to use it?
For those who use AI regularly, prompting becomes second nature. You learn to refine, question and expand on results. For others, this first interaction may seem unfamiliar, even intimidating. Without guidance, many simply don’t get started.
Trust: Do you trust it enough to rely on it?
This is where things get particularly interesting. Trust varies not only by platform, but also by mindset. In our research, platforms like Perplexity score very well on trust, but they remain relatively specialized.
Which raises an important question: Are users who adopt these tools early also the most confident in navigating and validating AI results?
It’s likely. This reinforces a larger point: AI adoption isn’t just a technology curve, it’s a people curve.
As AI becomes integrated into the way people research and decide, AI proficiency risks becoming the next layer of the digital divide, amplifying the advantage of those who are already digitally confident.
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Research is fragmenting, which has real business consequences
Different audiences develop different behaviors:
- AI-first users → Delegate tasks, summarize, pre-select.
- AI-assisted users → Validation on all platforms.
- Users avoiding AI → Relying on Google, retailers and communities.
These behaviors are not corrected. The same person can use AI to write a legal letter, but still turn to Google when researching a product.
Habits take time to form, and right now people are experimenting. This means that we do not simply move from one research path to another; rather, we are witnessing a fragmentation of discovery into several distinct paths.
This fragmentation is not just a change in behavior. This has direct commercial consequences. If you assume that your audience behaves like early adopters, you risk making poor strategic choices.
Overinvesting in AI optimization can mean missing out on traditional users, while overindexing on Google can mean missing out on AI-led users. Ignoring trust gaps can also erode trust.
The Opportunity: Your Most Valuable Audience May Already Be AI-Driven
There is a real advantage to this divide. The audiences that adopt AI the fastest are often valued by many brands: decision-makers, professionals, and higher-income consumers.
Our data shows that these users often align with what we define as “digital explorers,” early adopters who already delegate some of their decision-making to AI by comparing options, summarizing information, and pre-screening before even visiting a website.
Behavior is just one layer. Below that is trust, which determines how far users are willing to go with AI.
When you map behavior from this perspective, three clear patterns emerge:
- High-trust users → Able to delegate to AI.
- Medium trust users → Probably cross-check across all platforms.
- Unconfident users → Rely on familiar environments.
Different behaviors, backgrounds, expectations and, above all, content needs.
How to respond to fragmented search
As these high-value, AI-driven users delegate their decisions earlier, the goal now is to be understood, highlighted and recommended by AI tools, before a click occurs.
1. Segment by behavior, not just demographics
Age or income may explain who your audience is, but not how they decide. To achieve this, you need to go beyond superficial segmentation and build a behavioral understanding of discovery, combining quantitative and qualitative insights.
Quantitative data shows you large-scale trends:
- What platforms are used.
- How often.
- By which audience groups.
Qualitative analysis explains why:
- What people trust.
- Where they feel confident.
- Which pushes them to switch between platforms.
People are not loyal to just one research method. They adapt their behavior to the task at hand.
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Someone could turn to AI to summarize options, use Google to validate details, and go to TikTok or Reddit for real-world context, all in the same journey.
Your segmentation should be mapped throughout the customer journey, including where AI plays a role, where people seek reassurance, and where they need human evidence, as the same person may prioritize AI at the start of a journey and avoid AI at the decision point.
If you don’t understand these changes, you risk designing a strategy that will only work part of the way. This is where brands lose relevance.
2. Design several discovery trails
Once you understand your audience’s behavior, the next step is to design a strategy that reflects it.
In our study, 51% of users say they turn to social media for information in the format they prefer, such as images and videos, while 40% appreciate information from real people. This tells us how people want to perceive information: through visual and understandable formats, with human perspectives and real-world context.
AI is the tool for answers, while social remains the place of human context. Platforms like TikTok and Instagram are key parts of the search journey, especially in the early stages of exploration. At the same time, AI is used to summarize and simplify, while traditional search engines are still relied on for validation and details.
It’s important to be there in the moments that matter, with the right content, in the right format and with the right voice.
3. Optimize for Clarity
Users are now more specific, more conversational, and more complex in their searches, especially in AI environments.
That’s why your content should be structured to answer real, nuanced questions, surfacing information that humans and machines can interpret. If your content is unclear, it may not be visible at all.
4. Build trust and efficiency
AI doesn’t change the need for reassurance, because even when people use it to quickly narrow down options, they’re still looking for signals that help them feel confident in a decision, including reviews, authority, real-world validation, and brand credibility.
We’re already seeing this in AI-generated summaries of reviews and recommendations. Efficiency could get you shortlisted. Confidence is what makes you choose.
The future of research is human
AI will evolve and platforms will change, but the determining factor is not the technology, but how people use it.
The future of research will be defined by human behavior. To win, don’t just optimize platforms: understand the people behind them: how they think, research, and decide.
