The Shift From Search Sessions To Decision Sessions via @sejournal, @DuaneForrester

This one started with a question from Adorján-Csaba Demeter, a subscriber in Romania, who asked how big the behavior change could be after Google’s AI Mode Personal Search launch, and it pushed me to think past the product announcement and into the habit shift underneath it.

AI changing search is a foregone conclusion. The real story is what happens to people when search stops acting like a library and starts acting like a helper that knows what you meant, what you like, and what you have coming up next.

When effort drops, behavior changes first. Then business models change. Then the web scrambles to catch up.

What Google Actually Changed

Google did not just add another AI layer to results. It moved AI Mode from “answer from the web” toward “answer from the web plus your life,” starting with opt-in connections to Gmail and Google Photos for AI Pro and AI Ultra subscribers in the U.S., delivered as a Labs experiment.

That detail matters because it tells you what Google thinks the next battleground is.

Not faster answers, but stickier habits.

When the system can read your hotel confirmation in Gmail, it can plan. When it can see the kinds of trips you take in Photos, it can recommend. You stop doing the work of explaining context. You start delegating outcomes.

That is a bet on human behavior.

The three behavior shifts that will most likely follow, in order, are:

1. People ask more questions, and they ask harder questions.

Google already sees this pattern with AI Overviews. In major markets like the U.S. and India, Google says AI Overviews drive over a 10% increase in usage for the types of queries that show them. That is a habit signal, not a satisfaction claim.

When people believe the system will do more for them, they return more often, and they push further. Queries get longer. They get more specific. They get more outcome-oriented. People stop asking “what is” and start asking “what should I do.”

Personal context amplifies that shift. If the system already knows your reservations, your preferences, and your recent activity, the user has less friction and more confidence. That increases question volume.

2. Sessions end sooner, and fewer decisions happen on websites.

Here’s the part businesses need to internalize. AI does not just reduce clicks. It compresses the journey and ends sessions earlier.

Pew’s browsing-panel study found that when an AI summary appeared, users clicked a traditional search result in 8% of visits versus 15% when there was no AI summary. Pew also found users were more likely to end their browsing session after a page with an AI summary, 26% versus 16% without.

3. People shift from browsing to delegating.

This is where behavior becomes durable. Traditional search trained people to open tabs, compare sources, build their own plan, then act. AI Mode personalizes the plan inside search itself. It turns “find me information” into “help me decide.” If the system can use your life context, it can do the assembly work you used to do manually.

That is the transition from search sessions to decision sessions. A search session ends when you find information. A decision session ends when you have a recommended next step and you are ready to act.

Adoption Will Be Real, And Uneven, For A Simple Reason

People like convenience, but they do not always like the feeling of being summarized.

Pew found that among Americans who have seen AI summaries in search results, only one in five say they find them extremely or very useful. Most say somewhat useful, and 28% say not too or not at all useful.

Low-stakes categories will move fastest because the cost of being wrong is low. High-stakes categories will move slower because trust and liability show up quickly, even when the convenience is obvious.

Even with mixed sentiment, usage is already going mainstream. Deloitte’s 2025 Connected Consumer survey found 53% of surveyed consumers are either experimenting with gen AI or using it regularly, up from 38% in 2024.

The behavior change is already underway, and I think Google is trying to capture it inside its existing habit loop.

What This Does To Businesses, Even If Your SEO Is Perfect

This is where most teams get stuck. They see AI Mode and AI summaries and assume it is “just another ranking change.” It is not. It is a consumer behavior change that reshapes the economics of discovery. The shift is subtle at first, then it hits you all at once, because it changes what people consider a completed search experience.

When sessions complete in the answer layer, classic top-of-funnel traffic becomes less reliable, even if your rankings hold. The competitive line shifts to inclusion: being referenced, cited, recommended, or selected as the next step inside the plan the system generates.

To win there, build for next-step intent. Most marketing content assumes the user will land on your site and then decide. AI compresses that journey, so your content has to carry options, tradeoffs, and a clear “what to do next,” in a form that survives summarization.

Vertical Impacts, Where Behavior Shifts First

Healthcare

People already use search as a first stop for health. The Annenberg Public Policy Center found that most (79%) U.S. adults say they’re likely to look online for the answer to a question about a health symptom or condition.

And the way they search is predictable. A 2025 JMIR survey study found participants most often sought information on health conditions, 90.2%, and medication info came next, 60.3%.

As the answer layer feels more confident, people will use it for triage and next steps. It will influence which clinic they choose and how quickly they escalate a concern.

Healthcare businesses should expect:

1. Less website traffic for broad informational topics, and more pressure on “what do I do next” moments.
2. Increased competition to be the cited and trusted source inside AI answers.
3. Higher stakes for accuracy and clarity, because summarization can remove nuance.

There is also a revealing warning signal here. A study of health-related AI Overviews citations, found YouTube was the single most cited source, accounting for 4.43% of citations in that dataset.

That is not an argument against AI. It is a reminder that citation sources do not automatically align with medical rigor. Businesses in healthcare need to make their evidence, authorship, and care pathways machine-readable and unambiguous.

Financial Services

Finance is already living in an “assistant” world, and that matters because it shows how quickly consumers accept delegated help when it saves effort.

Bank of America reports that Erica (their consumer AI assistant) has surpassed 3.2 billion client interactions since its 2018 launch, and clients now interact with Erica more than 2 million times per day.

That is behavior change at scale.

Meanwhile, consumers are increasingly willing to use AI for financial advice and information. ABA Banking Journal reported in September 2025 that 51% of respondents said they turn to AI to get financial advice or information, and another 27% said they are considering it.

Now when we connect the dots…

If AI Mode personalizes search around a user’s life context, financial decision-making gets pulled earlier into the assistant layer. Budgeting questions, product comparisons, “should I refinance,” “how much house can I afford,” and “what happens if I miss a payment” all become conversational.

Financial services businesses should expect:

1. Increased competition for being the recommended next step, not just being discoverable.
2. More pressure to publish clear, plain-language product explanations that survive summarization.
3. A sharper separation between low-stakes guidance and regulated advice, with trust and compliance becoming part of how content gets used.

Retail And Ecommerce

Retail gets hit hard because the classic behavior pattern is tab sprawl, and AI collapses it into a shortlist.

Retail businesses should expect:

1. Fewer browsing sessions that start with generic research and end on a product page.
2. More “shortlist behavior,” where the system presents a handful of options and the user picks.
3. Higher importance for product data that can be summarized cleanly, including dimensions, compatibility, return policies, and warranty terms.

If your differentiation lives in fluffy copy, it dies in the summary. If it lives in measurable attributes, verified reviews, and clear tradeoffs, it survives.

Local Services

Local services are where this gets practical fast. People search when something broke, they need help now, and they do not want homework.

AI Mode personal context will steer choices based on urgency, location, constraints, and preferences. That means “best next step” routing becomes default behavior.

Local businesses should expect:

1. Less opportunity to win by content volume alone.
2. More emphasis on entity clarity, service area accuracy, availability, pricing ranges, and proof of credibility.
3. A rise in “invisible funnel” decisions, where the customer shows up ready to book because the plan already happened elsewhere.

What You Can Do Today, Without Waiting For The Dust To Settle

For Consumers

1. Decide where you want personalization, and where you do not. Personal AI is a trade. You get convenience, but you give context. Make that choice deliberately.

2. Use AI for options, then verify what has consequences. Health, money, legal, and safety decisions deserve a second look. If an answer influences a purchase, a medical step, or a contract, capture the source and key details so convenience does not erase accountability.

For Businesses

1. Stop treating clicks as the only signal that matters. Clicks will drop in many query classes, and sessions will end sooner. Measure presence in answers, citations, recommendations, and downstream conversions that happen after exposure.

2. Rebuild your content around next-step intent. Take your highest value pages and rewrite them for decision completion. Clear options. Clear tradeoffs. Clear “what to do next.”

3. Make your entity impossible to misunderstand. Clean organization signals, consistent naming, authoritative profiles, accurate locations, and structured data where relevant. When the machine layer tries to explain who you are, make it easy.

4. Publish proof, not fluff. In high-stakes verticals, show your sources, your credentials, your policies, and your constraints. AI can compress text, but it still needs real signals to anchor trust.

The Competitive Forecast, Google Versus The Rest

If AI Mode personal search takes off, the winners will not be determined by model quality alone. Distribution and habit will do most of the work.

Scenario one, Google accelerates

Google’s biggest advantage is not that it can build an assistant. It is that it can place the assistant inside a habit billions of people already have. (Android + Siri) It already sees increased usage when AI Overviews appear, over 10% in major markets for those query types.

If Google can move Personal Intelligence from paid opt-in into broader availability, and expand the connected sources beyond Gmail and Photos, it can turn search into a daily operating layer for planning and decisions. That is a habit engine.

Scenario two, the market stays plural

ChatGPT and other assistants will continue to grow because they do not live only in “search.” They live in work, writing, learning, and deep tasks. Many users will keep separate habits, one for web discovery, another for assistant workflows, at least for a while.

In a plural market, businesses must optimize for multiple answer layers, not just Google.

What To Watch In 2026

1. Whether Google keeps Personal Intelligence as a paid feature or uses it as a default habit builder.
2. Whether connected context expands, and which sources get added next.
3. Whether user sentiment shifts from lukewarm to reliant or stays mixed as Pew found.
4. How quickly session compression shows up by vertical, since that will reveal where business disruption hits first.

The Takeaway

The change to watch is not that AI can answer questions. That part is already here, and it will keep improving. The real change is that people will stop doing the assembly work they have always done in search. They will ask more, browse less, and increasingly accept plans that arrive pre-built, because it feels faster and it feels complete. Habits will change.

When that happens, power moves upward into the answer layer. Competition shifts from who ranks to who gets included, because inclusion is what influences the decision before a user ever lands on your site. The web does not disappear, but its role changes. It becomes the dependency that feeds answers, not the destination where discovery naturally occurs.

If you run a business, you cannot pause this shift. You can adapt. Build for decision completion. Make your proof easy to carry forward so it survives summarization and still earns trust. Measure what matters when the click often disappears.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Collagery/Shutterstock

Information Retrieval Part 2: How To Get Into Model Training Data

There has never been a more important time in your career to spend time learning and understanding. Not because AI search differs drastically from traditional search. But because everyone else thinks it does.

Every C-suite in the country is desperate to get this right. Decision-makers need to feel confident that you and I are the right people to lead us into the new frontier.

We need to learn the fundamentals of information retrieval. Even if your business shouldn’t be doing anything differently.

Here, that starts with understanding the basics of model training data. What is it, how does it work and – crucially – how do I get in it.

TL;DR

1. AI is the product of its training data. The quality (and quantity) the model trains on is key to its success.
2. The web-sourced AI data commons is rapidly becoming more restricted. This will skew data representativity, freshness, and scaling laws.
3. The more consistent, accurate brand mentions you have that appear in training data, the less ambiguous you are.
4. Quality SEO, with better product and traditional marketing, will improve your appearance in the training and data, and eventually with real-time RAG/retrieval.

What Is Training Data?

Training data is the foundational dataset used in training LLMs to predict the most appropriate next word, sentence, and answer. The data can be labeled, where models are taught the right answer, or unlabeled, where they have to figure it out for themselves.

Without high-quality training data, models are completely useless.

From semi-libelous tweets to videos of cats and great works of art and literature that stand the test of time, nothing is off limits. Nothing. It’s not just words either. Speech-to-text models need to be trained to respond to different speech patterns and accents. Emotions even.

How Does It Work?

The models don’t memorize, they compress. LLMs process billions of data points, adjusting internal weights through a mechanism known as backpropagation.

If the next word predicted in a string of training examples is correct, it moves on. If not, it gets the machine equivalent of Pavlovian conditioning.

Bopped on the head with a stick or a “good boy.”

The model is then able to vectorize. Creating a map of associations by term, phrase, and sentence.

• Converting text into numerical vectors, aka Bag of Words.
• Capturing semantic meaning of words and sentences, preserving wider context and meaning (word and sentence embeddings).

Rules and nuances are encoded as a set of semantic relationships; this is known as parametric memory. “Knowledge” baked directly into the architecture. The more refined a model’s knowledge on a topic, the less it has to use a form of grounding to verify its twaddle.

Worth noting that models with a high parametric memory are faster at retrieving accurate information (if available), but have a static knowledge base and literally forget things.

RAG and live web search is an example of a model using non-parametric memory. Infinite scale, but slower. Much better for news and when results require grounding.

Crafting Better Quality Algorithms

When it comes to the training data, drafting better quality algorithms relies on three elements:

1. Quality.
2. Quantity.
3. Removal of bias.

Quality of data matters for obvious reasons. If you train a model on poorly labeled, solely synthetic data, the model performance cannot be expected to exactly mirror real problems or complexities.

Quantity of data is a problem, too. Mainly because these companies have eaten everything in sight and done a runner on the bill.

Leveraging synthetic data to solve issues of scale isn’t necessarily a problem. The days of accessing high-quality, free-to-air content on the internet for these guys are largely gone. For two main reasons:

1. Unless you want diabolical racism, mean comments, conspiracy theories, and plagiarized BS, I’m not sure the internet is your guy anymore.
2. If they respect company’s robots.txt directives at least. Eight in 10 of the world’s biggest news websites now block AI training bots. I don’t know how effective their CDN-level blocking is, but this makes quality training data harder to come by.

Bias and diversity (or lack of it) is a huge problem too. People have their own inherent biases. Even the ones building these models.

Shocking I know…

If models are fed data unfairly weighted towards certain characteristics or brands, it can reinforce societal issues. It can further discrimination.

Remember, LLMs are neither intelligent nor databases of facts. They analyze patterns from ingested data. Billions or trillions of numerical weights that determine the next word (token) following another in any given context.

How Is Training Data Collected?

Like every good SEO, it depends.

1. If you built an AI model explicitly to identify pictures of dogs, you need pictures of dogs in every conceivable position. Every type of dog. Every emotion the pooch shows. You need to create or procure a dataset of millions, maybe billions, of canine images.
2. Then it must be cleaned. Think of it as structuring data into a consistent format. In said dog scenario, maybe a feline friend nefariously added pictures of cats dressed up as dogs to mess you around. Those must be identified.
3. Then labeled (for supervised learning). Data labeling (with some human annotation) ensures we have a sentient being somewhere in the loop. Hopefully, an expert to add relevant labels to a tiny portion data, so that a model can learn. For example, a dachshund sitting on a box looking melancholic.
4. Pre-processing. Responding to issues like cats masquerading as dogs. Ensuring you minimize potential biases in the dataset like specific dog breeds being mentioned far more frequently than others.
5. Partitioned. A portion of the data is kept back so the model can’t memorise the outputs. This is the final validation stage. Kind of like a placebo.

This is, obviously, expensive and time-consuming. It’s not feasible to take up hundreds of thousands of hours of expertise from real people in fields that matter.

Think of this. You’ve just broken your arm, and you’re waiting in the ER for six hours. You finally get seen, only to be told you had to wait because all the doctors have been processing images for OpenAI’s new model.

Data labeling is a time-consuming and tedious process. To combat this, many businesses hire large teams of human data annotators (aka humans in the loop, you know, actual experts), assisted by automated weak labeling models. In supervised learning, they sort the initial labeling.

For perspective, one hour of video data can take humans up to 800 hours to annotate.

Micro Models

So, companies build micro-models. Models that don’t require as much training or data to run. The humans in the loop (I’m sure they have names) can start training micro-models after annotating a few examples.

The models learn. They train themselves.

So over time, human input decreases, and we’re only needed to validate the outputs. And to make sure the models aren’t trying to undress children, celebrities, and your coworkers on the internet.

But who cares about that in the face of “progress.”

Types Of Training Data

Training data is usually categorized by how much guidance is provided or required (supervision) and the role it plays in the model’s lifecycle (function).

Ideally a model is largely trained on real data.

Once a model is ready, it can be trained and fine-tuned on synthetic data. But synthetic data alone is unlikely to create high-quality models.

• Supervised (or labeled): Where every input is annotated with the “right” answer.
• Unsupervised (or unlabeled): Work it out yourself, robots, I’m off for a beer.
• Semi-supervised: where a small amount of the data is properly labeled and model “understands” the rules. More, I’ll have a beer in the office.
• RLHF (Reinforcement Learning from Human Feedback): humans are shown two options and asked to pick the “right” one (preference data). Or a person demonstrates the task at hand for the mode to imitate (demonstration data).
• Pre-training and fine-tuning data: Massive datasets allow for broad information acquisition, and fine-tuning is used to turn the model into a category expert.
• Multi-modal: Images, videos, text, etc.

Then some what’s known as edge case data. Data designed to “trick” the model to make it more robust.

In light of the let’s call it “burgeoning” market for AI training data, there are obvious issues of “fair use” surrounding it.

“We find that 23% of supervised training datasets are published under research or non-commercial licenses.”

So pay people.

The Spectrum Of Supervision

In supervised learning, the AI algorithm is given labeled data. These labels define the outputs and are fundamental to the algorithm being able to improve over time on its own.

Let’s say you’re training a model to identify colors. There are dozens of shades of each color. Hundreds even. So while this is an easy example, it requires accurate labeling. The problem with accurate labeling is its time-consuming and potentially costly.

In unsupervised learning, the AI model is given unlabeled data. You chuck millions of rows, images, or videos at a machine, sit down for a coffee, and then kick it when it hasn’t worked out what to do.

It allows for more exploratory “pattern recognition.” Not learning.

While this approach has obvious drawbacks, it’s incredibly useful at identifying patterns a human might miss. The model can essentially define its own labels and pathway.

Models can and do train themselves, and they will find things a human never could. They’ll also miss things. It’s like a driverless car. Driverless cars may have fewer accidents than when a human is in the loop. But when they do, we find it far more unpalatable.

It’s the technology that scares us. And rightly so.

Combatting Bias

Bias in training data is very real and potentially very damaging. There are three phases:

1. Origin bias.
2. Development bias.
3. Deployment bias.

Origin bias references the validity and fairness of the dataset. Is the data all-encompassing? Is there any obvious systemic, implicit, or confirmation bias present?

Development bias includes the features or tenets of the data the model is being trained on. Does algorithmic bias occur because of the training data?

Then we have deployment bias. Where the evaluation and processing of the data leads to flawed outputs and automated/feedback loop bias.

You can really see why we need a human in the loop. And why AI models training on synthetic or inappropriately chosen data would be a disaster.

In healthcare, data collection activities influenced by human bias can lead to the training of algorithms that replicate historical inequalities. Yikes.

Leading to a pretty bleak cycle of reinforcement.

The Most Frequently Used Training Data Sources

Training data sources are wide-ranging in both quality and structure. You’ve got the open web, which is obviously a bit mental. X, if you want to train something to be racist. Reddit, if you’re looking for the Incel Bot 5000.

Or highly structured academic and literary repositories if you want to build something, you know, good … Obviously then you have to pay something.

Common Crawl

Common Crawl is a public web repository, a free, open-source storehouse of historical and current web crawl data available to pretty much anyone on the internet.

The full Common Crawl Web Graph currently contains around 607 million domain records across all datasets, with each monthly release covering 94 to 163 million domains.

In the Mozilla Foundation’s 2024 report, Training Data for the Price of a Sandwich, 64% of the 47 LLMs analysed used at least one filtered version of Common Crawl data.

If you aren’t in the training data, you’re very unlikely to be cited and referenced. The Common Crawl Index Server lets you search any URL pattern against their crawl archives and Metehan’s Web Graph helps you see how “centered you are.”

Wikipedia (And Wikidata)

The default English Wikipedia dataset contains 19.88 GB of complete articles that help with language modeling tasks. And Wikidata is an enormous, incredibly comprehensive knowledge graph. Immensely structured data.

While representing only a small percentage of the total tokens, Wikipedia is perhaps the most influential source for entity resolution and factual consensus. It is one of the most factually accurate, up-to-date, and well-structured repositories of content in existence.

Some of the biggest guys have just signed deals with Wikipedia.

Publishers

OpenAI, Gemini, etc., have multi-million dollar licensing deals with a number of publishers.

The list goes on, but only for a bit … and not recently. I’ve heard things have clammed shut. Which, given the state of their finances, may not be surprising.

Media & Libraries

This is mainly for multi-modal content training. Shutterstock (images/video), Getty Images have one with Perplexity, and Disney (a 2026 partner for the Sora video platform) provides the visual grounding for multi-modal models.

As part of this three-year licensing agreement with Disney, Sora will be able to generate short, user-prompted social videos based on Disney characters.

As part of the agreement, Disney will make a $1 billion equity investment in OpenAI, and receive warrants to purchase additional equity.

Books

BookCorpus turned scraped data of 11,000 unpublished books into a 985 million-word dataset.

We cannot write books fast enough for models to continually learn on. It’s part of the soon to happen model collapse.

Code Repositories

Coding has become one of the most influential and valuable features of LLMs. Specific LLMs like Cursor or Claude Code are incredible. GitHub and Stack Overflow data have built these models.

They’ve built the vibe-engineering revolution.

Public Web Data

Diverse (but relevant) web data results in faster convergence during training, which in turn reduces computational requirements. It’s dynamic. Ever-changing. But, unfortunately, a bit nuts and messy.

But, if you need vast swathes of data, maybe in real-time, then public web data is the way forward. Ditto for real opinions and reviews of products and services. Public web data, review platforms, UGC, and social media sites are great.

Why Models Aren’t Getting (Much) Better

While there’s no shortage of data in the world, most of it is unlabeled and, thus, can’t actually be used in supervised machine learning models. Every incorrect label has a negative impact on a model’s performance.

According to most, we’re only a few years away from running out of quality data. Inevitably, this will lead to a time when those genAI tools start consuming their own garbage.

This is a known problem that will cause model collapse.

• They are being blocked by companies that do not want their data used pro bono to train the models.
• Robots.txt protocols (a directive, not something directly enforceable), CDN-level blocking, and terms of service pages have been updated to tell these guys to get lost.
• They consume data quicker than we can produce it.

Frankly, as more publishers and websites are forced into paywalling (a smart business decision), the quality of these models only gets worse.

So, How Do You Get In The Training Data?

There are two obvious approaches I think of.

1. To identify the seed data sets of models that matter and find ways into them.
2. To forgo the specifics and just do great SEO and wider marketing. Make a tangible impact in your industry.

I can see pros and cons to both. Finding ways into specific models is probably highly unnecessary for most brands. To me this smells more like grey hat SEO. Most brands will be better off just doing some really good marketing and getting shared, cited and you know, talked about.

These models are not trained on directly up-to-date data. This is important because you cannot retroactively get into a specific model’s training data. You have to plan ahead.

If you’re an individual, you should be:

• Creating and sharing content.
• Going on podcasts.
• Attending industry events.
• Sharing other people’s content.
• Doing webinars.
• Getting yourself in front of relevant publishers, publications, and people.

There are some pretty obvious sources of highly structured data that models have paid for in recent times. I know, they’ve actually paid for it. I don’t know what the guys at Reddit and Wikipedia had to do to get money from these guys, and maybe I don’t want to.

How Can I Tell What Datasets Models Use?

Everyone has become a lot more closed off with what they do and don’t use for training data. I suspect this is both legally and financially motivated. So, you’ll need to do some digging.

And there are some massive “open source” datasets I suspect they all use:

• Common Crawl.
• Wikipedia.
• Wikidata.
• Coding repositories.

Fortunately, most deals are public, and it’s safe to assume that models use data from these platforms.

Google has a partnership with Reddit and access to an insane amount of transcripts from YouTube. They almost certainly have more valuable, well-structured data at their fingertips than any other company.

Grok trained almost exclusively on real-time data from X. Hence why it acts like a pre-pubescent school shooter and undresses everyone.

Worth noting that AI companies use third party vendors. Factories where data is scraped, cleaned and structured to create supervised datasets. Scale AI is the data engine that the big players use. Bright Data specialise in web data collection.

A Checklist

OK, so we’re trying to feature in parametric memory. To appear in the LLMs training data so the model recognizes you and you’re more likely to be used for RAG/retrieval. That means we need to:

1. Manage the multi-bot ecosystem of training, indexing, and browsing.
2. Entity optimization. Well-structured, well-connected content, consistent NAPs, sameAs schema properties, and Knowledge Graph presence. In Google and Wikidata.
3. Make sure your content is rendered on the server side. Google has become very adept at rendering content on the client side. Bots like GPT-bot only see the HTML response. JavaScript is still clunky.
4. Well-structured, machine-readable content in relevant formats. Tables, lists, properly structured semantic HTML.
5. Get. Yourself. Out. There. Share your stuff. Make noise.
6. Be ultra, ultra clear on your website about who you are. Answer the relevant questions. Own your entities.

You have to balance direct associations (what you say) with semantic associations (what others say about you). Make your brand the obvious next word.

Modern SEO, with better marketing.

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