How Google's AI search could revamp business strategy

What's changing with Google search?

Google has been expanding AI-generated answers throughout its search results, but this latest overhaul goes beyond an incremental update. Gemini 3.5 Flash now powers a much larger portion of search results, synthesizing information from multiple sources into a single conversational response.

Traditional search engines were primarily discovery tools, where users clicked through websites to get information. Businesses competed for those clicks through SEO, paid search campaigns and content marketing. Google's AI-powered search is changing that model by using generative AI to synthesize information from multiple sources directly within the search results, enabling users to complete more complex tasks without leaving the platform.

Rather than serving primarily as a gateway to the web, Google's AI search is becoming the place where users get their answers. This is prompting many organizations to rethink digital visibility and search optimization. Lauren Starr Dillon, head of marketing at Name.com, a domain registrar and web services company, said businesses should avoid treating AI search as simply another SEO challenge. While traditional SEO focuses on rankings and earning clicks, she said AI discoverability is about whether AI systems recognize a brand as authoritative enough to reference directly in their responses.

AI is making the early stages of the customer journey much less visible to brands.

The shift is also changing how businesses understand the customer journey. Rather than directing users to company websites early in the buying process, AI search enables people to research products, compare options and answer their initial questions -- all while on Google's platform.

"AI is making the early stages of the customer journey much less visible to brands," said Jen Jones, CMO at Siteimprove, a software company that provides digital marketing, website governance and content optimization tools. "Buyers can research products, compare options and answer many initial questions before ever visiting a company's website," she added.

The impact goes beyond website traffic. As AI-generated answers address more questions upfront, they are also changing how customers form opinions and make purchasing decisions, compressing the buying journey by reducing the need to compare information across multiple websites.

"Customers are beginning to shape their perception of a brand before ever visiting its website," Dillon said. "At the same time, AI search is often meeting people at a moment of much higher purchase intent."

This also changes what determines which brands customers encounter. Rather than relying on traditional popularity signals such as rankings and backlinks, AI systems look for credibility signals, like authoritative content, structured information and independent validation from external sources. For businesses, that means the challenge is no longer simply attracting traffic from search but establishing authority for AI systems to recognize and reference.

"It's an authority problem, not a traffic problem," Sharma said.

Google Zero: When search visibility doesn't guarantee traffic

Traditional search is becoming a zero-click experience, where users find answers through featured snippets, knowledge panels and other elements on Google's results page. According to SparkToro and Datos research, nearly 59% of Google searches in the U.S. in 2024 ended without a click to an external website.

AI-powered search could accelerate this trend by enabling search engines to synthesize more complex answers, reducing the need for users to navigate multiple websites. If this continues, strong search rankings won't guarantee traffic. This scenario, sometimes called "Google Zero," sees users receive complete answers within AI-powered search experiences rather than visiting the original source.

As a result, the value of appearing in search results could change. A brand might appear in AI-generated answers without receiving a visit, making traditional traffic-based metrics less useful for measuring search performance.

As AI systems increasingly summarize information instead of directing users to individual websites, companies must think beyond whether they appear in search results and consider how their brands are represented in AI-generated answers.

Brands need more than just visibility. They also need to be represented accurately and positively within AI-generated responses.

Kratchounova said brands will need to focus on being discovered and how they're represented within AI-generated responses. "Because AI systems synthesize and interpret information instead of simply ranking webpages, brands need more than just visibility. They also need to be represented accurately and positively within AI-generated responses," she said.

How AI search disruption will vary across industries

AI-powered search won't affect every business in the same way. Disruption will depend partly on how customers discover, evaluate and purchase products and services and how much a business relies on search to reach them. Organizations that rely on informational search might face greater pressure, while businesses with strong brands, direct customer relationships or local presence might experience a different set of opportunities.

Publishers and affiliate websites could face some of the biggest challenges, since their business models depend on answering informational queries that AI systems summarize directly. A study from Ahrefs found that Google AI Overviews can significantly reduce click-through rates for traditional organic results, with one analysis showing declines of up to 58% depending on the query and search intent.

These businesses should place greater emphasis on subscriptions, newsletters and communities and produce original reporting and expert analysis that AI systems prioritize.

E-commerce companies face a different challenge, as AI-powered shopping experiences could shorten browsing journeys. For retailers, visibility might depend on whether AI systems can interpret structured product information, pricing and reviews.

B2B companies could see fewer prospects reaching their websites if AI systems answer educational and comparison questions directly, though longer B2B sales cycles and multiple stakeholders mean brand authority across AI-generated answers still matters, even without an immediate site visit.

Local and service-based businesses might see a different outcome: Organizations with accurate listings, strong reviews and clear service information could benefit as AI systems help users discover nearby providers, creating a new discovery channel rather than replacing customer interactions.

Evaluating your organization's reliance on search

The first step is to understand how much revenue, customer acquisition and brand awareness depend on organic search and where that reliance is concentrated across the business. Many organizations have the data to begin that assessment, Jones said. "Companies should already be tracking how much of their traffic and conversions come from organic search compared to other acquisition channels," she noted. Understanding that mix is critical, she said, because it shows how much of a business could be affected as search behavior continues to evolve.

To evaluate that exposure, organizations should ask themselves the following questions:

• What percentage of leads, conversions and revenue originates from organic search?
• Which pages, topics or content assets drive the most valuable customer interactions?
• Are customers discovering the company primarily through informational content that AI systems could summarize directly?
• Does the organization have direct relationships with customers through email, communities, subscriptions or first-party data?

Organizations with years of investment in SEO-driven content marketing, limited owned-audience channels and business models that depend heavily on capturing top-of-funnel informational traffic might be more exposed to changes in search behavior.

For these businesses, the consequences could extend beyond declining website traffic. A decline in search-driven traffic could affect audience growth, pipeline generation and revenue while also reducing visibility into how customers discover and evaluate search results. As AI systems answer questions before users reach company websites, organizations might have fewer opportunities to understand customer intent during the early stages of the buying process.

Businesses with more diversified acquisition strategies might be better positioned to adapt. Strong brands, loyal customers, active communities, email programs and high-touch sales processes can give companies multiple ways to reach and engage customers beyond search.

Customer reviews, publisher reviews and community-driven platforms such as Reddit are now influencing how AI systems evaluate and represent brands.

That doesn't mean organizations should abandon search. Instead, Jones said companies should identify which elements of their existing SEO strategies have been most effective and apply those lessons to AI-driven search and discovery. Traditional SEO still matters, she said, but it should account for AI-generated search results and LLM-based discovery.

Building resilience beyond Google search

The shift toward AI-powered search requires executives to treat discoverability as a broader business capability that spans digital infrastructure, brand authority, customer relationships and measurement. Businesses have four priorities to ensure customers find, evaluate and engage with them online.

1. Build AI-ready digital experiences

Traditional SEO is focused on earning rankings and clicks. AI search introduces a different challenge: becoming a trusted source that AI systems reference or cite when generating answers.

Organizations are investing in content quality, structured data, authoritative information and clear signals of expertise to improve the likelihood that their information will be understood and surfaced by AI systems.

"AI rewards depth, authenticity and a genuine point of view," Jones said. "Simply assembling a page full of keywords is no longer enough."

For businesses, this could mean creating content that directly addresses customer questions, strengthening technical foundations, improving product and service information, and ensuring digital assets are structured in ways AI systems can interpret.

Companies will also need to create information that provides original insights, proprietary data, expert perspectives or useful analysis rather than simply repeating information that AI systems already understand.

UiPath's Sharma said organizations should consider whether a piece of content is "citation-worthy" -- meaning it provides enough specificity and authority for an AI system to use when answering a buyer's question.

But becoming visible in AI-generated answers requires more than optimizing a company's website. AI systems rely on external validation, including media coverage, analyst research, customer reviews and other independent mentions that confirm whether a company is an authority in its market.

"AI systems can't assess credibility from the source -- everyone claims best-in-class," Sharma said. "They rely on independent documentation."

Dillon echoed this point, saying AI systems build their understanding of companies from a range of sources, not just their websites.

"Your website, domain, content, media coverage, reviews and social presence shape how your brand is understood," Dillon said. "Customer reviews, publisher reviews and community-driven platforms such as Reddit are now influencing how AI systems evaluate and represent brands."

AI search optimization cuts across marketing, content, SEO, PR and product. No single function owns it today.

For organizations, this means AI visibility should be treated as a broader authority-building effort rather than another attempt to chase search algorithms. Companies will need consistent, credible information across their own digital properties and the external sources AI systems use to understand their brands.

"AI search optimization cuts across marketing, content, SEO, PR and product," Sharma said. "No single function owns it today."

Organizations that treat AI search as a cross-functional effort will be better positioned to build authority and maintain consistent brand representation across AI-driven platforms.

2. Reduce reliance on a single discovery channel

As AI search increasingly answers questions before users reach company websites, businesses are looking for ways to build customer relationships that exist beyond search discovery. That includes investing in email newsletters, customer communities, first-party data strategies, loyalty programs and other owned channels that enable businesses to maintain direct connections with their audiences.

Rather than viewing search optimization and owned audiences as separate strategies, companies should treat them as connected parts of a broader digital ecosystem. The content and expertise developed for newsletters, communities and customer education can also inform the content and digital assets used across search and AI-driven discovery channels.

That's especially important for businesses whose growth depends on repeat engagement, retention or customer loyalty rather than one-time discovery. A software company might need to look beyond search-driven educational content and invest in communities, customer advocacy and product-led experiences. A publisher might need to strengthen subscriber relationships rather than rely primarily on search traffic. An e-commerce company might need to invest more heavily in brand recognition and direct customer relationships as AI assistants influence purchasing decisions.

"Search optimization and building owned audiences are all part of the same ecosystem," said Kratchounova. "Organizations should look for ways that investments in communities, newsletters and first-party data can also strengthen broader digital visibility."

Companies with strong direct relationships will have more opportunities to maintain engagement and gather first-party insights, even as AI systems make more of the customer journey less visible to businesses.

3. Prepare for agent-driven customer journeys

The next phase of AI search is moving beyond answers toward action. As AI search becomes more agentic, the customer journey might extend beyond discovery and evaluation to include transactions and other tasks completed on a user's behalf. AI systems will help customers compare products, book services and complete transactions, so businesses will need to ensure AI agents can accurately access, interpret and act on information about their offerings. That might require improving product feeds, structured data, digital catalogs, inventory information and service availability.

This changes the role of digital content. Companies are no longer just creating information for human visitors navigating websites; they're also creating digital assets and structured data that AI systems need to interpret, evaluate and act on when assisting customers.

The companies that will succeed are the ones that make their businesses easier for both customers and AI systems to understand.

For e-commerce organizations, the priority would be to ensure that AI systems can accurately understand product details, pricing and availability. For service businesses, it might mean ensuring customer information and booking options are easy for AI agents to interpret.

"The companies that will succeed are the ones that make their businesses easier for both customers and AI systems to understand," said Jones.

That requires more than technical improvements alone. Companies will need consistent, accurate information across their websites, product databases, customer platforms and third-party sources so AI systems can interpret their offerings and act on behalf of customers.

4. Rethink how success is measured

Traditional search metrics such as rankings, impressions and organic traffic will remain useful, but they might no longer capture the full picture of how customers discover and evaluate a brand.

Organizations should begin tracking a broader set of indicators to understand how AI is affecting visibility, brand representation and business outcomes. These metrics could include the following:

• How often the brand appears in AI-generated answers.
• Whether products and services are accurately represented.
• Which sources AI systems cite when discussing the brand.
• How visible the brand is across different AI platforms and prompts.
• Qualified leads, conversions and revenue influenced by AI-driven discovery.
• Growth in direct traffic, subscribers, community participation and other owned relationships.

The goal is not simply to replace SEO metrics with a new set of AI metrics. Instead, executives should develop a broader view of how customers discover, evaluate and engage with the organization across traditional search, AI-driven platforms and direct channels.

A broader measurement strategy can help organizations understand where they remain dependent on search, where AI is influencing customer decisions and which investments are contributing to business outcomes.

Kinza Yasar covers AI and emerging technology for TechTarget, with a focus on ethics, enterprise adoption, governance and business strategy. Before moving into journalism, she worked in IT and network support roles, giving her a systems-level perspective on how enterprise technologies are built, deployed and managed.