Imagine you want to buy a new pair of running shoes.

The old process might look like this:

Google → Search results → Open 10 websites → Read reviews → Compare prices → Watch YouTube videos → Make a decision.

Now imagine doing this instead:

“I run 5–10 km three times a week. I have slightly wide feet, a budget of ₹10,000, and I want something comfortable for road running. Compare the best options for me and explain the trade-offs.”

That is a very different shopping experience.

You didn’t search for ten websites.

You described your problem.

The AI helped interpret the problem, narrowed the options, compared them and explained the differences.

The customer journey is becoming conversational.

And this is one of the biggest changes marketers need to understand about AI.

AI isn’t simply creating a new place to advertise.

It is changing how consumers discover information, evaluate options and make decisions.

AI Is Changing the Question Customers Ask

Traditional search often begins with keywords.

For example:

• Best running shoes
• Best CRM for startups
• Best camera under ₹1 lakh
• Digital marketing course

AI interfaces make it easier to ask much more complicated questions.

Instead of:

“Best running shoes”

a customer can ask:

“I run mostly on roads, have wide feet, sometimes get knee discomfort and don’t want to spend more than ₹10,000. Which running shoes should I consider?”

The second question contains far more context.

And that changes the job of marketing.

The brand is no longer only trying to rank for a keyword.

It needs to become relevant to a problem, situation and set of constraints.

From Keywords to Intent

This is one of the most important shifts happening in search and discovery.

For years, marketers have thought in terms of keywords.

But customers don’t actually think in keywords.

They think in problems.

They think in goals.

They think in preferences.

They think in constraints.

They think:

“I need something that does X, costs less than Y and works in situation Z.”

AI makes it easier for consumers to express all of that at once.

Google itself has described AI Mode as shifting shopping journeys from keywords toward natural conversations, while its newer shopping experiences combine product information, prices, reviews and inventory information into more conversational experiences.

Google has also expanded AI-powered shopping features in India, including conversational shopping in AI Mode and product comparisons in Gemini. :contentReference[oaicite:0]{index=0}

That means marketers need to understand something fundamental:

The future of discovery may be less about matching a phrase and more about matching a situation.

The Old Consumer Journey

For a long time, a simplified buying journey looked like this:

Awareness → Search → Consideration → Comparison → Purchase

Each stage could involve different websites, platforms and sources.

You might discover a product on Instagram.

Search for it on Google.

Read reviews on Amazon.

Watch a YouTube review.

Compare prices on another website.

Then return to the original retailer and buy.

The customer was doing much of the research themselves.

The New AI-Assisted Consumer Journey

AI can compress many of those steps.

A simplified version can look like:

Problem → Prompt → AI Research → Recommendations → Comparison → Verification → Purchase

The customer still makes the decision.

But the interface between the customer and information has changed.

Instead of manually collecting information from ten places, the customer can ask an AI system to help organize it.

OpenAI’s current shopping experience, for example, allows users to describe what they want, refine results conversationally and compare products using information such as price, reviews and features. :contentReference[oaicite:1]{index=1}

This doesn’t mean every purchase will happen this way.

It means the consideration stage is becoming increasingly assisted by AI.

1. Discovery Is Moving From “Search” to “Conversation”

Search traditionally requires the customer to know what to type.

Conversation doesn’t have the same requirement.

You can explain what you want in ordinary language.

You can say:

“I need a laptop for video editing and gaming, but I travel frequently, so I don’t want anything too heavy.”

Or:

“I need a CRM for a 20-person B2B sales team. We care about automation and reporting, but we don’t want something complicated.”

Or:

“Find me a hotel in Mumbai for a weekend. I want something quiet, close to restaurants, with a good breakfast and under ₹15,000 a night.”

The consumer isn’t simply retrieving information.

They are delegating part of the research process.

2. AI Can Become the New Comparison Layer

Comparison websites have existed for years.

AI can make comparison conversational.

Instead of opening five product pages, a customer can ask:

“Compare these three phones for camera quality, battery life and long-term software support. I don’t care about gaming.”

The important word here is context.

A traditional comparison might tell you which phone has the best specifications.

An AI-assisted comparison can potentially explain which phone is the better fit for your priorities.

OpenAI’s March 2026 shopping update describes side-by-side product comparisons using attributes such as price, features and reviews. :contentReference[oaicite:2]{index=2}

This creates a new marketing challenge:

Being included in the comparison may become as important as being visible in the search result.

3. Recommendations Can Become More Personal

Traditional advertising often works with broad audience segments.

For example:

Men aged 25–34 interested in fitness.

But a conversational AI system can work with much richer information provided during a specific interaction.

The customer might say:

• My budget is ₹8,000.
• I run three times a week.
• I have wide feet.
• I prefer lightweight shoes.
• I don’t care about appearance.

Now the recommendation is based on a much more specific need.

This changes the meaning of personalization.

Personalization is moving from “Who are you?” toward “What are you trying to accomplish right now?”

4. AI Can Reduce the Number of Tabs Consumers Need

Think about the traditional online buying experience.

One tab for Google.

One for Reddit.

One for YouTube.

Three product pages.

One price comparison site.

One review website.

And eventually, you have 12 tabs open and still aren’t sure what to buy.

AI shopping interfaces are designed to reduce that fragmentation.

OpenAI describes its current shopping experience as helping consumers explore, compare and refine options in one conversational environment. :contentReference[oaicite:3]{index=3}

Google is pursuing a similar direction with AI Mode, Gemini and its Shopping Graph.

The implication for marketers is important:

Your website may no longer be the first place where the customer understands your product.

It may be the place where the AI gets information about your product.

5. The Product Page Is Becoming Data

This may sound strange, but it is strategically important.

For a human shopper, your product page is a page.

For an AI system, the information on that page can become structured evidence.

Product name.

Features.

Specifications.

Price.

Availability.

Reviews.

Use cases.

Compatibility.

Limitations.

Customer feedback.

The clearer and more accurate that information is, the easier it can be for systems to understand what the product actually does.

Google’s Shopping Graph already aggregates huge amounts of product information, while newer AI shopping experiences use product feeds and merchant data to improve discovery and comparison. :contentReference[oaicite:4]{index=4}

This means product information architecture is becoming a marketing issue.

Good product data is no longer only useful for humans.

6. Reviews May Become Even More Important

There is an interesting connection between AI and social proof.

When a customer asks an AI:

“Which of these two hotels is better for a quiet family trip?”

reviews can become an important source of evidence.

The AI can potentially analyze recurring patterns:

• What guests consistently praise
• What guests consistently complain about
• Who the property is best suited for
• What experiences are common
• Which differences matter between options

That makes one thing increasingly important:

What customers actually say about your brand.

Not just what your advertising says.

Not just what your homepage says.

The accumulated experience of customers becomes part of the information environment surrounding the brand.

7. Brand Familiarity May Matter More, Not Less

Here is where AI creates an interesting paradox.

If AI can compare hundreds of products instantly, shouldn’t brands become less important?

Not necessarily.

When consumers delegate more of the research process, they may rely more heavily on signals they already trust.

That can include:

• Brand familiarity
• Reputation
• Reviews
• Distinctive positioning
• Product quality
• Availability
• Clear differentiation
• Past experience

Imagine an AI gives you five options.

You may still ask:

“Which one is the brand I already trust?”

Or:

“Which one has the strongest reputation for this particular need?”

So AI may reduce the importance of some traditional discovery mechanics while increasing the importance of being meaningfully different and memorable.

This is one reason your work on brand strategy remains relevant in an AI-first world.

8. The New Battle Is Not Only “Rank #1”

Traditional SEO often creates a relatively simple objective:

Get the page to rank.

AI discovery introduces another question:

Does the system understand my brand well enough to mention or recommend it when the relevant problem is discussed?

This is part of the reason concepts such as GEO have emerged.

GEO is not simply “SEO for ChatGPT.”

It is better understood as the broader effort to make information about a brand, product or organization easier for AI systems to understand, retrieve and potentially reference.

The distinction matters.

You are no longer optimizing only for:

“What keyword should this page rank for?”

You increasingly also need to think about:

“What questions should this brand be a credible answer to?”

9. AI Changes the Meaning of “Consideration”

Consideration used to involve a lot of manual work.

Read.

Compare.

Open.

Close.

Search again.

Ask a friend.

Watch a review.

Compare again.

AI can compress this process.

But compression does not mean the consideration stage disappears.

It may become more concentrated.

Instead of spending an hour collecting information, the customer may spend ten minutes evaluating an AI-generated shortlist.

That means every shortlisted brand has less time to make its case.

Your value proposition needs to be clear.

Your differences need to be understandable.

Your evidence needs to be credible.

And your weaknesses may be easier to discover too.

10. The Customer Can Ask Follow-Up Questions

This is another major difference between traditional search and conversational discovery.

Search often requires a new query.

Conversation can continue from the previous context.

A customer might ask:

“Which laptop should I buy?”

Then:

“Which one is lighter?”

Then:

“Which one has better battery life?”

Then:

“What if I increase my budget by ₹20,000?”

Then:

“Which one would you personally choose for travel?”

The customer journey becomes a conversation rather than a sequence of disconnected searches.

Google has described this conversational follow-up experience as part of the evolution of AI Mode and AI Overviews. :contentReference[oaicite:5]{index=5}

11. AI Can Move Closer to the Purchase

The most important change may be what happens after discovery.

AI isn’t necessarily stopping at recommendations.

Shopping experiences are moving toward transactions.

OpenAI introduced Instant Checkout in 2025 and has continued expanding AI-powered product discovery in 2026. Google is similarly building agentic commerce infrastructure around Search, Gemini and its Universal Commerce Protocol. :contentReference[oaicite:6]{index=6}

This points toward a future where the customer journey could become:

Ask → Discover → Compare → Decide → Buy

without necessarily moving through ten different websites.

The implications for marketers are enormous.

12. The Website Isn’t Disappearing

This is where marketers can easily overreact.

AI doesn’t mean:

“Nobody will visit websites anymore.”

People still need detailed information.

They still want to verify claims.

They still want to see products.

They still want to understand companies.

They still want customer support.

And for many purchases, they will still want the brand’s own experience.

What is changing is the role of the website in discovery.

Instead of being the only place where the customer begins research, the website increasingly becomes one of the sources from which AI and consumers gather evidence.

That makes the quality of the website more important, not irrelevant.

13. Content Marketing Has a New Job

Traditional content marketing often asks:

“What keywords can we create articles around?”

AI-era content needs a broader question:

“What information would genuinely help a customer understand this problem?”

That means creating content that answers:

• What is this?
• Who is it for?
• Who is it not for?
• How does it compare?
• What are the trade-offs?
• What does it cost?
• What are the limitations?
• How does it work?
• What do customers experience?
• When should someone choose an alternative?

This kind of content is useful to humans first.

And that is exactly why it can also become useful in AI-mediated discovery.

14. Original Information Becomes More Valuable

There is another major consequence of generative AI.

If everyone can produce generic articles in seconds, generic information becomes easier to create.

That increases the value of information that is harder to reproduce.

For example:

• Original research
• First-party data
• Experiments
• Expert interviews
• Unique case studies
• Original frameworks
• Real customer experiences
• Proprietary analysis

For Brand Yaatra, this is particularly important.

A generic article saying “What is marketing?” can be produced by thousands of websites.

An original Brand Yaatra experiment measuring how people respond to different marketing messages is much harder to replicate.

AI makes original thinking more valuable, not less.

15. AI Makes Trust More Important

There is a strange contradiction here.

AI gives consumers more information.

But it also creates more information that consumers cannot easily verify.

AI-generated images.

AI-written reviews.

AI-generated videos.

AI-generated websites.

AI-generated product descriptions.

AI-generated recommendations.

As synthetic content becomes easier to produce, genuine evidence becomes more valuable.

That means brands will increasingly need to prove:

Why should anyone believe us?

Customer experiences.

Transparent information.

Demonstrable results.

Independent evidence.

Clear policies.

Strong reputation.

These become increasingly important trust signals.

16. The New Consumer Journey Is Not Linear

There is another important point.

AI doesn’t create one fixed journey.

It creates more possible journeys.

A consumer might:

See an Instagram post → Ask AI about the product → Compare alternatives → Read reviews → Visit the brand → Ask AI another question → Purchase.

Another might:

Ask AI → Discover a brand → Watch YouTube reviews → Visit Reddit → Return to AI → Purchase.

Another might:

Search Google → See an AI answer → Click a source → Compare products → Ask an AI follow-up → Buy.

The journey is becoming less like a funnel and more like a network.

The New AI Consumer Journey

Here’s a useful simplified model:

1. Trigger
A need, problem, desire or curiosity appears.

2. Prompt
The customer describes what they want to an AI or search engine.

3. Discovery
The system identifies relevant information, brands or products.

4. Shortlisting
The consumer receives a smaller set of options.

5. Comparison
Trade-offs, price, features, reviews and suitability are evaluated.

6. Verification
The consumer checks important claims and seeks additional evidence.

7. Decision
The consumer chooses.

8. Transaction
The purchase happens.

9. Experience
The customer uses the product.

10. Feedback
The experience becomes reviews, recommendations and future social proof.

And then that feedback can influence the next customer’s discovery.

What This Means for Marketers

1. Stop thinking only in keywords

Understand the questions, problems, situations and constraints behind the keywords.

2. Make your brand easy to understand

If your value proposition is confusing to humans, it is unlikely to become clearer simply because AI is involved.

3. Build information depth

Explain your products, services, use cases, differences, limitations and customer outcomes clearly.

4. Invest in reputation

Reviews, customer experiences and independent mentions can become increasingly important in AI-mediated discovery.

5. Create original information

Research, experiments, data and unique insights are harder to commoditize than generic content.

6. Think beyond your website

Customers discover brands across search, social platforms, communities, reviews, creators, AI systems and marketplaces.

7. Don’t abandon SEO

AI search does not make traditional search optimization irrelevant. Search engines still crawl, index and retrieve web information. The opportunity is to build strong SEO foundations while also making your content understandable and useful in AI-mediated discovery.

8. Think about GEO as an extension of discoverability

GEO can complement SEO by focusing on how your information may be understood and surfaced in AI-generated answers.

What Happens to Advertising?

Advertising isn’t disappearing either.

But its role may change.

Imagine a consumer asks:

“What’s the best family SUV under ₹30 lakh with good safety and comfortable third-row seating?”

That question reveals a lot of intent.

It is not simply an awareness query.

The customer is already trying to solve a specific problem.

That creates opportunities for marketers to appear at different points:

• When the customer first discovers the category
• When the customer compares options
• When the customer verifies information
• When the customer is ready to buy

Google is already experimenting with advertising experiences designed around AI-powered Search and conversational shopping journeys. :contentReference[oaicite:7]{index=7}

The broader shift is from:

“Show this person an advertisement.”

toward:

“Help this person make a decision at the right moment.”

The Biggest Change: AI Can Become Part of the Decision

This is ultimately what makes AI different from another marketing channel.

Social media can influence what you notice.

Search can influence what you discover.

Advertising can influence what you consider.

But an AI assistant can potentially participate in the research and comparison process itself.

It can answer:

• “Which one should I choose?”
• “What’s the difference?”
• “Is this worth the extra money?”
• “Which is better for my situation?”
• “What are the disadvantages?”
• “What alternatives should I consider?”

That puts AI much closer to the decision-making process.

The New Marketing Question

For years, marketers asked:

“How do we get customers to find us?”

In an AI-mediated world, another question becomes increasingly important:

“When someone asks for help solving the problem we solve, will our brand be part of the answer?”

That is a fundamentally different way of thinking about discoverability.

And it connects SEO, content, reputation, brand strategy, customer experience and GEO into one larger system.

Final Thought

AI is not simply giving consumers a faster search box.

It is changing the interface between people and information.

Consumers can describe problems instead of constructing perfect keywords.

They can compare options conversationally.

They can ask follow-up questions.

They can receive personalized recommendations.

And increasingly, AI systems are moving closer to the transaction itself.

That doesn’t mean traditional marketing disappears.

It means the customer journey becomes more intelligent, more conversational and potentially much more compressed.

And for marketers, the challenge becomes bigger than simply being visible.

You need to be understandable.

You need to be credible.

You need to be relevant to real problems.

And you need to give both humans and AI systems enough evidence to understand why your brand deserves consideration.

Because the next generation of consumers may not ask:

“Which website should I visit?”

They may simply ask:

“What should I choose?”

Frequently Asked Questions

How is AI changing consumer behaviour?

AI is making it easier for consumers to describe complex needs, compare options, receive personalized recommendations and continue research conversationally. This can reduce some of the manual work traditionally involved in product discovery and comparison.

How is AI changing the customer journey?

AI can compress parts of the traditional journey by combining discovery, research and comparison into a conversational experience. A simplified AI-assisted journey can move from problem to prompt, discovery, shortlisting, comparison, verification and purchase.

Will people stop using Google because of AI?

There is no basis for saying that people will stop using traditional search altogether. Google continues to expand Search with AI Mode, AI Overviews and shopping capabilities. The more useful observation is that search itself is becoming more conversational and capable of handling complex questions.

What is AI shopping?

AI shopping refers to using AI systems to help with product discovery, recommendations, comparison and potentially purchasing. Current experiences from companies such as Google and OpenAI increasingly support conversational product research and comparison.

What does AI mean for SEO?

SEO remains important because AI systems and search engines still depend on web information. However, marketers increasingly need to think beyond individual keywords and create clear, authoritative content that answers real questions and provides useful evidence.

What is GEO in marketing?

GEO, or Generative Engine Optimization, broadly refers to efforts to make information easier for AI systems to understand and potentially surface or reference in generated answers. It complements rather than simply replaces traditional SEO.

Will brands become less important because AI can compare everything?

Not necessarily. AI may make comparison easier, but consumers can still rely on brand familiarity, reputation, differentiation, customer experience and trust when evaluating recommendations. Greater transparency and competition can make strong brand meaning even more valuable.