Digital touchpoints in the AI era

Customer journeys used to be so simple. Years ago, you might have seen a billboard advertisement on your way to work, heard a radio spot, or seen a product through a shop window. You'd walk into a store, find the product, pay for it, and head home.

Then television entered the game. Brands competed for potential customers' attention during prime-time hours and the journey often began in the commercial break.

Next came the internet and new digital touchpoints: websites, email, search engines, and social media. The digital customer journey became more complex, but was still relatively predictable. 

Marketers got good at mapping and managing these interactions, optimizing for search, running tests, and fine-tuning every experience to boost engagement and customer satisfaction. 

However, the digital landscape is shifting again. Large language models (LLMs), generative AI (GenAI), and agentic answer engines are changing the way that customers find, and interact with, brands. As marketers, we need to make sure that our brands, and our content, are ready.

What are digital touchpoints?

Simply put, touchpoints, or more specifically marketing touchpoints, are the points at which customers and brands interact. 

Some touchpoints are physical: A retail store, for example, is a touchpoint. A stall at an industry event is a touchpoint. Newspapers, brochures, and billboards can be touchpoints. Even product packaging can be one of the most effective forms of marketing. 

As you might have guessed, digital touchpoints move that interaction online. Digital touchpoint examples include websites, emails, social media pages, mobile apps, in-store kiosks, embedded page banners, search engine results pages (SERPs), and so on. 

Supporting digital experiences

Customer interactions at digital touchpoints constitute the “digital experience,” and so they need to fit their format. For example, web-page content should be built for the computer or laptop screen, and be navigable by a mouse or tracker pad. Mobile content should be optimized for smaller screens and touch navigation.

Those content experiences are how brands capture attention, and nudge potential customers down the acquisition funnel to the point they convert and make a purchase. SERPs are a huge part of that process: Content needs to be visible to Google, which means brands need to prioritize search engine optimization (SEO), and follow the relevant best practices for the search engine touchpoint. That means prioritizing keywords, metadata and anything else that could boost the content’s ranking and its visibility. 

Touchpoint challenges 

Orchestrating effective, engaging, SEO-friendly content experiences across multiple touchpoints in the customer journey is a significant challenge. 

The more touchpoints in an ecosystem, the more content needs to be created, managed, and optimized — all of which adds to the complexity of content workflows and the burden placed on content teams. 

If a brand’s content platform isn’t capable of that level of flexibility, problems follow. In some inflexible legacy content platforms, for example, brands might need to produce different versions of content for the separate touchpoints that they maintain, adjusting text and image specifications manually for each publication. 

That approach not only leads to an explosion of content, but often means extensive content duplication and human error as a result of repetitive copy-and-paste work. In that environment, experiences can quickly become fractured, inconsistent and inaccurate, while customers become confused and frustrated.  

Search engines also pick up on those problems, which leads to content dropping in SERPs, lower customer loyalty, and competitors picking up your missed opportunities.

Content platform solutions

Here's the good news: Those digital touchpoint challenges can be solved with the right kind of content platform — you can see how it’s done with Contentful.

In the Contentful digital experience platform (DXP), composable architecture and an API-first philosophy support structured content models, in which content is broken down to its component parts, and stored centrally. In a structured model, content is created once and then reused across the ecosystem, supporting any touchpoint or customer journey without introducing formatting errors or inconsistencies. 

For example, an image used in a web press release can be reused seamlessly in a subsequent blog post, or on a mobile app, a digital kiosk, or any other customer-facing experience. It’s the same image, used across touchpoints.

This changes the possibilities for creating and publishing content on different touchpoints. It frees non-technical teams from dependency on developers, and enables organizations to become more creative, agile, and responsive with their campaigns.

That flexibility has helped thousands of brands deliver digital experiences around the world.

And now the marketing landscape, and the customer journey, is changing again. Brands that have optimized their content ecosystems by leveraging composable architecture and structured models, must be ready to adapt to a new touchpoint: the LLM-powered answer engine.

Digital touchpoints in the Great Content Collapse

We talked about a "huge shift" in the digital landscape earlier; many marketers know it as the Great Content Collapse. The term describes the way AI-powered answer engines are reshaping how people discover information online.

Historically, customers researching a product might visit multiple websites, read blog posts, compare reviews, browse product pages, and gradually move toward a purchasing decision.

But in the age of agentic AI, the top-of-funnel (TOFU) and middle-of-funnel (MOFU) content experiences and touchpoints that used to take customers through a carefully designed journey aren’t guaranteed to attract and hold attention in the same way.

Answer engines, such as ChatGPT, Claude and Gemini, can effectively leapfrog those customer journey stages. They can give customers the information they're searching for in seconds, without them ever having to engage with the touchpoints that brands relied on in the past.

How is the customer journey changing?

Rather than opening an app, starting a Google search, or browsing a website, customers can now feed natural-language questions about a product to an AI agent. They can then refine their search through conversation, and receive answers that draw information from multiple sources.

Here's what that change looks like.

Imagine you're shopping for a new car. After some research, you've narrowed your options down to two vehicles: a Manufacturer A and Manufacturer B. Both are reputable, both fit your budget, and both meet your requirements. But you can't decide which one you want.

You could spend hours on each brand’s website, and on car industry websites, comparing specifications, ownership costs, and even collect customer feedback from review sites. 

Or you could go to an answer engine and simply ask: "Which car should I buy?"

This is where the "agentic" part comes into play. The answer engine can take that question and run with it, introducing factors you may not have considered, such as fuel economy, servicing costs, reliability, depreciation, and long-term ownership expenses. You can then discuss those factors with the AI to build a clearer picture of each vehicle and its relative strengths.

The point is that the AI agent can address the detail and nuance of your question more comprehensively than a SERP — which means visibility is no longer (just) about ranking in search. Brands need to make their content easy for AI systems to find, understand, trust, and use. 

Adapting to the AI touchpoint

Traditional digital touchpoints had clear starting points and destinations. The customer began their journey with a search query, arrived on a website, browsed content, and ideally ended on the checkout page.

AI-powered touchpoints are different because they're conversations: a back-and-forth between customer and agent that can continue throughout the decision-making process, right up until the moment the customer switches to a brand-controlled touchpoint to make a purchase.

That changes the role and function of content in the customer journey. In addition to fulfilling conventional SEO objectives, brand content needs to support the ongoing conversation at the AI touchpoint — which includes follow-up questions, suggestions for alternatives, overlooked details, and so on.

Making content for AI touchpoints

Websites, apps, search engines, and social media platforms aren't going away; you still need to maintain good SEO and think about how customers interact with content on websites and apps. But you should also start thinking about the AI touchpoints, and how AI agents will view your content. 

Strategies that worked for a traditional Google search bot may not work as effectively for an answer engine that can understand context, synthesize information, and respond to a much broader range of signals.

To cut the story short, to ensure your brand is capable of creating, publishing, and managing effective content across the full range of touchpoints that your customers use, you’ll need to leverage composability and build a structured content model. 

Here’s why. 

Content consistency

LLMs and AI agents prefer information that is organized, consistent, and reusable because it's inherently machine-readable. Structured content makes it easier for systems to identify information, understand its context, and use it accurately in their responses.

Structured content creates exactly those conditions. Organizations create an asset once and deploy it across an expanding ecosystem of touchpoints without duplicating effort or introducing inconsistencies.

And, as new touchpoints emerge, brands don't need to rebuild their content strategy from scratch. They can extend existing content into new experiences while maintaining the consistency and accuracy that both customers and AI systems expect.

Context

A structured content model doesn't just protect the accuracy of content assets; it also preserves the relationships between them.

That means AI systems can quickly and easily understand how images relate to products, how products relate to features, how features relate to use cases, and how those use cases relate to different audiences.

Those relationships provide valuable context. Instead of simply retrieving information, AI agents can synthesize it, connect it to user intent, and provide richer, more comprehensive responses.

Personalization

As AI systems become more capable of understanding user preferences, customer expectations will also evolve. While website personalization is already a critical marketing requirement, users will increasingly expect answer-engine experiences to reflect their specific needs, priorities, and circumstances.

To meet that expectation, brands will still need to develop and curate personalized experiences in order to help AI agents surface relevant content for individual users at every stage of the customer journey.

Structured content provides the flexibility needed to do exactly that. Organizations can personalize messaging, adapt content for different audiences, and deliver more relevant experiences at scale.

Brands need to be ready

We can’t view touchpoints as isolated destinations on the customer journey anymore. They've become part of a persistent experience that stretches across multiple channels, devices, platforms. Customers expect that experience to adapt seamlessly as they move between them.

But that flexibility now needs to extend into the AI frontier — a landscape that’s still being explored and mapped. In fact, you could say, we're in an AI-era oil rush: No one knows exactly how AI agents will ultimately shape the new generation of customer experiences, but we’re racing to find out.

New platforms will emerge, customer behaviors will continue to shift, and the touchpoints that matter most may look very different in a few years' time. What we do know is that brands need to be ready to capitalize on the change.

That's where a digital experience platform like Contentful makes a critical difference. Contentful’s composable architecture and structured content ensure brands are always in control of their customer experiences, regardless of the touchpoints that support them.

More importantly, Contentful’s flexibility means that you’ll be able to build experiences that fit the touchpoints of the future, with content that can be found, understood, and trusted by both your audiences and the AI agents that they use to find you.

Build better content for every touchpoint in your ecosystem: Get in touch with the Contentful team to get started.

Take a tour

Chat with our team

Your experiences should wow, not wait. Let's talk.