A Gartner survey has found that many consumers do not believe generative AI will be an adequate replacement for traditional search engines.
The technology research and advisory firm revealed that only one-third of customers agree that GenAI tools are as effective as search engines, with many still relying on traditional search engines, social platforms, and brand websites for primary research.
For marketing teams, this means content strategies must support both AI-driven discovery and traditional search channels.
With no clear indication on which methods will overtake others, this increased visibility for multiple discovery paths means marketing teams will now have to appeal to the masses to remain discoverable across all environments.
Emma Mathison, Senior Principal, Research in the Gartner Marketing practice, explained how marketers must now strategically support multiple discovery paths, rather than expecting one to replace another, whilst still maintaining accuracy, clarity, and consistency.
“Marketers cannot afford to think of AI as a replacement for traditional search,” she said.
“Winning visibility now means optimizing for both AI-driven answers and classic search results, with content that is specific, conversational, and trustworthy.”
“That means refreshing content regularly across search, social, and retail platforms, as well as investing in comparison tools, FAQs, and reviews to meet consumers’ demand for deeper research and broader consideration sets.”
However, despite many consumers now turning toward generative AI for answers, Gartner has uncovered how current GenAI features are prolonging the research journey for consumers compared to other search engines.
In its research, Gartner found that 31% of consumers agreed that AI summaries were increasing research time, and that over two-thirds continued their journey past Google’s AI Overview, with Gartner suggesting that these summaries cannot produce definitive answers.
And whilst 82% reportedly read AI Overviews in search results, many consumers admit they do not rely on these answers exclusively for definitive information.
However, for marketers, 31% of customers are supposedly more likely to consider AI overview-recommended products, meaning teams cannot prioritize one search engine over another during campaigns.
Mathison further highlighted how marketers must continue to advertise their content in line with these consumer trends, ensuring that companies can evolve with customer expectations.
“These findings underscore the need for marketers to maintain strong content strategies across traditional and AI-driven channels, ensuring consistency and relevance as consumer behaviors evolve”
Despite generative AI not being at a stage to take over traditional search engines, these tools are still transforming search behavior on platforms such as Google and social media.
In a survey conducted later in 2025, research found that there has been a definitive shift in GenAI’s arrival into the researching space.
In fact, 51% of consumers admitted that their researching habits have now changed due to generative AI, with 71% of these respondents agreeing that it has altered their phrasing, as well as 38% now including more specific terms to receive targeted results.
This data indicates that despite GenAI’s relevance to direct research, its implementation within traditional search engines has transformed how people research, with increased natural language and specificity, this means that marketing content must now align with detailed, question-based, and conversational queries across all research forms.
Increasing Governance and Transparency Expectations
Gartner has also found that marketing expectations have further increased to strengthen data governance and transparency in AI.
The technology research and advisory firm further predicted that by 2028, 60% of brands will be using agentic AI to create more streamlined one-to-one interactions.
AI agents are now working beyond generative responses into autonomous capabilities on behalf of users to improve productivity, helping marketing and sales teams to deliver qualitative experiences at scale, marking the end of traditional channel-based marketing.
Gartner argues that this shift will require marketing teams to implement stronger data governance, transparency, and changes in how they operate to handle personalization ethically and effectively.
Emily Weiss, Senior Principal Researcher in the Gartner Marketing practice, noted that marketing organized around separate channels is no longer effective, with marketing teams now expected to focus on data quality, governance, and ongoing oversight rather than channel execution.
“This marks the end of channel-based marketing as we know it,” she explained.
“Marketers must prepare by putting strong data governance in place, tracking customer journey changes weekly, and integrating agentic systems into martech stacks to enable secure, ethical personalization at scale.”
Furthermore, Gartner predicts that by 2027, brands will allocate 50% of their influencer marketing budgets to content and creator authenticity initiatives.
With AI now simplifying content generation from large volumes of data, such as images, videos, and personas, this can increase the likelihood of fake or misleading content being amplified, with brands risking severe reputational damage if trust is lost.
This increase in media falsehoods may force brands to strengthen preventive measures to verify content from social platforms, including detecting or preventing deepfakes, verifying creator identities, and confirming a content’s origin.
In fact, 78% of consumers agree that explicit labeling of AI-generated content is “very important” or “the most important factor” in maintaining trust.
Customers are now strongly valuing transparency around AI, meaning budgets will have to shift away from pure outreach and volume toward verification and quality controls to meet these new standards.
Quite possibly, brands that choose to delay this budget shift could face a higher risk as consumer expectations and technology continue to evolve.
However, brands that do adapt early can build resilience to changes in AI-mediated search and discovery environments.
By confirming creator authenticity and content integrity, engagement quality increases, and compliance and transparency become baseline expectations.
Rising AI Expectations and the Future of Marketing
Marketing expectations are expected to increase as AI becomes more visible during decision support, with customers now expecting brands to stand behind AI-driven outputs.
This increase in expectations for marketing teams means that customer expectations could also rise in accordance.
Whilst many consumers still report primary reliance on traditional search engines, AI’s rising implementation of these tools will impact a customer’s research decisions, increasing their overall expectations for information gathering.
With over half of all brands expected to utilize agentic AI to improve personalization with automated one-to-one interactions, customers will expect relevant answers immediately, from either AI agents or search results.
This will also include consistency expectations across the customer journey, meaning marketers must align information surfaced in search, AI summaries, and agent interactions.
Marketers could also experience higher transparency expectations from this, especially when they interact with AI, to improve trustworthiness amid the rising concerns of content authenticity.
These expectation increases will mean marketers will need to shift from broad, static campaign planning to AI-driven interaction systems that respond to individual customer behavior in real-time.
This will allow marketing teams to deploy AI agents to tailor customer messages, offers, and guidance continuously rather than at specific touchpoints.
And with AI not being confined to one task or channel, this will allow marketing, sales, and customer-facing teams to intertwine, meaning customer interactions can be coordinated by autonomous systems based on individual customer data.
This will require stronger data practices and governance to ensure the AI systems remain accurate, ethical, secure, and within company guidelines.
For marketers, AI-driven campaigns need real-time customer data, privacy safeguards, and automated decision-making logic embedded in marketing technology stacks.
This may in turn see more marketing roles shifting toward AI system management, strategy, and oversight, with increased skilling in data governance, AI orchestration, and ethical design.
This also includes seeing increased engagement quality, trust signals, and customer value over time, as metrics become tied to AI agents rather than per channel.
Expectations may also increase during AI-driven campaign releases, product recommendations, and additional promotions, where errors or misleads may be attributed to the brand rather than the technology itself.
As AI improves, customers are unlikely to tolerate higher levels of friction and mistakes, meaning issues may be perceived as brand failure, and raise expectations for quality control and human oversight.
Eventually, customers will expect faster, more relevant, and more transparent interactions across all touchpoints.
This means that brands must adapt their strategies, data infrastructure, and operational models to respond to these changes effectively.
