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How Is AI Disrupting Search, and What Should Businesses Do About It?
The biggest risk with AI may not be making the wrong bet. It may be waiting until the answer is obvious.
Andrew Coleman, LQ’s Founder, has seen this movie before.
More than 20 years ago, when he was building HomeGain, he made roughly 110 search partnership deals while the market was still trying to figure out which search platforms would win. One of those platforms, then known as BackRub, became Google. The tests were so successful, he was even a business reference for a series round for them.
No one knew at first which platforms would win, Andrew certainly didn’t. That uncertainty feels very familiar with what is happening with AI today. We learned then, betting before things are certain is what delivers the big reward.
LQ also learned the same lesson, in a harder way with social. We were late to invest because the attribution was not clear enough.
That experience shapes Andrew’s advice now, which is to test early enough to learn before the market win becomes obvious.
Google is not disappearing tomorrow, but the customer journey is changing. It is becoming less predictable, less click-driven, and more dependent on how AI systems decide what information to surface.
For companies that rely on search, affiliate traffic, or content discovery, that creates both a risk and an opportunity. The question is no longer just, “How do we rank?”
The more important marketing question has always been “Where are our customers doing their research?”
If more of that research is happening in AI search and LLMs, the next question to ask is “How do we show up there?”
In this coffee chat, Monica Niblack and Andrew discuss why the rise of AI search feels familiar, what is different this time, and why businesses should test before the market becomes easy to understand.
Is AI Search Replacing Google?
No, Google still matters. But its role in the customer journey is changing. Search results pages are increasingly filled with generated answers, summaries, shopping experiences, maps, videos, and other features that can answer a question without sending the user to a website.
A customer may discover a brand through an AI-generated answer, visit the site later through a direct search, and convert days after that. The original source may be difficult to identify. The impact is real, but the path is less obvious. This makes attribution harder, but it does not make the influence less real.
What waiting can cost you
A few years later, another digital disruption changed the way consumers discovered products. In the coffee chat, Andrew admits that LQ was late to invest in social because the team could not draw a clean direct line between spend and return.
We had become so reliant on Google clicks that attribution became a sticking point. Waiting for that clear line to appear was still the wrong call.
That lesson matters now. If businesses wait for perfect measurement before testing AI search, they may understand the channel only after competitors have already capitalized on it.
What should businesses do first?
Start with the customer.
Not the platform. Not the latest AI announcement. The customer.
Ask:
- Where do people in our market go when they’re beginning their research?
- What questions do they ask before they ever talk to sales?
- Which platforms are influencing their decisions?
- What sources does AI use when it forms an answer?
- Are we currently visible in those sources?
- How much of our current business depends on traditional search or search-driven partners?
Those questions will look different by industry.
A mortgage company, for example, may need to understand how people research rates, lenders, and loan options across Google and AI platforms. A software company may need to know which review sites, comparison pages, and customer discussions appear in AI-generated recommendations. A healthcare company may need to understand which sources AI systems treat as credible when answering sensitive questions.
There is no AI search playbook because there is no universal customer journey.
Should every company invest in AI search now?
Not necessarily. AI search is changing quickly. The major platforms are still testing formats, advertising models, citations, and ways to keep users engaged. A company that decides to build an entire growth strategy around one platform may find itself rewriting the plan six months later.
That doesn’t mean companies should ignore the channel. It means they need to be deliberate about the level of investment. The mistake is assuming this can be tested once with hard revenue values then checked off the list.
If it matters to your customers, it needs an owner.
How do you measure a channel when clicks disappear?
This is where leadership gets uncomfortable. Marketing teams have spent years training businesses to expect a direct line between an investment and a reported return. This doesn’t mean every vague brand activity deserves a budget. It means companies need a more thoughtful testing framework: Look for changes in direct traffic, branded search, qualified demand, assisted conversions, customer feedback, and market visibility. Compare results over time. Test specific hypotheses. Keep asking what the data is telling you. The goal is not to manufacture certainty but to gather enough signal to keep making better decisions.
The advice is simple, even if the execution isn’t
Be curious. Get comfortable with uncertainty. And keep testing what matters to your customer.
Read the full coffee chat transcript here
Watch the coffee chat here
