Experience Thinking and AI Strategy: Designing Beyond Technology

AI is a popular topic right now. There are many talks, many tools, and many opinions about where it is going and what it means. I am adding to that conversation today, but I want to do it from a specific perspective. 

What I want to talk about is AI strategy through the lens of Experience Thinking. Not AI as a technology on its own, but AI as something that shapes experiences across organizations. 

My background is not in design in the traditional sense. I come from cognitive psychology and research, and much of my work has been focused on supporting designers and teams by helping them understand how people think, learn, and interact with systems. That perspective has become increasingly relevant as AI has evolved, particularly because many AI systems are based on models that mimic human cognition. 

Experience Thinking emerged for me out of a frustration with how narrowly experience is often defined. Too often, the experience is treated as a screen or an interface. Experiences are much broader than that. 

Experience is Broader than a Single Interaction 

When we talk about experience, we are talking about all the ways people interact with an organization. That includes products, services, environments, content, events, communications, and systems. It includes how people buy from an organization, work for an organization, or interact with it digitally. 

Organizations are usually structured around what I think of as experience points or islands. Marketing owns one part of the experience. Product teams own another. Engineering, communications, and service teams all contribute their own pieces. Individually, those pieces may work reasonably well, but they are often not well connected. 

People on the outside notice that immediately. They notice when experiences do not flow. They notice when moving from one touchpoint to another feels disjointed. Experience design is not just about making individual moments work well, but about designing the connections between them. 

This is why experience design has increasingly focused on journeys and flows over time. Thinking in time helps make sense of complexity. It allows us to see how multiple products, services, and interactions work together across longer relationships, not just single moments. 

Limits of Current Experience Design Processes

There are many established design processes that teams use today. Double Diamond, Design Thinking, Human-Centered Design, and User-Centered Design all aim to address experiential challenges. 

While they differ in structure, they share three core elements. First, they emphasize understanding people through research, tasks, and journeys. Second, they rely on iteration, prototyping, and learning rather than getting things right the first time. Third, they depend on empirical testing with real people to validate whether something works. 

These principles are sound, but they are also very generic. They work reasonably well when designing single products and contained systems. These processes struggle when experiences span multiple products and services that include brand, content, products, and services. We are in a world where many offerings are more complex, do involve multiple products and need an end-to-end service solution to meet the needs our our customers.  

That gap becomes even more visible when AI enters the picture. 

Experience Thinking as a Framework

Experience Thinking is an attempt to address that gap and create connections. It breaks experience into four key elements that are always present and always interacting: brand experience, content experience, product experience, and service experience. 

These elements are not sequential steps. They are distinct areas that require different ways of thinking and designing, but they must work together in a coherent way. 

This is particularly important for AI strategy because AI does not live in just one of these areas. It touches all of them. 

AI is already Shaping your Brand

Brand is rarely discussed when organizations talk about AI, yet AI is already influencing how brands are experienced. Every interaction with an AI system carries tone, personality, and emotional impact. Whether that interaction feels warm or cold, helpful or frustrating, intentional or awkward, it affects how people perceive the organization behind it. 

Right now, many of these decisions are framed as technical adjustments rather than brand decisions. Changes in how an AI responds or sounds are treated as system improvements, even though they directly influence trust and comfort. There is very little deliberate discussion about whether AI should have its own identity, whether it should align closely with the organizational brand, or how it should express that brand consistently.

That conversation needs to happen, because AI is already part of the brand experience. 

AI pushes Content to Centre Stage 

Content has long been treated as something that sits alongside products and services. It explains, supports, or markets what already exists. In practice, content often is the experience, but it has not always been treated that way. 

Generative AI makes this impossible to ignore. AI systems create content continuously and adapt it to different people and situations. This raises questions around personalization, ethics, trust, and appropriateness, all of which are rooted in content decisions rather than technology alone. Multimodal AI only amplifies this, as text, images, voice, and movement blend into a single experience. 

As AI becomes more embedded, content strategy can no longer be an afterthought. It becomes a core part of how experiences are designed and understood. 

Designing AI Experiences around Prompts 

Most current AI implementations focus on product experience, and in particular on conversational interfaces. Prompts have become the dominant way people interact with AI systems. 

While prompts can be useful, they also reveal a deeper issue. Users are spending significant time learning how to phrase questions, rewriting prompts, and saving prompts that work. This keeps the burden of understanding the intent, knowing the results and goals with the user. It feels like early search experiences, where success depended on knowing how to ask rather than on the system understanding and truly supporting intent. 

A more experience-driven approach starts earlier. It begins by understanding what people need to know, change or decide. User research helps identify those core questions. From there, the product experience can be designed to support those needs directly. An AI prompt might be part of the design solution, but it should not be assumed to be the primary interface. 

Service Design: where AI could Leap ahead

Service experience is what connects interactions over time. It shapes how people move from one moment to the next and how relationships with organizations develop. 

At the moment, AI plays a limited role in service experiences. Some early examples exist, such as better routing in customer support or emerging companion-style experiences in healthcare. These hint at what is possible, but they are still the exception. 

The real opportunity lies in using AI to guide people proactively, helping them navigate complex systems, and connecting experience islands into something more coherent. As more teams think in journeys and flows over time, service design will likely become one of the most important areas for AI strategy. 

Shifting the Focus from Technology to Experience 

AI strategy often struggles because organizations focus first on the technology and then try to justify it through business cases. My belief is that this approach needs to be reversed. 

If we focus on creating better experiences, the AI value will follow. The business case becomes clearer when experiences are intentional, connected, and meaningful. 

Experience Thinking offers a way to structure that work. It helps teams move beyond seeing AI as a tool and instead see it as part of a larger experience ecosystem. 

The real question is not where we can add AI. The question is what experience we are trying to create, and where AI can support that intended experience. Let’s get started.