Marketers Face Higher Expectations Across Search and AI Tools
Generative AI isn't replacing search. Only a third of consumers believe GenAI is as effective as a traditional search engine. People still rely on Google, social platforms, and brand sites for research - and they want more detail, more comparisons, and consistent answers everywhere.
The takeaway: winning visibility now means serving multiple discovery paths at once. Your content has to work for AI summaries and classic SERPs, plus social and retail search. Specific, conversational, and trustworthy beats vague and fluffy every time.
What the data says
- Only one-third of customers say GenAI tools match search engines for effectiveness.
- 82% read AI Overviews in search results, yet most keep researching beyond them.
- 31% say AI summaries increase research time; over two-thirds continue past Google's AI Overview.
- 31% are more likely to consider products recommended by AI Overviews - you can't favor one engine or format.
- 51% changed research habits due to GenAI; 71% changed phrasing; 38% use more specific terms.
What this means for your content strategy
- Build for two fronts: AI answers and blue links. Write clear, specific, citation-friendly content. Anticipate the exact questions people ask and answer them directly.
- Refresh where research happens: search, social, retail listings. Maintain FAQs, comparisons, spec sheets, and reviews. Keep them current and consistent.
- Optimize for natural language: include question-based headings, concise summaries, and follow-up clarifications to match the way people now query.
- Strengthen trust signals: credentials, sources, dates, review volume/recency, and product details that reduce uncertainty.
Operate for multi-path discovery
Your goal is a single source of truth that flows to every surface: site pages, SERP snippets, AI summaries, product feeds, and social posts. Inconsistency kills trust.
- Create canonical answers for top customer questions and synchronize them across channels.
- Track where answers drift. Update the origin content first, then push updates everywhere else.
Rising governance and transparency expectations
By 2028, most brands will use agentic AI for one-to-one interactions. That's bigger than a new channel - it's a shift to AI systems acting on behalf of customers and teams. With that comes higher standards for data quality, auditing, and oversight.
- Data governance first: document sources, permissions, usage rights, and retention. Audit data quality monthly.
- Label AI content clearly: 78% of consumers want explicit disclosure. Use visible labels and keep logs of AI involvement.
- Fight fakery: verify creators, confirm content origin, and detect deepfakes. Consider provenance tech such as the C2PA standard.
- Shift budgets: move spend from pure reach to verification, authenticity, and review operations.
AI is changing search behavior, even when people still use Google
People are asking longer, clearer questions. They're scanning AI summaries, then digging deeper. That means your assets need to serve quick answers and deeper research in one flow.
- Open with a direct answer. Follow with details, comparisons, and proof.
- Structure pages for skimmers and researchers: TL;DR, then sections with specifics.
- Cover context users actually need: who it's for, trade-offs, pricing clarity, and alternatives.
How to prepare for agentic AI
- Move from channels to systems: less "run the social calendar," more "maintain data, policies, and models that serve every touchpoint."
- Integrate agents into martech: define guardrails, approvals, and escalation paths. Keep human sign-off for sensitive actions.
- Track the journey weekly: where do customers start, switch tools, and convert after AI touchpoints?
Practical 90-day plan
- Days 1-30: Audit top pages for direct answers, freshness, references, and alignment with AI summaries. Add FAQs, comparisons, and updated product data.
- Days 31-60: Roll out AI content labeling and a creator verification workflow. Stand up authenticity checks for influencer content.
- Days 61-90: Pilot a lightweight agent for on-site Q&A using approved content. Define metrics, human review, and data logging.
Metrics that matter
- Answer coverage: percentage of priority questions with a canonical, updated answer.
- Consistency score: alignment of claims across site, SERP snippets, AI summaries, and product listings.
- Research depth: engagement with FAQs, comparisons, and reviews; time to confident choice.
- Trust signals: labeled AI content views, creator verification rate, dispute rate, and resolution time.
Don't ignore AI Overviews
Even if users keep researching, AI Overviews still influence what they consider. Make your content easy to quote and verify. Clear answers, sources, and up-to-date details help systems select and summarize you correctly.
Learn how Google frames this experience here: Google on AI Overviews.
Team skills to build
- Data governance and policy writing
- Content operations for accuracy and version control
- AI orchestration and QA for agent outputs
- Creator verification and content provenance
If you're upskilling your team fast, see our AI certification for marketing specialists.
The bottom line
Search behavior is splintering. AI is visible, influential, and imperfect. Your edge comes from consistent answers, trustworthy content, and systems that keep pace with how people actually research and decide.
Serve every discovery path. Govern your data. Label your AI. Then let the metrics prove it.
Your membership also unlocks:
