AI isn’t just reshaping the digital products we design—it’s also changing how we design them. And in doing so, it compels us to reconsider who designs them.

This pivotal shift overlaps with—and is increasingly tied to—a broader evolution in digital product development: the transition from UX Writing to Content Design. While this transformation began well before the current AI revolution, the emergence of generative AI has dramatically accelerated both its pace and scope, cementing the role of Content Design as fundamental to shaping AI-powered digital experiences.

At the heart of this lies a profound change in how digital products are built and experienced: Interfaces are no longer limited to static paths and buttons; they are evolving into fluid, conversational systems with dynamic responses. As a result, the way we design them must also transform. And unlike traditional software, which is programmed through code, these AI systems are increasingly guided by natural language instructions—placing language, and those who know best how to craft it (hint: Content Designers), at the very center of the design process.

Breaking With Tradition

Traditional UX Writing emerged as a specialized discipline focused on creating clear, consistent interface copy—the words users see and interact with directly. In this model, writers typically receive interface mockups (in various stages of completion) and provide copy that fits predetermined spaces and interactions: titles, descriptions, labels, messages, form fields, and navigation elements. This approach assumes a level of predictability and control that serves us well in the current era of digital experiences.

But this model carries inherent limitations that become increasingly apparent in the age of AI: When users can express themselves freely and systems can generate dynamic responses, the entire paradigm of writing fixed copy for predetermined scenarios breaks down.

A single user input might combine multiple intents, switch topics mid-conversation, or express needs in unexpected ways. The traditional approach of mapping out every possible user flow and writing specific copy for each scenario becomes not just impractical, but often impossible.

Moreover, the traditional model often treats content as a final layer of polish—something to be added once the “real” design work is complete. This approach might have been adequate when digital products were primarily about navigating through predetermined paths, but it falls short when dealing with AI systems that communicate in, react to, or generate natural language.

Evolving The Craft

Enter Content Design, a discipline that thinks systematically about information and interaction through language. Rather than focusing solely on the words that appear in an interface, Content Design considers deeper questions: How does information flow? How is meaning constructed? How do conversations naturally unfold? How do users build mental models of complex systems?

This evolution represents more than just an expansion of responsibilities. It marks a fundamental shift in how we approach the creation of digital experiences. Content Designers working with AI systems must create frameworks that guide behavior across an infinite range of potential interactions. Don’t just think of this as writing responses—Content Designers must design how conversations evolve, how context flows from one moment to the next, and how systems maintain coherence throughout complex interactions.

Consider a traditional chatbot based on templated responses. These have been around forever, and UX Writers were involved in crafting clear messages for predetermined scenarios. But in an AI-powered system, Content Designers must think about how the system maintains context across complex, non-linear conversations, when and how it should ask for clarification, how it handles ambiguous or unexpected inputs, how it transitions between different types of interactions, how it maintains consistency while allowing for natural variation, and how it recovers from misunderstandings or errors. This is how we move from UX Writers defining copy to Content Designers defining the experience.

The Language of Systems

Now, here’s a valid question: Why isn’t this simply part of the role of Product Designers? Well, it increasingly is, just like how writing (good enough) interface copy has often been a reality for Product Designers. But while many responsibilities overlap—and I don’t want to draw a strict line—the point is about embracing how Content Designers bring specialized expertise in language, information architecture, and conversation design that’s essential for creating AI-powered experiences. More importantly, Content Designers work in the medium that increasingly controls AI systems themselves: natural language.

Unlike traditional software, which is programmed through code, AI systems are increasingly guided through natural language instructions—prompts that shape how they think, behave, and interact. This transforms Content Design from a discipline that documents system behavior to one that actively directs it. When crafting these instructions, Content Designers aren’t just writing about what the system does—they’re defining its fundamental behavior patterns, decision-making frameworks, and interaction models.

Content Designers working with AI systems must consider three core aspects of system behavior:

• Interaction patterns
  How the system engages with users across different contexts and scenarios
• Decision frameworks
  The principles and guidelines that inform how the system processes and responds to input
• Behavioral consistency
  How to maintain coherent system behavior while allowing for natural variation in responses

This shift has profound implications for how we think about system architecture. When working with AI systems, language choices ripple through the entire experience in ways that weren’t true for traditional interfaces. The words and phrases used in training and guiding AI systems shape not just how they communicate, but how they interpret and process user input. This makes language choices a fundamental architectural concern.

The Architecture of Trust

Perhaps most crucially, Content Designers working with AI systems become architects of trust.

As AI takes on more complex and consequential interactions in our daily lives, how can we ensure users understand and trust these systems appropriately? This challenge becomes particularly critical as AI systems handle increasingly sensitive tasks and increasingly make decisions on users’ behalf.

The trust challenge operates on multiple levels:

Providing Transparency

How do we help users understand what they’re interacting with? This requires careful consideration of when and how to acknowledge a system’s AI nature, how to set appropriate expectations, and how to maintain transparency throughout the experience. Too little transparency can lead to misplaced trust, while too much technical detail can overwhelm users or undermine their confidence.

Handling (Un)certainty

AI systems regularly encounter situations where they can’t be certain about the best response or where user input could have multiple valid interpretations. How these moments of uncertainty are handled often determines whether users continue to trust and engage with the system. Content Designers need to develop patterns for acknowledging uncertainty that maintain user confidence while ensuring accuracy and honesty.

Key principles for handling uncertainty include:

• Transparent communication
  Clearly acknowledging when the system is unsure
• Graceful recovery
  Providing clear paths forward when misunderstandings occur
• Appropriate confidence
  Matching the system’s expressed certainty to its actual confidence level

Creating Coherence

Trust develops through consistent behavior over time, but maintaining consistency in AI systems presents unique challenges. Unlike traditional interfaces where every interaction can be precisely scripted, AI systems need to maintain consistent behavior across an almost infinite range of possible interactions. This requires creating frameworks that ensure consistent personality, tone, and behavior while allowing for natural variation in responses.

Practical Implications

This evolution demands fundamental changes in how organizations structure their teams and processes. The traditional approach of treating content as a final layer of polish simply doesn’t work for AI products. Content expertise needs to be present from the earliest stages of product conception, influencing architectural decisions, system design discussions, and the fundamental shaping of AI behavior.

Organizations must reimagine their development processes, moving from sequential handoffs to more collaborative, iterative approaches. Success in this transformation requires focus on three key areas:

New Collaboration Models

The integration of Content Design into AI product development demands a fundamental rethinking of how teams work together. Traditional handoff processes, where designs move sequentially from one discipline to another, must give way to more fluid, collaborative approaches that recognize the interconnected nature of content and system behavior in AI products.

This transformation begins with joint design sessions that bring together Content Designers, Product Designers, and engineers from the earliest stages of product development. Rather than treating these as separate disciplines with distinct handoffs, successful teams create integrated workflows where decisions about interface design, content strategy, and technical implementation inform each other continuously.

New Skill Sets

This also requires an evolution of skills which goes beyond learning new tools or technologies. Content Designers need to develop new ways of thinking about language and interaction, build deep understanding of conversation design principles, and learn how to shape AI behavior through language.

Core competencies for modern Content Designers include:

• AI Literacy
  Understanding capabilities, limitations, and behavioral patterns
• Conversation Design
  Expertise in natural dialogue flow and context management
• Systematic thinking
  Ability to create scalable frameworks for consistent behavior
• Behavioral psychology
  Understanding how users interpret and respond to AI interactions

New Tools and Methodologies

The rapid evolution of AI technology is already driving the emergence of new tools and methodologies for all roles involved, especially for Product Designers, UX Researchers and Developers. I don’t believe Content Designers have yet seen the same rapid evolution of their tools, but it is only a matter of time. New tools will emerge to allow teams to evaluate the nuanced aspects of how content shapes AI interactions, from the natural flow of conversations to the system’s ability to maintain context across complex interactions.

Prompt management will be a crucial focus area, allowing versioning and tracking the effectiveness of different prompts. These tools need to reflect the reality that prompts are fundamental architectural components, and allow teams to understand how subtle changes in language affect system behavior and ultimately the quality of the experience.

This touches on another critically needed capability: Finding effective ways to measure and evaluate AI interaction quality. Traditional metrics like task completion rates and user satisfaction scores are already being supplemented with novel measurements that capture the unique characteristics of AI interactions, such as conversation naturalness, context preservation, the various definitions of helpfulness.

A Call to Action

To all Content Designers navigating this transformation: this is your moment.

The evolution from a craft that primarily writes content to one that fundamentally shapes experiences is underway, moving increasingly at the center of answering the profound question: How will we shape the future of human-AI interaction?

The answers will emerge through the work of Content Designers who embrace this expanded role and push the boundaries of what’s possible. The future of human-AI interaction depends not just on technological advancement, but on our ability to shape these technologies into experiences that are truly human-centered, trustworthy, and valuable.

This is more than an evolution in job titles and responsibilities—it’s a fundamental transformation in how we create digital experiences. As AI continues to reshape the digital landscape, Content Design will play an increasingly crucial role in ensuring these experiences serve human needs effectively and ethically.

The challenge before us is not just to adapt to this change, but to actively shape it, ensuring that as AI systems become more powerful, yet remain fundamentally human-centered in their design and impact.