How Interaction Modes Connect the System

Interaction Modes Enhance Context

Most design systems describe what’s visible — components, layouts, user types, or tasks.Interaction Modes describe what’s happening in context. They enhance every other framework — giving User Types emotional dimension, giving Jobs To Be Done (JTBD) behavioral expression, giving Information Architecture adaptive logic, and giving AI contextual awareness.

In short: Interaction Modes are context enhancers. They bring systems to life by making them aware of how people are thinking, feeling, and acting right now.

AI and Interaction Modes

As AI becomes more deeply integrated into the design system, Interaction Modes act as the connective layer that gives it contextual intelligence. Traditional AI models are great at generating or predicting content, but without a clear understanding of why a user is doing something or how they’re thinking in the moment, those outputs can feel generic or misplaced. Interaction Modes solve this by giving AI a behavioral lens — a way to interpret intent, emotion, and cognitive state.

• When the system knows that a user is in Analyzing mode, it can prioritize clarity, data visualization, and comparison tools.
• When the user shifts to Decision-Making, the AI can summarize key trade-offs, present pros and cons, or generate recommendations.
• In Reviewing, it can surface anomalies or summaries that reduce cognitive load. The same underlying dataset becomes situationally aware, adapting presentation and tone based on behavioral context.

Over time, this unlocks a richer form of intelligence across the product ecosystem. Instead of a single, static interface, the experience becomes adaptive — flexing its layout, hierarchy, and content to match user mindset. Designers define the intent; AI interprets and responds. Interaction Modes provide the metadata bridge that lets AI reason about what’s happening and adjust accordingly.

In this sense, Interaction Modes don’t just enhance human understanding — they enhance machine understanding too. They turn abstract design logic into structured signals AI can use to generate, recommend, or personalize responsibly. It’s how the system learns not just to see users, but to understand their state of mind.

User Types, UX Personas, Jobs To Be Done and Interaction Modes

User Types and UX Personas

User Types & UX Personas tell us who the user is and the nature of their role and environment. Interaction modes are universal in nature—they describe patterns of behavior and mindset that cut across all user types, regardless of role or domain.

• For example, both a worker submitting expenses and a manager approving them engage in reviewing, but the manager may spend far more time in that mode than the worker.
• Similarly, configuring might appear occasionally for an employee adjusting personal settings, but for an administrator it can dominate their day-to-day.
• Even shared modes, like finding, can feel different in intensity: a recruiter might constantly search for candidates (high frequency, high stakes), while an employee might only use it occasionally to locate a payslip.

By recognizing that all users draw from the same universal set of modes—while some over-index on certain ones—we can design systems that adapt to both the common ground and the unique emphases of different user types.

By recognizing that all users draw from the same universal set of modes—while some over-index on certain ones—we can design systems that adapt to both the common ground and the unique emphases of different user types.

Jobs To Be Done and Interaction Modes

Jobs to Be Done (JTBD) describes the why behind user behavior — the underlying goal or outcome a person is trying to achieve. Interaction Modes describe the how — the mindset, behaviors, and cognitive patterns users adopt as they pursue that goal. When paired, they create a powerful dual lens for understanding intent and designing with purpose. JTBD tells us what success looks like from the user’s perspective, while Interaction Modes reveal how that pursuit unfolds moment to moment.

For instance, a job like “evaluate team performance” may include modes such as Reviewing, Analyzing, and Decision-Making. Recognizing those shifts helps designers move beyond static task flows toward experiences that respond to the user’s mental state — emphasizing clarity during analysis, summarization during review, and confidence at the point of decision. Together, JTBD and Interaction Modes bridge motivation and behavior, turning abstract intent into actionable design logic.

Critical User Journeys and Interaction Modes

Critical user journeys represent end-to-end flows across the product. Within those journeys, Interaction Modes can highlight the behavioral shifts user make and help us to identify the right patterns at the right time. This can help lead to targeted improvement to patterns, layouts and overall experiences at scale.

1. Linear Journey

The most straightforward journey. Example: ‘Requesting time off’ may begin with Finding / Navigating, move into Executing, and finish in Reviewing before Completing. This mapping helps us see journeys as patterns of modes, not just a sequence of screens.

Use cases: Simple HR tasks: Submit expense, Request time off, Complete CPCI, etc.

2. Cyclical Loop

Continuous oversight; no final “Completing,” just perpetual adjustment. A cyclical CUJ (e.g., performance management) may cycle repeatedly between Monitoring, Analyzing, Executing / Configuring and back to Monitoring.

Use cases: Workforce planning dashboards, performance tracking etc.

Information Architecture (IA) and Interaction Modes

Information Architecture (IA) defines the structure and navigation of product content (L0–L4). Interaction Modes complement IA by describing the behavioral lens applied to that structure. IA tells us where content lives (e.g., Personal → Benefits and Pay). Interaction Modes tell us how the user is engaging (e.g., Analyzing their payslip vs Finding it quickly). Together, IA and Interaction Modes provide a dual-tagging system that enables intelligent, intent-aware experiences.