Everyone talks about AI-driven research as if it’s already universal, as if we’ve collectively moved on, users have changed, and discovery has changed for everyone. But the reality is much less simple.
While AI-based research is growing rapidly, it is not adopted uniformly. The gap is increasingly influenced by something we don’t often discuss in research: household income.
My agency has been tracking how people search since the beginning of 2025. When looking at household income, we found a clear and significant gap. Overall, about 27% of people say they use ChatGPT regularly. But if you analyze income by income, the picture changes radically.
- Households from £25-30,000: approximately 18% usage
- Households from £50-60,000: approximately 30% utilization (average UK household income falls into this range based on the tax year ending 2024)
- £70-80,000 households: ~49%
- £100,000+ households: ~48–58%
In other words, higher-income families are more than twice as likely to use generative AI tools. This is no small variation. It calls into question one of the most important assumptions shaping research strategy: that the adoption of AI is happening at the same pace for everyone.
We are seeing the emergence of a new type of digital inequality in the way people access information and make decisions. This gap does not exist in isolation.
Across the UK, FutureDotNow has found 52% of working age adults you cannot complete all essential digital tasks required for the job. The adoption of AI is overlapping an existing digital skills gap, which already shapes who can confidently access, evaluate and act on information.
As the writer William Gibson said: “The future is already here – it’s just not distributed equally.”
Adopting AI isn’t just about access to tools. It is shaped by human behavior, in particular:
- Access.
- Capacity.
- Trust.
Access: Who is exposed to AI in everyday life?
If you work in a digital, business or knowledge-based role, you are much more likely to be encouraged or expected to use AI. It becomes part of your workflow. This is reflected in our data, where sectors like IT and business consistently drive adoption, reinforcing how workplace exposure accelerates behavior.
Otherwise, your exposure may be limited to headlines, media narratives, 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, suggestions become second nature. You will learn how to refine, challenge and develop results. For others, that first interaction may seem unusual, 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 just by platform, but by mindset. In our research, platforms like Perplexity score high on trust, but are still relatively niche.
This raises an important question: Are early adopters of these tools also the most confident in exploring and validating AI results?
It’s probable. This reinforces a larger point: AI adoption isn’t just a technology curve, it’s a human curve.
As AI becomes an integral part of how people research and decide, AI literacy 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 and this has real commercial consequences
Different audiences are building different behaviors:
- AI-first users → Task delegation, summary, selection.
- AI-assisted users → Cross-platform validation.
- Users who avoid AI → Rely on Google, resellers and communities.
These behaviors are not fixed. The same person could use AI to draft a legal letter, but still turn to Google when searching for a product.
Habits take time to form, and right now people are experimenting. This means that we are not simply moving from one research path to another; rather, we are seeing the fragmentation of discovery into several distinct paths.
This fragmentation is not just a behavioral change. It has direct commercial consequences. If you assume your audience will behave like early adopters, you risk making poor strategic choices.
Overinvesting in AI optimization can mean losing traditional users, while over-indexing on Google can mean losing AI-driven users. Ignoring trust gaps can also erode trust.
The opportunity: Your most valuable audience may already be AI first
There is real upside to this gap. The audience that adopts AI faster is often valued by many brands: decision makers, professionals and higher-income consumers.
Our data shows that these users often align with what we call “digital explorers,” early adopters who are already delegating some of their decision making to AI by comparing options, summarizing information, and listing results 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 mapping behavior through this lens, three clear patterns emerge:
- High trust users → Able to delegate to AI.
- Users with medium trust → Likely to cross-check across all platforms.
- Unsafe users → Rely on familiar environments.
Different behaviors, paths, expectations and, above all, content needs.
How to respond to fragmented research
As these high-value, AI-conscious users delegate decisions upfront, the goal now is to be understood, presented to, and recommended by AI tools, before a click even occurs.
1. Segment based on behavior, not just demographics
Age or income might explain who your audience is, but not how they decide. To achieve this, you need to go beyond surface-level segmentation and build a behavioral understanding of discovery by combining quantitative and qualitative information.
Quantitative data shows large-scale patterns:
- Which platforms are used.
- How often.
- From which audience groups.
Qualitative analysis explains why:
- What people trust.
- Where they feel safe.
- What drives them to switch from one platform to another.
People aren’t loyal to a single search method. They are adapting their behavior to the task at hand.
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Someone could use AI to summarize options, use Google to validate specifics, and go to TikTok or Reddit for real-world context, all within the same journey.
Segmentation needs to be mapped across the entire customer journey, including where AI plays a role, where people seek reassurance and where they need human evidence, as the same person can be AI-first at the start of a journey and avoid AI at the point of decision.
If you don’t understand these changes, you risk designing a strategy that only works for part of the journey. This is where brands lose relevance.
2. Design for multiple journeys of discovery
Once you understand how your audience behaves, the next step is to design a strategy that reflects this.
In our research, 51% of users say they turn to social media for information in their preferred format, such as images and videos, while 40% value information from real people. This tells us how people want to experience information: through visual and digestible formats, with human perspectives and real-world context.
Artificial intelligence is the tool for answers, while the social remains the place of the human context. Platforms like TikTok and Instagram are key parts of the search journey, particularly 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 show up in the moments that matter, with the right content, in the right format and in the right voice.
3. Optimize for clarity
Users are now more specific, conversational and complex in what they search for, particularly in AI environments.
This is why your content needs to be structured to answer real, nuanced questions, surfacing information that humans and machines can interpret. If your content isn’t clear, it may not appear at all.
4. Build trust along with efficiency
AI doesn’t change the need for reassurance, because even when people use it to quickly narrow down options, they still look for signals that help them feel confident in a decision, including reviews, authority, real-world validation, and brand credibility.
We’re already seeing this reflected in AI-generated review and recommendation summaries. Efficiency could get you on the shortlist. Trust 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 search will be defined by human behavior. To win, don’t just optimize for platforms: understand the people behind them: how they think, search and decide.
