Have you deployed conversational AI to help users navigate your site? Marketing teams may be producing more content than ever, but that content isn’t always easily accessible.
Often, case studies, blog posts, videos, and white papers may be too advanced for a visitor early in their journey.
Navigation menus assume their structure is obvious, and search bars assume the user knows the right keywords. Unfortunately, many feel overwhelmed by data overload.
Conversational artificial intelligence offers an opportunity to meet people where they are and help them find content relevant to their needs.
What Conversational AI Is and Why It Works for Content Discovery
Conversational artificial intelligence (AI) is the simulation of human conversation by computer programs or applications across chat interfaces, voice assistants and other platforms. Conversational systems use natural language processing and machine learning to understand intent beyond keywords. A more in-depth understanding and the ability to detect nuance matter for content discovery because reader questions rarely arrive fully formed.
There is evidence that searchers now expect conversational interfaces. According to Pew Research Center studies, 62% of adults report interacting with an AI-driven system at least several times a week. That familiarity lowers friction.
Users feel comfortable asking for more details, narrowing their request and exploring their options rather than conforming to your site structure.
Conversational AI works because it understands how people think. Instead of forcing visitors to translate their requests into navigation logic, it translates them into paths through content. This method is more in line with how marketing research actually works and is about exploration rather than retrieval.
Where Traditional Navigation Falls Short for Modern Marketing Content
Most business sites still utilize static hierarchical menus and basic search capabilities, which are more acceptable to visitors who know what they’re looking for. Those engaged in comparative evaluation, exploring a new category or testing an assumption may be highly friction-sensitive.
Visitors searching for relevant content often hit friction. The discovery gap leads to early exits, shallow engagement and missed opportunities to educate prospects before they’ve made a decision.
As libraries grow, resources can be lost under labels that are obvious to insiders but meaningless to outsiders, especially without adaptive guidance to help them.
How Conversational AI Improves Navigation and Discovery
Conversational AI moves the experience into an engaged assistant that listens, adapts and responds in real time, bringing a new level of interactivity:
- Conversational tools interpret intent from multiple turns of input, allowing a visitor to ask a more general question and then narrow it. The systems adjust their recommendations as context builds.
- The AI can connect related content, offering other articles, comparisons or relevant next steps that may interest users in addition to answering a question.
- It reduces cognitive load. Visitors don’t have to scan menus to guess categories. They ask a single question and receive personalized, efficient guidance in return.
These benefits are critical because the costs of discovery friction are high. In B2B contexts, where knowledge workers are often strapped for time and already spend nearly 29% of their workweek searching for information, prospects won’t take the time to stay and explore your content when they encounter unclear navigation on your site.
In B2C environments, where companies are already losing nearly 20% of traffic to zero-click search, visitors expect instant clarity or they leave. Conversational AI reduces this friction by meeting users where they are and guiding them to relevant content immediately.
Ways to Use Conversational AI for Marketing Content
Conversational AI should complement navigation for discovery tasks when default paths are inadequate rather than completely replace customary methods. Of the examples below, a few have combined strategies. It is also critical to understand how the design of conversational guidance fits within the scope of the rest of your content before narrowing down the most suitable approach.
Design Conversational Prompts Around Real Questions
Conversational AI is most effective when content relationships are predefined rather than inferred from content requests. Marketers should map the relationship among introductory content, comparative content, decision-stage content and post-purchase educational content to ensure consumers follow a logic-based path through assets of increasing specificity from the broader to the narrower.
Well-defined mappings also help prevent dead ends in conversation. The system knows which assets might match the current question and can guide the user on a journey that results in longer interactions.
Define and Map Content Relationships
Prompt design should reflect how people search when they have little confidence. Short keyword queries are still a very common way to find such information.
In 2023, the Pew Research Center analyzed 68,879 Google searches and found that only 8% of one- or two-word queries produced an AI summary, while 53% of queries with 10 or more words did.
That pattern aligns with what UX research firm Nielsen Norman Group refers to as “keyword foraging,” which is the time taken to identify the right keyword for a search before actually conducting it. Generative AI tools make this easier, as people can articulate what they want in human language when they are unsure of the correct term.
Ideally, prompts match what visitors ask over the phone, in chat logs and form fills, and in internal site search. Start them with intent and follow up with a short clarifying question to the right asset.
Use Conversations to Find Underutilized Assets
Marketing teams often have long-form content they may struggle to drive traffic to. Conversational AI allows them to use a secondary channel to distribute existing content, which can be presented to users based on their specific queries.
Teams can rely on existing content rather than continually creating new assets to address every gap in understanding. High-quality content can also provide clarity or depth.
Design Content for Conversational Discovery
Conversational AI also relies on the specific page sections it pulls into answers, such as summaries, headings and short excerpts. Clear and succinct summaries, informative subheadings, and consistent terminology give the conversational AI system greater confidence that it is extracting the most relevant content in response to the user’s context or a pure query.
Conversational experiences favor content that stands on its own, and points people to related concepts, rather than isolated recommendations.
Measurement can also identify improvements in conversational discovery over time. Surveys are among several tools used in user experience research, especially for questions about clarity, usefulness and perceived relevance. However, they are sensitive to the way a construct is defined.
Poorly constructed and misapplied measurement scales can lead to inaccurate results and a team optimizing for the wrong signals. Using standardized, quality-checked scales specifically designed to measure indicators of a construct enables more robust conclusions and better content decisions.
For marketers, this means supplementing conversational analytics with disciplined UX research. When feedback instruments receive the right signals, teams can determine how well their content drives discovery and where conversational nudges need fine-tuning.
Refine Responses Based on Engagement Data
Technology and marketing teams can also refine systems by analyzing which responses led to more extended conversations or conversions toward a specific goal, then adjusting follow-up questions and recommendations. If engagement data show the mismatch between user intent and content positioning, or if visitors frequently drop off after a set of recommendations, it is likely an alignment issue.
Reducing Friction While Guiding Users
Conversational AI adds value to a website’s marketing content by shifting the focus from site structure to use. Designers must remove potential roadblocks, understand intent and guide users through ambiguity to make existing content work.
For teams considering conversational AI as a tool to help users discover content more easily, now is the best time to audit for discovery gaps. As you do so, think deeply about the questions your audience is really asking, and find ways to surface content that might otherwise go unseen.
Eleanor Hecks is a design and marketing writer and researcher with a particular passion for CX topics. You can find her work as Editor in Chief of Designerly Magazine and as a writer for publications such as Clutch.co, Fast Company and Webdesigner Depot. Connect with her on LinkedIn or X to view her latest work.
