OpenAI has started rolling out GPT-6 in ChatGPT alongside what it calls Intelligent UI - a system that can add visuals and interactive tools directly inside answers. This is not a cosmetic change. It signals a structural shift in how AI answer engines present information, and it has direct consequences for brands trying to earn visibility and citations in those surfaces.
The question practitioners need to ask is not "how impressive is this?" but rather: what does my content need to look like for an AI system to use it when building an interactive answer? The mechanics of citation and extraction are changing, and content strategies built around plain prose are about to feel that strain.
What Intelligent UI Actually Does to the Answer Format
Text-only answers have always been the dominant format in AI search. Even when sources are cited, the response is a block of synthesised prose. Intelligent UI changes that by allowing ChatGPT to surface interactive elements - comparisons, calculators, visual breakdowns - as part of the answer itself. The answer becomes a tool, not just a summary.
That matters for AEO because AI systems constructing interactive outputs need data they can extract cleanly and render in structured form. Prose that buries a comparison table inside three paragraphs is harder to use than content that presents the same information in a structured, scannable format. The underlying content architecture starts to matter in a way it has not before.
Other AI search products appear to be moving in a similar direction. Perplexity's answer pages have been incorporating more structured sections alongside citations, and Google's AI Overviews have included visual elements for certain query types. GPT-6's Intelligent UI continues that trend. The direction across the major AI surfaces is the same: richer, more structured outputs.
What This Means for Content Structured Toward AI Extraction
AI extraction has always favoured content that is specific, factual, and organised. Question-and-answer formats, clear headings, defined entities, and explicit comparisons all make it easier for a model to pull usable information. That principle does not change with Intelligent UI - it becomes more important.
If an AI system is building an interactive comparison of, say, business insurance products, it needs discrete data points: coverage type, price range, exclusions, provider name. Content that presents those facts in a consistent, extractable structure is far more likely to contribute to that answer than a long-form article that covers the same ground in narrative form. Brands that have built product and service pages around clear data attributes are better positioned for this shift than those that have not.
Schema markup becomes more relevant here, not less. Structured data tells crawlers and AI systems what a piece of content is about at a machine-readable level. If your product page uses appropriate schema - pricing, reviews, specifications - that information is available for extraction in a form that maps naturally onto structured visual outputs. Brands that treat schema as a box-ticking exercise rather than a content signal will notice the gap widening.
The Citation Question Gets Harder to Answer
One of the consistent challenges in AEO measurement is tracking when and how your content is cited inside an AI answer. When answers were text-based, the citation model was relatively straightforward - a source link appeared at the bottom or inline. Interactive answers complicate this. If your data is used to populate a comparison widget, does a traditional citation appear? Does the user see your brand as the source at all?
This is not a hypothetical. It affects how you measure AI-referred traffic and leads. If interactive elements in ChatGPT answers reduce the frequency of source links, referral traffic from ChatGPT to your site may not reflect your actual visibility inside those answers. The measurement gap that already exists in AI search gets wider. Teams need to be tracking brand mentions in AI outputs directly - through prompt-based monitoring and brand entity tracking - not relying on referral traffic as the sole signal.
CRM attribution is where this gets operationally significant. If a prospect uses ChatGPT's interactive UI to compare your service against competitors, decides to contact you, and arrives via a direct or dark traffic session, the CRM entry may show no source. That lead came from AI search. Your reporting will not say so unless you have built the right attribution architecture - UTM hygiene on all owned links, first-party lead source questions at the point of conversion, and direct channel auditing that accounts for AI-referred sessions.
Bot Access and Crawler Permissions Need Revisiting
For AI systems to use your content in richer, more structured outputs, they need to be able to access it. That sounds obvious, but a number of sites block OpenAI's crawlers - either intentionally or as a side effect of broad bot-blocking rules. If your content cannot be crawled, it cannot be cited, regardless of how well it is structured.
The llms.txt convention - which some publishers have adopted to communicate content access preferences to AI crawlers - sits alongside robots.txt as a way to signal which pages you want AI systems to reach. Note that it is not a formal standard and its adoption and interpretation varies across crawlers. If you have not audited your crawler permissions recently - including for OAI-SearchBot, PerplexityBot, and Google-Extended - now is the moment to do that. The content you produce for AI visibility is worthless if the systems you want to cite it cannot reach it.
Where Digital PR Fits Into This
Interactive AI answers draw on multiple signals to determine which sources are authoritative enough to cite or use as data inputs. Third-party mentions, coverage in credible publications, and the frequency with which your brand appears alongside specific topics all contribute to how AI systems perceive your authority in a given area. Digital PR is a direct input to that signal.
The brands most likely to appear inside GPT-6's interactive answers are those that already have a clear, consistent presence across authoritative sources. That means genuine press coverage, inclusion in comparison and review content from independent publishers, and a track record of being cited in the kind of editorial contexts that AI systems treat as credible. Thin brand signals do not survive the shift to richer AI outputs - they get filtered out in favour of sources with more corroboration.
The practical implication is that digital PR and AEO need to be planned together, not run as separate workstreams. Coverage in a credible outlet builds the entity signal that makes your brand citable. Structured content on your own site gives the AI system something useful to extract once it recognises you as a valid source. Neither works as well without the other.
What to Do Now
The immediate priority is a content audit focused on structure. Identify your highest-value pages - service comparisons, product specifications, FAQ content, pricing information - and assess whether the data on those pages is presented in a way that can be extracted cleanly. Tables, defined lists, consistent labelling, and schema markup are all worth reviewing against what you actually have live.
Alongside that, check your crawler access settings. Confirm that AI bots can reach your key content pages. Put first-party lead source tracking in place if you have not already - a simple "how did you hear about us" field tied to CRM records gives you a layer of attribution that referral traffic alone will never provide. And start monitoring your brand's presence in AI outputs directly, not just through traffic reports.
Interactive AI answers raise the stakes on content quality and structure. The brands that treat this as a content engineering problem - not just a writing problem - are the ones that will show up inside those answers rather than watching competitors fill that space.