How is researching with AI changing the way we make decisions online?
Researching with AI is transforming search from a process of exploration into one of instant answers. Instead of comparing sources, reviewing perspectives, and building conclusions ourselves, many users now rely on AI-generated summaries to quickly deliver information. This shift is changing how people interact with search, trust content, and evaluate credibility online.
As AI search tools become more integrated into everyday workflows, brands, marketers, and leaders must rethink how they build authority and visibility. This article explores the rise of GEO (Generative Engine Optimization), the growing “User Agency Gap,” and why human judgment, verification, and trust still matter in an AI-driven world.
I caught myself doing something recently that I suspect many of us are doing now, maybe without even realizing it.
I was trying to answer a specific question by comparing two martech tools. It wasn’t a broad “best software” search. It was a decision-level question with trade-offs, implementation details, and enough nuance to warrant a bit of digging. A few years ago, I would have opened multiple tabs, looked over feature documentation, read a few reviews, and built a point of view from there. That was what research looked like.
But this time, I just read the AI-generated summary at the top of the results, glanced at the bullet points, and moved on. I didn’t check any other sources or dig deeper.
That made me stop and think.
I work in research, analytics, and decision-making, so I take evidence seriously. As a trained data scientist, even I find the convenience tempting. The change is so subtle that it seems harmless. When researching with AI, it doesn’t feel like you’re lowering your standards. It just feels efficient.
These days, the internet doesn’t work like a library where we browse shelves. It’s more like a concierge: we ask for something, and a polished answer appears. This feels easier than before, but it also means we’re putting in less effort.
I don’t think we talk about this trade-off enough.
The issue isn’t that AI summaries have no value. The real challenge is that once we get used to quick answers, it’s easy to forget the difference between reviewing information ourselves and simply accepting an answer. Those aren’t the same.
The Structural Shift: From SEO to GEO
For years, search made us act like detectives. The process could be inefficient, but it required discipline. You had to sort through results, notice differences in quality, credibility, and relevance, and take part in building your own answer, even if you were in a hurry.
That was the focus during the SEO era. Visibility mattered because higher rankings brought more clicks and visits. Now, that system is changing.
Now, instead of a list of options, we often get a synthesized response right away. We enter a prompt, the system gathers the information, and we receive a ready-made answer. Most people researching with AI don’t start by checking sources; they just accept what they get.
The data shows that shift. In a 2024 SparkToro study based on Datos clickstream data, 58.5% of Google searches in the U.S. ended without a click to the open web. Search Engine Land’s coverage of that research helped bring the finding into wider view. Similarweb has also reported that zero-click behavior has increased since the rollout of AI Overviews, with news-related queries rising from 56% to 69% between May 2024 and May 2025.
Pew Research adds more insight. In its study of Google search behavior, users who saw an AI summary clicked on a regular search result only 8% of the time, compared to 15% for those who didn’t see an AI summary. People also clicked links in the AI summary only 1% of the time. This doesn’t mean people have stopped searching. It means search is now designed to answer questions before anyone clicks.
That’s why Generative Engine Optimization, or GEO, is becoming the main goal for content marketers.
If SEO were about making content easy to find in a list of links, GEO is about making content easy to understand, trust, and reuse in AI-generated answers. It’s no longer just about ranking.
Now, clarity matters most. Can a generative system grasp what you do, why it matters, and whether your information is reliable?
That’s why the move from SEO to GEO is about more than marketing. It’s about how we behave. Search used to reward people who were willing to dig deeper. Now, AI search rewards sources that are easy for the system to interpret for us.
The User “Agency Gap”: Why We’re Letting Go
People are changing how they behave because today’s web is overwhelming. Users face ads, sponsored content, affiliate pages, repetitive articles, gated reports, and content designed more for ranking than usefulness. Even when information is available, it’s often unclear. A simple question can mean sorting through many versions of the same answer, each shaped by different motives.
When researching with AI, people are looking for speed and simplicity. Synthesized answers eliminate the need to sort, compare, and evaluate sources, creating the sense that the initial work has already been done.
This creates a trust paradox.
A list of sources should give us confidence by offering choices, but a concise summary often feels more trustworthy because it reduces uncertainty. It organizes information and gives us a clear perspective, saving us the effort of putting it together ourselves.
But unfortunately, those answers aren’t neutral. This leads to the “User Agency Gap.” It happens when we give up the work of discovery and, without realizing it, also give up some of the thinking. The question isn’t whether AI can help. We know it can. The real question is whether we stay involved enough to know when an answer should be challenged.
AI might seem objective because it doesn’t have a personal agenda, but it’s built on training data, retrieval patterns, ranking signals, and design choices. It doesn’t think as people do. It predicts, combines, and presents information to make things look coherent based on the data it’s been trained with.
What This Means for Brands and Leaders
This shift changes how brands are seen in the market. For years, brands focused on being found through their own content, including website copy, blogs, keywords, landing pages, and metadata. These still matter, but they’re no longer the main goal.
When AI becomes part of the discovery process, authority becomes more distributed.
A brand’s website is only one input. Reviews, interviews, third-party mentions, guest articles, podcasts, conference bios, media coverage, research citations, and credible partnerships all become increasingly important. These sources demonstrate whether a brand or leader is consistently understood beyond their own platforms.
This is the authority premium. If AI systems seek trusted, corroborated information, reputation must go beyond what you control and be consistent in those contexts. This requires a new level of brand discipline.
It means leaders need to ask better questions about how visible they really are.
Not just “Are we publishing enough?” but “Are we being described accurately?”
Not just “Are we ranking?” but “Are we credible enough to be included in the answer?”
Not just “Do we have content?” but “Does our content make a clear conclusion easier?”
A lot of content is accurate without being useful. It explains topics without helping readers apply the information, adding volume without clarity. In an AI-driven environment, this is a weakness.
To be included in AI-generated answers, content needs to make clear claims and offer practical value. It should define the issue, explain why it matters, and make conclusions easy to understand. Brands and marketers need to move from just sharing facts to showing good judgment.
What do you believe?
What pattern are you seeing?
What trade-off should people understand?
What mistakes are others making?
What decision does this information support?
These questions help make content more conclusive and give brands and leaders a real opportunity. As AI-generated answers condense information, vague claims will be harder to defend, but clear authority will stand out.
The Ethical Handshake: Keeping the Human in the Loop
Every major improvement brings new responsibilities. Researching with AI helps us find information faster, but it can also make us think less as we go. That matters because research is valuable not just for the answers, but for the process itself.
When researchers review competing sources, compare arguments, spot contradictions, and work through uncertainty, they do more than just gather facts. They build judgment. They learn to notice weak logic, missing context, and exaggerated claims. Over time, this discipline shapes their intuition.
If we let tools handle too much of this work, we risk losing important skills without noticing.
People often call skillful intuition “instinct,” but it’s really pattern recognition built on experience with complex situations. It’s knowing when something seems wrong, when advice sounds good on the surface, or when an answer misses important side effects. These skills don’t vanish right away, but they do fade if we stop using them.
That’s why I think every organization should have a verification gate. This means treating AI output as a starting point, not the final answer. Use it to get oriented quickly, but don’t let it replace your own judgment. Let AI organize the basics and show possibilities, but always review things yourself before making important decisions.
In practice, that may mean asking a few disciplined questions:
- What source or evidence would change this answer?
- What assumptions are embedded in this summary?
- What perspective is absent?
- What risk grows if we act on this too quickly?
- Is this recommendation consistent with our values, constraints, and goals?
These questions might slow things down a bit, but they make the process much better.
This matters most when the stakes are high. Decisions about hiring, budgets, customers, markets, reputation, and people should never be made just because the answer sounds good. A confident answer isn’t proof. A neat summary isn’t the same as being accountable.
As researching with AI gets better at mixing public information, real originality comes from what only people can offer. That includes lived experience, hard-earned perspective, and memories from within organizations. Personal judgment grows from facing real outcomes. Stories from real situations, lessons from mistakes, and the subtle details between categories all matter.
These things are hard for AI to copy because they aren’t general facts that can be scraped from the rest of the internet. They come from individual experiences.
This makes personal insight even more valuable. An executive who has managed a crisis, an operator who has seen how incentives change behavior, a founder who understands why customers hesitate, or a manager who knows what good morale looks like; all of these types of knowledge are important.
Organizations that use AI well won’t remove the human layer. They’ll strengthen it. They’ll let systems handle scale, while people provide judgment, accountability, and meaning. That’s the ethical handshake: machine efficiency working with human responsibility.
The New Definition of Discovery
For most of the last decade, discovery meant searching. You had a question, opened your browser, checked different options, and worked toward an answer. “Looking it up” was a habit built on exploring links and pages, and it depended on your willingness to sort through them.
By 2026, this behavior is changing. When researching with AI, people start with a prompt, get a summary, and move on from there. In many cases, they’re not just gathering information; they’re letting a system narrow choices, shape the issue, and guide their path.
We’ve moved from searching for answers to letting systems help create them. This brings new responsibilities for users, leaders, and brands.
For users, the responsibility is to stay attentive enough to know when convenience needs verification. Researching with AI isn’t the end-all, be-all.
For leaders, the responsibility is to preserve judgment even as workflows become more automated.
For brands, the responsibility is to become understandable, credible, and consistent enough to be represented accurately in synthesized environments.
In the past, the goal was to appear on a list. Now, the goal is to be the trusted source that gets included even when there’s no list. It means being the company, expert, or idea that an AI system confidently suggests when someone needs guidance.
That’s the new definition of discovery. In many ways, it brings us back to an old truth: when information is everywhere, trust is what matters most.
