AI answer engines now synthesize responses from dozens of sources at once. Yet most companies still speak about their own brands in conflicting voices across departments. A new Semrush study lays bare the cost.
Only 22% of U.S. marketers, business owners and SEO professionals surveyed in April 2026 reported a fully integrated strategy for AI search and traditional SEO. The rest described gaps of varying severity. Business Insider first reported the findings.
Leigh McKenzie, director of online visibility at Semrush, put it plainly. “In the past, companies were able to operate in a fairly siloed approach.” Brand teams might post one message on social media. SEO teams wrote something different for the blog. Customer success ignored Reddit chatter. Those separations once carried limited risk. No longer.
Large language models pull indiscriminately from corporate sites, news coverage, forum discussions and video transcripts. Inconsistencies in messaging or data suddenly surface as inaccurate brand descriptions or missed citations. Thirty percent of respondents said their brand appeared inaccurately in AI results. Twenty-nine percent complained of unclear or generic positioning. Thirty-seven percent watched competitors mentioned more often.
The numbers come from a survey of 481 professionals. They reflect a broader pattern. Organizations pour resources into AI initiatives yet allow departmental walls to persist. A separate 2026 report found 79% of organizations create AI applications in silos. That fragmentation expands security risks and slows value capture. Writer.com detailed the findings.
But the visibility problem hits marketing hardest. AI search traffic could surpass traditional search volume by 2028, according to earlier Semrush analysis. Visitors arriving via AI answers convert at 4.4 times the rate of standard organic traffic. Those gains remain theoretical for companies unable to present a single coherent story. Semrush published the traffic study in 2025.
HubSpot offers a counterexample. The company ranks third in business and professional services in Semrush’s quarterly brand visibility index. Its CMO, Kipp Bodnar, rejected the idea of a standalone answer-engine team. “That integrated approach has allowed us to move faster, avoid duplicate work, and create a more consistent experience across both traditional search and AI-driven discovery,” he told Business Insider.
Such coordination remains rare. Eighteen percent of surveyed organizations assigned AI search to a dedicated generative engine optimization specialist or team. Sixteen percent housed it inside SEO. Fifteen percent gave it to content teams. Fourteen percent spread responsibility across groups. The rest defaulted to ad hoc efforts or ignored the channel.
At Semrush itself, the SEO function gained influence. McKenzie noted his seat at the leadership table grew substantially larger than two years earlier. The shift reflects new reality. AI models update frequently. What worked last quarter can vanish. Brands that treat visibility as an afterthought lose ground to rivals who align every external signal.
Recent data reinforces the volatility. ChatGPT responses cite lower-ranking traditional results nearly 90% of the time. Quora and Reddit dominate certain categories in Google AI Overviews. Yet half of all links in ChatGPT answers point to business or service websites. Models still need authoritative primary sources. They simply discover them differently. Semrush’s AI search trends report from March 2026 highlighted growing query complexity, multimodal inputs and declining click-through rates.
Click-through erosion accelerates the stakes. AI Overviews already appear on more than 13% of queries and continue expanding. When summaries satisfy user intent, clicks drop sharply. Visibility inside the answer matters more than ranking position. Companies fragmented across PR, content, social and product marketing cannot reliably influence those summaries.
Enterprise adoption surveys paint an even starker picture. Fifty-four percent of C-suite leaders admit AI efforts are pulling their organizations apart. Seventy-five percent describe current strategies as more for show than substance. Only 29% report significant return on generative AI investments. Data silos rank among the top barriers to scaling, according to multiple industry analyses released this year.
IDC research cited in recent reports identifies data quality, availability and departmental isolation as persistent obstacles. Seven in ten IT and business leaders point to silos when explaining why AI projects stall. The pattern repeats across sectors. Marketing cannot access real-time product data. Customer support insights never reach content teams. Brand guidelines evolve separately from technical documentation.
Forward-looking organizations respond by elevating visibility leaders and forming cross-functional groups. They audit brand mentions across every channel that feeds the models. They align messaging so Reddit threads, earnings calls, blog posts and support articles reinforce identical claims. Consistency reduces hallucination risk and improves citation frequency.
Yet many leaders still view AI search as a tactical SEO add-on. That mindset lags the market. Models synthesize. They do not rank links in isolation. A strong traditional ranking helps but does not guarantee inclusion. Brands cited in AI answers often appear because multiple signals converge: authoritative backlinks, clear entity definitions, consistent third-party discussion and structured data.
The gap between awareness and action widened in 2026. Projections once considered distant now feel immediate. AI traffic may deliver equivalent economic value to traditional search by the end of 2027. Companies that treat the two channels as separate bets risk ceding territory to integrated rivals.
Measurement challenges compound the problem. Teams without unified ownership struggle to track AI citations, share of voice or conversion lift from answer engines. They optimize for clicks that may never materialize. Or they chase rankings that no longer drive traffic. Both approaches waste resources.
Some enterprises experiment with AI-powered internal search tools precisely to combat their own data fragmentation. These systems unify knowledge across Slack, SharePoint, CRMs and intranets. Early results suggest productivity gains. The same principle applies externally. Brands that unify their external presence stand a better chance of unified representation inside AI responses.
McKenzie’s warning carries weight. Yesterday’s siloed comfort no longer works. AI surfaces every contradiction. It rewards coherence. Organizations that reorganize around this truth will capture disproportionate visibility and higher-value traffic. Those that delay face inaccurate descriptions, lower citation rates and shrinking influence in the next phase of search.
The data leaves little ambiguity. Integration is no longer optional. It determines which brands appear when users ask complex questions. And those questions now dominate AI query volume. Companies still operating as collections of independent teams risk becoming invisible in the very systems shaping customer discovery.
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