A product executive slides open her desk drawer. Tucked between old cables and outdated business cards is a thick, glossy report. The binding is pristine, the typography immaculate, the insights meticulously crafted. Six figures well spent, at least according to the invoice. Dust motes catch the light as she lifts it out: a monument to research that shaped… nothing, influenced… no one, and expired the day it was delivered. 

It’s every researcher’s quiet fear. The initiative they poured months of work, a chunk of their sanity, and about a thousand sticky notes into becomes shelf-ware. Just another artifact joining strategy decks and persona posters that never found their way into real decisions. 

This is the way research has been delivered for decades, by global consultancies, boutique agencies, and yes, even by me. At $150K a report, it sounds extravagant. But when you consider the sheer effort, the rarity of the talent involved, and the stakes of anchoring business decisions in real customer insight, it’s not hard to see why leaders sign the check. 

The issue isn’t the value of the research. It’s the belief that insights should live in documents at all. 

Research as a Living System 

Now picture a different moment. The same executive doesn’t reach for a drawer. She opens her laptop and types: “What causes the most friction when ordering internationally?” 

Within seconds she’s reviewing tagged quotes from dozens of interviews, seeing patterns of friction emerge, even testing new messaging against synthesized persona responses. The research isn’t locked in a PDF. It’s alive, queryable, and in motion. 

This isn’t a fantasy. It’s the natural evolution of how research should work: not as one-time deliverables, but as a living system. 

The numbers show why change is overdue. Eighty percent of Research Ops & UX professionals use some form of research repository, but over half reported fair or poor adoption. The tools are frustrating, time consuming to maintain, and lack ownership. Instead of mining the insights they already have, teams commission new studies, resulting in an expensive cycle of creating artifacts that sit idle, while decisions move on without them. 

It’s a Usability Problem 

Research hasn’t failed because of weak insights. It’s been constrained by the static format of reports. Once findings are bound in a PDF or slide deck, the deliverable has to serve multiple audiences at once, and it starts to bend under its own weight. 

For executives, the executive summary provides a clean snapshot of findings. But when the time comes to make a concrete decision, the summary isn’t enough. They have to dive into the hundred-page appendix to trace back the evidence, which slows down the moment of action. 

On the other hand, product teams don’t need summaries, they need detailed insights for the feature they’re building right now. In long static reports, those details are often buried or disconnected from their workflow. Sometimes they don’t even realize the answer exists at all, so the research goes unused, or even gets repeated. An insight that can’t be surfaced when it’s needed might as well not exist. 

The constraint isn’t the quality of the research. It’s the format. Static deliverables fracture usability across audiences and leave each group working harder than they should to put insights into play. 

Research as a Product 

While we usually view research as an input into products, research itself is a product too. And with a product mindset, there is no “final deliverable,” only an evolving body of user knowledge that grows in value over time. 

In this model, the researcher acts as a knowledge steward of the user insight “product,” curating, refining, and continuously delivering customer insights to their users: the executives, product managers, designers, and engineers who need insights in different forms and at different moments. 

Like any product, research needs a roadmap. It has gaps to fill, like user groups not yet heard from, or behaviors not yet explored. It has features to maintain like transcripts, coded data, and tagged insights. And it has adoption goals, because insights only create value when people use them. 

This approach transforms reports too. A static deck becomes just a temporary framing of the knowledge that already exists in the system. With AI, you can auto-generate the right “version” of research for the right audience, such as an executive summary for the C-suite, annotations on backlog items for product teams, or a user-centered evaluation for design reviews. 

Treating research as a product also opens the door to continuous improvement. A research backlog can track unanswered questions, emerging themes, and opportunities for deeper exploration. Researchers can measure not just delivery (“did we produce quality insights?”) but usage (“did the insights influence a decision?”). Over time, the research “product” compounds in value, becoming a living, evolving system rather than a series of static outputs. 

This new model requires a new generation of tools. AI can now cluster themes, surface patterns, simulate persona responses, and expose insights through natural Q&A. AI makes the recomposition of insights into deliverables cheap. That allows us to focus on how our users get the insights they need in the way they need them. 

From Deliverable to Product 

Treating research as a product changes the central question. It’s no longer, “What should this report contain?” but “What questions might stakeholders need to answer, and how do we make those answers immediately accessible?” 

When research is built for inquiry, every transcript, survey, and usability session becomes part of a living knowledge base that compounds in value over time. Success shifts too: not in the number of reports delivered, but in how often insights are pulled into decisions. A six-figure investment should inform hundreds of critical choices, not one presentation that fades into archives. 

And here’s the irony: the product mindset actually produces better reports as well. When purpose-built reports focus as much on their usage as the information they contain, they become invaluable components of the software production machine. 

Research itself isn’t broken. It just needs a product mindset and AI-based qualitative analysis tools that turns insights into a living system, not a slide deck. 

Next in the series, we look at two more shifts: AI removing the depth vs. breadth constraint, and the rise of agents as research participants.

The pace of AI change can feel relentless with tools, processes, and practices evolving almost weekly. We help organizations navigate this landscape with clarity, balancing experimentation with governance, and turning AI’s potential into practical, measurable outcomes. If you’re looking to explore how AI can work inside your organization—not just in theory, but in practice—we’d love to be a partner in that journey. Request a strategy session.  

Key Takeaways

• Traditional research deliverables, like lengthy reports and slide decks, often expire the moment they are delivered, leaving insights unused.
• The problem is not weak research but static formats that fracture usability across executives, product teams, and designers.
• Treating research as a product reframes it as a living system: evolving, queryable, and compounding in value over time.
• With a product mindset, researchers become knowledge stewards, curating and delivering insights in forms tailored to each audience.
• AI enables this shift by clustering themes, surfacing patterns, and recomposing deliverables dynamically, making insights immediately accessible.

FAQs

What is the problem with traditional research reports?
Traditional reports often serve as static artifacts. Once published, they struggle to meet the needs of multiple audiences and quickly become outdated, limiting their impact on real decisions.

Why is research often underutilized in organizations?
Research is underutilized because its insights are locked in formats like PDFs or decks. Executives, product teams, and designers often cannot access the right detail at the right time, so findings go unused or studies are repeated.

What does it mean to treat research as a product?
Treating research as a product means building a continuously evolving knowledge base rather than one-time deliverables. Insights are curated, updated, and delivered in forms that align with the needs of different stakeholders.

How does AI support this new model?
AI makes it possible to cluster themes, surface weak signals, and generate audience-specific deliverables on demand. This reduces maintenance overhead and ensures insights are always accessible when needed.

What role do researchers play in this model?
Researchers become knowledge stewards, ensuring the insight “product” is accurate, relevant, and continuously improved. Their work shifts from producing final reports to curating and delivering insights that compound in value over time.

How does this benefit organizations?
Organizations gain faster, more confident decision-making. A six-figure research investment can inform hundreds of decisions, rather than fading after a single presentation.

Why Generative AI Requires Us to Rethink the Foundations of User-Centered Design 

User-centered design has long been our north star—grounded in research, journey mapping, and interfaces built around stable, observable tasks. It has been methodical, human-centered, and incredibly effective—until now. 

LLM-based Generative AI and Agentic Experiences, have upended this entire paradigm. These technologies don’t follow predefined scripts. Their interfaces aren’t fixed, their user journeys can’t be mapped, and their purpose unfolds as interaction happens. The experience doesn’t precede the user—it emerges from the LLM’s interaction with the user. 

This shift demands a new design framework—one that embraces unpredictability and builds adaptive systems capable of responding to fluid goals. One that doesn’t deliver rigid interfaces, but scaffolds flexible environments for creativity, productivity, and collaboration. At Robots & Pencils, we call this approach Emergent Experience Design. 

The Limits of Task-Based UX 

Traditional UX design starts with research that discovers jobs to be done. We uncover user goals, design supporting interfaces, and optimize them for clarity and speed. When the job is known and stable, this approach excels.  

But LLM-based systems like ChatGPT aren’t built for one job. They serve any purpose that can be expressed in language at run-time. The interface isn’t static. It adapts in real time. And the “job” often isn’t clear until the user acts. 

If the experience is emergent, our designs need to be as well. 

Emergent Experience Design: A UX Framework for Generative AI 

Emergent Experience Design is a conceptual design framework for building systems that stay flexible without losing focus. These systems don’t follow scripts—they respond. 

• Adapt to user goals in real time 

• Respond intelligently to unpredictable behavior 

• Stay aligned to intended outcomes without relying on rigid structures 

To do that, they’re built on three types of components: 

1. Open Worlds 

Open worlds are digital environments intentionally designed to invite exploration, expression, and improvisation. Unlike traditional interfaces that guide users down linear paths, open worlds provide open-ended sandboxes for users to work freely—adapting to user behavior, not constraining it. They empower users to bring their own goals, define their own workflows, and even invent new use cases that a designer could never anticipate. 

To define these worlds, we begin by choosing the physical or virtual space—a watch, a phone, a desktop computer, or even smart glasses. Then, we can choose one or more interaction design metaphors for that space—a 3D world, a spreadsheet grid, a voice interface, etc. A design vocabulary then defines what elements can exist within that world—from atomic design elements like buttons, widgets, cells, images, or custom inputs, to more expressive functionality like drag-and-drop layouts, formula editors, or a dialogue system. 

Finally, open worlds are governed by a set of rules that control how objects interact. These can be strict (like physics constraints or permission layers) or soft (like design affordances and layout behaviors), but they give the world its internal logic. The more elemental and expressive the vocabulary and rules are, the more varied and creative the user behavior becomes. 

Different environments will necessitate different component vocabularies—what elements can be placed, modified, or triggered within the world. By exposing this vocabulary via a structured interface protocol (similar to Model-Context-Protocol, or MCP), LLM agents can purpose-build new interfaces in the world responsively based on the medium. A smartwatch might expose a limited set of compact controls, a desktop app might expose modal overlays, windows or toolbars, and a terminal interface might offer only text-based interactions. Yet from the agent’s perspective, these are just different dialects of the same design language—enabling the same user goal to be rendered differently across modalities. 

Open worlds don’t prescribe a journey—they provide a landscape. And when these environments are paired with agents, they evolve into living systems that scaffold emergent experiences rather than dictate static ones. 

2. Assistive Agents 

Assistive agents are the visible, intelligent entities that inhabit open worlds and respond to user behavior in real time. Powered by large language models or other generative systems, these agents act as collaborators—interpreting context, responding to inputs, and acting inside (and sometimes outside) the digital environment. Rather than relying on hardcoded flows or fixed logic, assistive agents adapt dynamically, crafting interactions based on historical patterns and real-time cues. 

Each assistive agent can be shaped by two key ingredients: 

• Instinct: The training and architecture of the underlying LLM model, which provides its foundational capabilities. This could include the ability to understand text or image inputs, the language in which it responds, and its underlying reasoning patterns. 

• Identity: The purpose and personality assigned through prompt instructions and contextual inputs that shape the agent’s perspective—what it knows, how it prioritizes information, and how it speaks or acts. 

These two ingredients work together to shape agent behavior: instinct governs what the model can do, while identity defines what it should do in a given context. Instinct is durable—coded in the model’s training and architecture—while identity is flexible, applied at runtime through prompts and context. This separation allows us to reuse the same foundation across wildly different roles and experiences, simply by redefining the agent’s identity. 

Agents can perceive a wide variety of inputs from typed prompts or voice commands to UI events and changes in application state—even external signals from APIs and sensors. Increasingly, these agents are also gaining access to formalized interfaces—structured protocols that define what actions can be taken in a system, and what components are available for composition. One emerging standard, the Model-Context-Protocol (MCP) pattern introduced by Anthropic, provides a glimpse of this future: an AI agent can query a system to discover its capabilities, understand the input schema for a given tool or interface, and generate the appropriate response. In the context of UI, this approach should also open the door to agents that can dynamically compose interfaces based on user intent and a declarative understanding of the available design language. 

Importantly, while designers shape an agent’s perception and capabilities, they don’t script exact outcomes. This allows the agent to remain flexible and resilient, and able to improvise intelligently in response to emergent user behavior. In this way, assistive agents move beyond simple automation and become adaptive collaborators inside the experience. 

The designer’s job is not to control every move the agent makes, but to equip it with the right inputs, mental models, and capabilities to succeed. 

3. Moderating Agents 

Moderating agents are the invisible orchestration layer of an emergent system. While assistive agents respond in real time to user input, moderating agents maintain focus on long-term goals. They ensure that the emergent experience remains aligned with desired outcomes like user satisfaction, data completeness, business objectives, and safety constraints. 

These agents function by constantly evaluating the state of the world: the current conversation, the user’s actions, the trajectory of the interaction, and any external signals or thresholds. They compare that state to a defined ideal or target condition, and when gaps appear, they nudge the system toward correction. This could take the form of suggesting a follow-up question to an assistant, prompting clarification, or halting actions that risk ethical violations or user dissatisfaction. 

Moderating agents are not rule-based validators. They are adaptive, context-aware entities that operate with soft influence rather than hard enforcement. They may use scoring systems, natural language evaluations, or AI-generated reasoning to assess how well a system is performing against its goals. These agents often manifest through lightweight interventions—such as adjusting the context window of an assistive agent, inserting clarifying background information, reframing a prompt, or suggesting a next step. In some cases, they may even take subtle, direct actions in the environment—but always in ways that feel like a nudge rather than a command. This balance allows moderating agents to shape behavior without disrupting the open-ended, user-driven nature of the experience. 

Designers configure moderating agents through clear articulation of intent. This can include writing prompts that define goals, thresholds for action, and strategies for response. These prompts serve as the conscience of the experience—guiding assistants subtly and meaningfully, especially in open-ended contexts where ambiguity is the norm. 

Moderating agents are how we bring intentionality into systems that we don’t fully control. They make emergent experiences accountable, responsible, and productive without sacrificing their openness or creativity. 

From Intent to Interface: The Role of Protocols 

The promise of Emergent Experience Design doesn’t stop at agent behavior—it extends to how the experience itself is constructed. If we treat user goals as structured intent and treat our UI vocabulary as a query-able language, then the interface becomes the result of a real-time negotiation between those two forces. 

This is where the concept of Model-Context-Protocol becomes especially relevant. Originally defined as a mechanism for AI agents to discover and interact with external tools, MCP also offers a compelling lens for interface design. Imagine every environment—from mobile phones to smartwatches to voice UIs—offering a structured “design language” via an MCP server. Agents could then query that server to discover what UI components are supported, how they behave, and how they can be composed. 

A single requirement—say, “allow user to log in”—could be expressed through entirely different interfaces across devices, yet generated from the same underlying intent. The system adapts not by guessing what to show, but by asking what’s possible, and then composing the interface from the capabilities exposed. This transforms the role of design systems from static libraries to living protocols, and makes real-time, device-aware interface generation not just feasible, but scalable. 

A Mindset Shift for Designers 

In this new paradigm, interfaces are no longer fixed blueprints. They are assembled at runtime based on emerging needs. Outcomes are not guaranteed—they are negotiated through interaction. And user journeys are not mapped—they are discovered as they unfold. This dynamic, improvisational structure demands a design framework that embraces fluidity without abandoning intention. 

As designers, we have to move from architects of static interfaces to cultivators of digital ecosystems. Emergent Experience Design is the framework that lets us shape the tools and environments where humans co-create with intelligent assistants. Instead of predicting behavior, we guide it. Instead of controlling the path, we shape the world. 

Why It Matters 

Traditional UX assumes we can observe and anticipate user goals, define the right interface, and guide people efficiently from point A to B. That worked—until GenAI changed the rules. 

In agentic systems, intent is fluid. Interfaces are built on the fly. Outcomes aren’t hard-coded—they unfold in the moment. That makes our current design models brittle. They break under uncertainty. 

Emergent Experience Design gives us a new toolkit. It helps us move from building interfaces for predefined jobs to crafting systems that automate discovery, collaboration, and adaptation in real time. 

With this framework, we can: 

• Meet users where they are—not where we expect them to be 

• Guide them through complex systems with responsive, context-aware support 

• Preserve creativity, flexibility, and human agency at every step 

In short: it lets us design with the user, not just for them. And in doing so, it unlocks entirely new categories of experience—ones too dynamic to script, and too valuable to ignore.