Design Principles That Improve IA Patterns

Product teams approach information architecture backwards. They start with what they want to organise instead of how users think about what they need. The result? Navigation that makes perfect sense to the people who built it and absolutely none to the people who use it.

Spoiler: your users don’t care about your internal team structure.

Setting the Context

Information architecture is the skeleton of your product experience. Get it right, and users flow effortlessly toward their goals. Get it wrong, and even great features become invisible because nobody can find them.

The challenge is that IA problems are often invisible during development. Your team navigates effortlessly because they built the thing. The friction only becomes apparent when real users encounter your structure with fresh eyes and different mental models.

This matters more than ever because products have become increasingly complex. The days of simple, single-purpose applications are mostly behind us. Modern products (vibe-coded, too!) need to serve multiple user types, support varied workflows, and scale across expanding feature sets. Without solid IA foundations, complexity becomes… chaos.

The Development Context

Technical Considerations

IA isn’t just a design problem, it has significant technical implications. The structure you choose affects URL schemes, API design, caching strategies, and component architecture.

Product teams create beautiful navigation that requires nightmarish backend gymnastics to support, or they commit to URL structures that become constraints as the product evolves.

Think about IA early in technical planning. Ask questions like:

How will this structure scale as we add features?
What URL patterns support our IA while remaining SEO-friendly?
How does our navigation map to our data model?
Can our component architecture support different IA views for different user types?

The best IA emerges when design and engineering collaborate from the start, each informing and constraining the other productively.

Team Dynamics

IA work requires crossfunctional input in ways that many teams aren’t structured to provide. Designers see user experience, engineers see technical feasibility, product managers see feature roadmaps, and content strategists see information relationships.

Problems arise when one perspective dominates. Design-led IA might be beautiful but technically impractical. Engineering-led IA might be elegant architecturally but incomprehensible to users. PM-led IA might optimise for features being visible rather than findable.

Create space for genuine collaboration on IA decisions. This isn’t about consensus (this isn’t democracy…). Someone needs to own the final call, but that decision should be informed by all relevant perspectives.

“The most elegant information architecture I’ve ever worked with was created by a team that genuinely understood each other’s constraints. They found solutions none of them would have discovered alone.”

Implementation Approach

Best Practices

Start with user mental models: Before you design any structure, understand how your users think about the problem space. What language do they use? How do they categorise information in their own minds? User research here isn’t optional, it’s the foundation everything else builds on.

Test your assumptions early: Paper prototypes and card sorting exercises are cheap ways to validate IA approaches before you invest in building them. I’m consistently amazed at how often our “obvious” structures fail when tested with real users.

Design for growth: Today’s IA needs to accommodate tomorrow’s features. Leave room for expansion in your categories and navigation patterns. This doesn’t mean overengineering, it means avoiding structural decisions that will become constraints.

Create multiple wayfinding paths: Not everyone navigates the same way. Some users prefer hierarchical navigation, others use search, and still others follow task-based flows. Good IA supports all these approaches.

Maintain consistency: Whatever patterns you establish, apply them consistently. Users learn your IA vocabulary over time. Changing that vocabulary mid-experience creates friction and confusion.

Tooling and Process

Card sorting tools (open and closed) help you understand how users naturally group information. Services like Optimal Workshop or even simple Miro boards work well.

Tree testing validates whether your proposed structure is actually navigable. You can test hierarchies before investing in visual design.

Analytics tell you how users actually navigate your current product. Where do they get stuck? What paths do they take? What do they search for? This data is gold for IA improvement.

Prototype tools let you test IA changes quickly. Tools like Figma can create interactive navigation prototypes without engineering investment.

Documentation matters. Your IA decisions should be documented and accessible so future team members understand why things are structured as they are.

Scaling What Works

Growth Considerations

IA that works at one scale often breaks at another. A navigation pattern that handles ten features gracefully might become unusable at fifty features.

Plan for scale from the start:

Use progressive disclosure to manage complexity (show core options, hide advanced features)
Create consistent patterns for grouping related features
Build flexible navigation components that can expand without redesign
Consider how IA might need to adapt for different user segments

Watch for warning signs that your IA is struggling:

Increased support tickets about finding features
Search volume increasing relative to navigation
User complaints about complexity
New features getting poor adoption despite solving real problems

Maintaining Quality

IA quality degrades over time if left untended. New features get added wherever they fit, nomenclature drifts, and exceptions proliferate until the original structure is unrecognisable.

Treat IA as a living system that requires maintenance:

Regular audits to identify structural problems
Clear guidelines for how new features should be integrated
A designated owner responsible for IA health
Periodic user research to check that your structure still matches mental models

The teams maintaining great IA over time all share one thing: they treat structural decisions as seriously as feature decisions. Adding a new top-level navigation item isn’t a casual choice, it’s a significant product decision that deserves discussion.

Key Takeaways

Start IA work with user research, not internal assumptions, your org chart isn’t your navigation
Involve engineering early because IA decisions have significant technical implications
Test structures cheaply before building them using card sorting and tree testing
Design for scale from the start, but watch for warning signs that you’ve outgrown your structure
Maintain IA quality over time through audits, guidelines, and clear ownership

Next Steps

Look at your current product’s IA with fresh eyes. Ask yourself:

When did we last validate this structure with users?
Where do users struggle to find things?
What would break if we doubled our feature count?
Who owns IA decisions, and how are they made?

If you don’t have good answers to these questions, that’s your starting point. Even small IA improvements can have outsized effects on user experience.

Good information architecture is invisible, users achieve their goals without thinking about structure. That invisibility is the goal, and it’s worth the investment to achieve it.

Have questions or thoughts? Get in touch - I’d love to hear from you!