Leaders are focused on which new AI capabilities to add, while the more fundamental shift is already happening outside the organisation. Customer discovery, evaluation and decision-making are changing, whether or not a business has introduced any AI features of its own.
For enterprises running Adobe Experience Manager, that means stepping back from the tooling question. Before deciding what to add, or whether it's time to replatform, the real question is whether the digital experience already in place is prepared for how customers now behave.
Most enterprise conversations about AI are centred on technology. Leaders ask where they could introduce a chatbot, how generative tools might accelerate content production or which new capabilities should be added to the digital estate. But these otherwise reasonable questions run the risk of overlooking the more immediate change already taking place, outside the organisation.
As well as providing businesses with innovative new tools, it is giving customers new ways to find information, compare options and make decisions. In conversations with enterprise organisations, we're seeing growing interest in how AI is changing customer behaviour, rather than simply how AI can improve internal productivity. That shift is already visible in the numbers: Pew Research Center found in June 2026 that six in ten US adults now read AI-generated summaries in search results. McKinsey's consumer research also found that nearly half of consumers already use AI-based search at some point in their purchase journey.
For organisations running Adobe Experience Manager (AEM), this also creates a different kind of readiness question. Rather than looking at whether the platform has enough AI features, AEM businesses need to assess whether the content, customer journeys, information architecture, technology and operating model surrounding the platform are equipped for a world in which the customer's experience may begin long before they reach the website.
Enterprise digital experiences have traditionally been designed around the assumption that the website is the primary starting point. A customer searches, lands on a specifically selected page and moves through a journey curated by the organisation. Navigation, landing pages, product information and conversion routes all help progress users from initial interest towards a decision.
The rise of AI-driven discovery weakens that assumption. Customers can ask detailed, conversational questions of LLMs and AI tools and receive a synthesised response covering multiple providers, products or possible solutions. Google says people using AI Mode ask questions that are nearly three times longer than traditional searches, using more context, preferences and nuance in a single interaction.
By the time these customers arrive on a website, they are likely to already understand the market, have compared alternatives, have had a recommendation, and formed a view of the organisation. They may arrive directly on a detailed product, service or information page rather than moving from the homepage. In some cases, they may not visit at all. Pew's analysis of Google behaviour found that users clicked a conventional search result in just 8% of visits when an AI summary appeared, compared with 15% of visits without one.
It's worth clarifying that this is not the end of the website, however its role must adapt. Far from making the enterprise website irrelevant, this behaviour changes the role it must play. The website must contribute clear, credible information to AI-mediated discovery while also serving customers who arrive better informed, with more specific questions and potentially greater intent. Adobe reported that traffic from AI sources to US retail sites grew 393% year on year in the first quarter of 2026. Earlier Adobe research found that AI-referred shoppers spent 32% longer on sites, viewed 10% more pages and were 27% less likely to bounce than visitors from other channels.
This is a content and experience challenge, as well as a traffic acquisition challenge, and one that Digital and Technology Leaders need to address together.
AEM is a powerful enterprise platform, but the capability of the underlying technology does not automatically determine the adaptability of the digital experience built around it. Large digital estates are often the product of years of organisational decisions rather than platform limitations. Content models reflect old campaigns and internal structures. Templates multiply and integrations accumulate. Different regions, brands and business units develop their own processes, while governance and approval routes grow more complex.
None of this necessarily means the platform is failing. It does, however, mean organisations should distinguish between what AEM is technically capable of and what the wider experience, content, journeys and operating model built around it allows teams to do easily. Can your digital experience evolve quickly enough to support new channels and changing customer behaviours, regardless of whether AEM itself is capable? Can content be created once and reused appropriately across different experiences? Can teams respond quickly as customer language and behaviour change? Is important information clear, consistent and easy to find?
Adobe's own research found that 91% of organisations are already considering the impact of large-language-model search, and highlights the importance of clear authorship, credible information, reusable content and governance that helps teams move faster without losing accuracy or trust.
The question enterprises should ask therefore is not whether their AEM platform is ready for AI, it is:
'Is our digital ecosystem and business ready to respond to AI-driven change?'
Answering that broader question requires a joined-up assessment. AI readiness cannot sit solely with marketing, content teams, engineering or enterprise architecture because the barriers rarely fall neatly within one function. Five questions can help reveal where the real constraints and opportunities sit.
1. Is your content clear enough to drive visibility?
Content developed simply to fill web pages will struggle in an environment where information is interpreted, summarised and reused beyond the website. Yet AI-ready content does not, and must not, mean writing for machines at the expense of people. It means striking a balance between human-focused, journey enhancing content, and very clear, unambiguous information that can be read by robots. It means making expertise, propositions, evidence and essential information clear enough to be understood by all without relying on surrounding campaign context or visual presentation.
For AEM organisations, this raises practical questions about content structure. Is valuable information locked into long pages, esoteric visuals or inflexible components? Are product, service and organisational facts consistent across the estate? Is content modular enough to be reused without creating multiple conflicting versions? Are authorship, evidence and update dates visible where this level of credibility matters?
Clearer, more structured content benefits customers, internal teams and AI-driven discovery alike. It also creates a stronger foundation for teams using AI to research, produce, manage and optimise content responsibly.
2. Are your journeys designed for customers arriving later in the funnel?
A customer who has already used AI to research a complex product or service may not need a conventional awareness journey. They may arrive with a detailed question, looking for validation, proof or a clear next step.
Organisations need to examine whether their experience supports these informed entry points. Can a customer understand the proposition from a deep page without first visiting the homepage? Are comparisons, proof points, pricing information, limitations and next steps easy to find? Does the journey answer the questions customers actually ask, rather than only following the sequence the organisation would prefer them to take?
The objective is not to abandon carefully designed journeys. It is to recognise that customers may enter, leave and return at very different stages, and that every important page may now need to be able to stand alone as a credible first impression.
3. Can your operating model keep pace?
One of the consistent themes we're hearing from enterprise organisations isn't that AEM lacks capability, it's that evolving the customer experience has become increasingly difficult because of governance, operating models, lengthy build cycles and testing processes. Even the most capable platform will struggle to support changing customer expectations if publishing requires lengthy approval chains, ownership is unclear, or content teams cannot access the expertise needed to answer emerging questions.
An AI-empowered team doesn’t just need access to generative tools. It needs to have the governance, skills, data and authority to identify changing needs and improve the experience responsibly. AI can support research, analysis, production and optimisation, but human expertise remains essential for accuracy, relevance, judgement, and brand trust. Customer understanding too is a vital component of content creation in an AI-world, and teams must keep a close eye on changing customer behaviours and emerging trends to develop the right content to meet these changing needs.
Across the board adoption is moving faster than organisational maturity. McKinsey found that 88% of organisations were using AI in at least one business function in 2025, while most had still not scaled it across the enterprise. Digital and Technology Leaders need to look at how work moves through their organisation, where it slows down, and whether governance is protecting quality or simply preserving processes designed for a slower digital environment.
4. Is your architecture making adaptation easier?
When the conversation reaches technology, it should do so with a clearly defined problem. Organisations should assess whether their AEM architecture supports the experiences they now need to deliver. That includes examining integrations, content models, component libraries, data flows, and the ability to distribute consistent information across channels.
In some estates, the technology will be fundamentally sound, but specific implementation decisions, heavily customised components, duplicated content models or complex integrations, may mean relatively simple customer experience improvements become disproportionately difficult to deliver. Targeting work on improvements to content structure, UX, integration or governance can release significantly more value from the existing investment. Elsewhere, the assessment might reveal more substantial constraints. It’s then that platform modernisation or replatforming becomes a legitimate strategic consideration, but this should be considered as a response to evidenced business and customer needs rather than as an assumed consequence of AI.
5. Are you measuring the new journey?
Traditional website measures are still important, but they only provide part of the picture when discovery and decision-making increasingly occur elsewhere. Organisations need to understand whether their brand and expertise appear accurately in AI-generated responses, which customer questions are shaping discovery, and how AI-referred visitors behave once they arrive.
These signals need to be connected to several factors, including customer research, journey performance, content effectiveness, and commercial outcomes. Of course, the goal is not to pursue every emerging metric. Rather, it’s to build enough evidence to identify where the experience is working, where expectations are changing and which improvements will have the greatest impact.
The pressure surrounding AI can make every digital decision feel urgent. That creates a risk that organisations either rush into new technology or become paralysed by the apparent scale of change. A structured readiness assessment can provide a far more useful starting point.
For some AEM organisations, the priority will be around improving content quality, information architecture, or key customer journeys. Others may need to simplify governance, to equip teams to use AI responsibly, or simply to improve how content is reused and optimised. Some will identify architectural limitations that justify a wider platform strategy conversation.
There is no single correct destination because every enterprise begins with different customer needs, digital estates, and levels of maturity. What matters is recognising that the shift is well and truly underway.
AI is changing the context in which digital experiences are discovered and judged, regardless of whether an organisation has introduced its own AI features.
The organisations best prepared for that future will not necessarily be those that adopt the most tools. They will be those that understand how customer behaviour is changing, assess their existing experience honestly, and invest in the combination of content, UX, technology, and operating-model change that the evidence supports.
If you want to understand whether your current AEM experience is prepared for AI-driven changes in customer behaviour, we can help you assess your digital estate, identify where readiness gaps exist, and define a practical route forward.
Our structured AI Readiness Assessment is designed for organisations seeking an independent assessment of their digital experience, customer journeys, content strategy, GEO, operating model and overall platform readiness. The assessment will provide you with an evaluation of your content, customer journeys, information architecture, operating model, and platform readiness, together with clear recommendations and a prioritised roadmap for action.
Candyspace is a Quantiphi company. We combine award-winning, human-centred design and engineering with Quantiphi's global leadership in generative, conversational and agentic AI. We work across the leading enterprise platforms Optimizely, Contentful, StoryBlok and Commercetools and are trusted to deliver by some of the world's best-known brands, including ITV, Rolls-Royce, Mazda, The Royal Mint, Samsung, MS Now, Mars, and Interflora.
Together, Candyspace and Quantiphi help organisations create digital platforms that are technically robust, commercially focused and capable of supporting the next generation of intelligent, adaptive customer experiences.
Questioning whether AEM is still the right fit for your organisation? Get in touch with our team to discuss your options.