AI Search Key Findings:
As AI-powered search tools like ChatGPT, Google SGE, and Perplexity reshape how people find information, brands face a new challenge: being left out of the conversation entirely.
Worse still, when they do appear, their products or messaging may be misrepresented in the very answers users trust most.
Web traffic from AI-driven referrals in the U.S. increased more than tenfold between July 2024 and February 2025, according to Adobe Analytics.
These visits also performed better. AI-generated users stayed longer, viewed more pages, and bounced less compared to those arriving from traditional search.
That growth signals a pivotal shift. Search is no longer just about ranking; it’s about recognition within a generated response.
7 Ways Brands Are Adapting to AI Search, According to Bright Data
Brands are turning to new monitoring methods that go beyond SEO dashboards. But that’s not all they’re doing to stay ahead of the curve.
I spoke with Ariel Shulman, chief product officer at Bright Data, a leading data collection platform. He discussed seven key strategies to help brands stay visible:
- Track brand representation in AI-generated responses
- Go beyond keyword tracking with use-case monitoring
- Structure content for AI-friendly extraction
- Audit AI outputs for accuracy and consistency
- Use infrastructure tools to monitor at scale
- Adopt new visibility metrics tailored to AI
- Continuously update content based on model behavior
Editor’s Note: This is a sponsored article created in partnership with Bright Data.
For many digital-first brands, this list can seem daunting.
But that’s why agencies and infrastructure providers, like Bright Data, are working with organizations to help them:
- Better understand how their brand appears across AI platforms
- Identify any gaps in their visibility approach
Who is Ariel Shulman?
Ariel Shulman is the chief product officer at Bright Data, where he leads product strategy and innovation across the company’s web data and AI solutions. With a background in corporate development and partnerships, he has played a key role in scaling Bright Data’s global footprint. Today, Shulman focuses on helping organizations unlock the full potential of public web data to drive AI performance, brand visibility, and digital transformation.
Rethink Visibility: AI Search Is Not SEO
Traditional SEO strategies focus on keywords, backlinks, and rankings.
But AI-driven platforms like ChatGPT, Perplexity, and Google SGE don’t simply index and rank links. They synthesize structured and unstructured data to generate direct, contextual responses.
As a result, brand visibility now depends on whether your business is included in those answers and how accurately you’re represented.
“Bright Data leverages its expertise to innovate advanced monitoring capabilities, empowering brands to efficiently track, analyze, and optimize their visibility across emerging AI-driven search environments,” Ariel says.
To meet the demands of AI search, companies must rethink how they monitor brand presence.
That means moving beyond keyword tracking toward use-case-aware searches that reflect specific consumer priorities.
This shift requires not just new tools, but a new mindset: visibility is about being part of the answer, not just on the page.
Visibility Challenges in the Age of AI Search
Even brands with strong SEO foundations are finding that traditional tactics fall short in AI-driven environments.
One of the biggest challenges? AI platforms don’t just retrieve content; they generate answers.
That shift changes what visibility looks like and what it requires to achieve.
“Companies must innovate their brand monitoring strategies to track brand appearances across emerging AI search tools, moving beyond traditional keyword tracking toward use-case-aware searches reflecting granular consumer priorities,” Ariel says.
And often, businesses don’t know what users are prompting or how their brand is being synthesized into responses.
Another challenge is that AI tools rely on structured data to generate answers, but many businesses overlook that requirement.
“Moreover, brands need to produce content carefully balanced for both traditional SEO (long-form) and AI-specific requirements (concise, structured),” Ariel says.
Without structured content optimized for both human readers and machine processing, brands risk being excluded from AI results or misrepresented by them.
4 Common Mistakes Brands Make in AI Search
Even with the right intentions, many brands fall into outdated habits that don’t translate to AI-driven environments.
According to Ariel, the most common missteps include:
- Relying on keyword-based monitoring instead of tracking how the brand appears in real answers
- Overlooking user intent and failing to track prompts or use-case-specific queries
- Neglecting structured data standards, making it harder for AI platforms to extract accurate brand content
- Skipping regular audits of how their brand is portrayed in AI-generated responses
These mistakes are understandable. After all, the SEO rulebook has dominated for decades. But without adapting, brands risk being misrepresented or left out entirely.
Visibility today means thinking beyond rankings. It’s about ensuring your content and data are structured so AI can find, interpret, and summarize your brand accurately across a range of queries and contexts.
What It Takes to Monitor and Improve Brand Presence in AI Search
Tracking how a brand appears across AI-generated results isn’t easy.
The outputs are dynamic, context-dependent, and fragmented across different AI platforms, and that’s where data infrastructure plays a vital role.
“Bright Data provides advanced infrastructure enabling businesses to accurately monitor how their brands appear within granular, use-case-aware contexts across AI-generated search results,” Ariel says.
Rather than relying on surface-level tools, Bright Data enables brands to analyze AI responses at scale.
This includes identifying when their brand is missing from relevant queries, spotting inconsistencies across platforms, and flagging inaccurate representations.
“Clients typically approach us when facing inconsistent AI-driven brand mentions, limited visibility into large-scale AI outputs, or gaps between structured data and actual AI representations,” Ariel says.
One global eCommerce client, for example, used Bright Data’s monitoring capabilities to uncover and correct misaligned brand mentions across multiple AI search platforms.
The result: improved representation across AI platforms, greater consistency in messaging, and stronger engagement with customers.
Signs It’s Time to Consider External Help
For many teams, the shift to AI search happens gradually until visibility drops or inconsistencies start affecting customer trust.
But there are clear signs that a more structured approach to monitoring and optimization is needed.
“Clients typically approach us when facing inconsistent AI-driven brand mentions, limited visibility into large-scale AI outputs, or gaps between structured data and actual AI representations,” Ariel says.
If your brand is:
- Being mentioned inaccurately across different AI tools…
- Missing from AI-generated responses where competitors appear…
- Unsure how structured data is impacting AI model outputs…
- Unable to track brand performance across AI platforms in a scalable way…
…then it may be time to revisit your data infrastructure and monitoring tools.
AI-generated visibility isn’t just about publishing more content.
It’s about making sure the right data is being extracted, interpreted, and surfaced in ways that reflect your brand accurately and consistently.
What Executives Should Track (That Traditional SEO Doesn’t Cover)
Traditional SEO metrics like impressions, rankings, and click-through rates don’t capture how a brand performs in AI-generated answers.
Different metrics matter, ones that focus on representation, accuracy, and contextual visibility.
“Executives should prioritize metrics such as accuracy and consistency of AI-generated brand mentions, coverage within use-case-specific AI queries, and visibility in conversational AI contexts,” Ariel says.
These metrics shift the focus from traffic quantity to brand quality.
Instead of asking where you rank, brands need to ask:
- Are we being included in AI-generated responses?
- Are our products and messaging being represented accurately?
- Are we appearing in the use-case contexts most relevant to our audience?
Monitoring these dimensions requires a different toolkit, but for executives, they offer a more strategic view of how the brand is perceived in emerging AI-driven discovery paths.
What’s Next for AI Search & How Brands Can Stay Ahead
The future of AI search is evolving toward deeper context, more conversational interactions, and hyper-specific user intent.
As generative tools become the default way people find answers, visibility will depend on how well brands align with those behaviors.
“The major trends shaping AI search include increasing reliance on context-driven, conversational interactions and deeper granularity in consumer use-case queries,” Ariel says.
That shift creates both a challenge and an opportunity. Brands that continue to optimize solely for traditional SEO may see declining visibility.
But those that adapt their data and content strategies now will be better positioned to stay discoverable as AI platforms mature.
“As these trends accelerate, brands risk declining visibility if they don't proactively optimize content and data for AI-driven extraction and understand distinct consumer intents,” Ariel explains.
To stay ahead, companies must:
- Structure content for machine readability and summarization
- Monitor how their brand is appearing across different AI-generated responses
- Continuously update content based on how models interpret and surface it
Generative search is a fundamental shift in how people discover, evaluate, and engage with brands.
Why Industry Collaboration Matters
AI search is evolving fast, and no single company has all the answers.
That’s why transparency, shared standards, and open dialogue are critical to helping the entire ecosystem adapt responsibly.
“Industry leaders should openly discuss best practices around standardizing data for AI consumption, ensuring accuracy and transparency in AI-generated outputs, and ethical approaches to web data gathering,” Ariel says.
Contributing to shared understanding allows companies to:
- Shape how AI systems interpret brand content
- Reduce the risk of misinformation
- Build a healthier discovery environment for consumers
“Bright Data draws inspiration from organizations like Common Crawl, whose commitment to open, structured, and accessible web data has helped shape our approach to responsible, scalable AI data infrastructure,” Ariel says.
Brands don’t just need better tools; they need a more open, collaborative ecosystem that allows them to understand how AI sees them and how to improve that view together.
