Imagine you’re building a house. It starts simple: one bedroom, one bath, and a kitchen. A straightforward enough layout, until you decide to add another bedroom and that study you’ve always wanted. You want hardwood floors, custom windows, and a Nordic-inspired interior. 

Better have a clear plan from the outset, because putting it all together piecemeal will surely land you in trouble. Without a proper plan, you may find yourself in a never-ending cycle of expensive workarounds and customizations. And the final product may fall well short– and be far more expensive – of what you originally envisioned. 

The same thing happens with information architecture (IA) in digital products. IA is how sections, buttons, names, content, and data are organized. If IA isn’t managed systematically, “debt” builds up. What you’re left with is an accumulated mess that makes the product more difficult (and expensive) to use and maintain.

How IA debt gradually accumulates

Product and design teams are constantly balancing user needs with business pressures. With tight deadlines and competing priorities, even the most thoughtful teams sometimes make quick decisions that seem harmless in the moment but accumulate over time.

When teams are focused on shipping features and meeting sprint goals, structural decisions often get made "as it happens," such as:

• Adding a new menu button without considering how it fits into the overall navigation strategy
• Reusing an existing page template even when it doesn't quite match the new content type
• Creating similar sections with different naming conventions across different parts of the product

Understandable and, at least at first, barely noticeable. But as the product grows and the user base expands, the mess starts to cause problems: things are hard to find, names are confusing, and the data doesn’t match.

Another ripple: IA is complicating tech debt in the era of AI

It was bad enough when messy information structure was mostly a human problem. But today, with AI embedded in so many products, the stakes are much higher. AI doesn’t just display your content – it interprets it, categorizes it, and acts on it. 

When messy IA becomes the flawed foundation that AI systems learn from, organizational problems become algorithmic ones. While traditional technical debt affects backend systems and code maintenance, IA debt now directly impacts AI performance.

Poor information structure teaches AI models to make the same organizational mistakes at scale. If the underlying structure is flawed, AI will inherit those flaws, and small issues can snowball into serious product failures.

Here’s a glimpse of what can go wrong:

1. Search delivers irrelevant or nonsensical results
   If your product’s categories, tags, and metadata are inconsistent, AI search models can’t make accurate matches. For example, a user searching for “wireless headphones” might see a mix of unrelated products; some because they’re incorrectly tagged as “headphones”; others because “wireless” appears in an unrelated description.
   
   
2. Chatbots misinterpret documents
   When help-center articles, product manuals, and legal policies aren’t clearly labeled or structured, an AI assistant might pull the wrong information. Imagine a user asking, “How do I reset my account password?” and the bot replying with a step from an outdated internal policy document instead of the latest instructions.
   
   
3. Recommendations feel random and unhelpful
   Recommendation engines rely on patterns in clean, organized data. If your content is mislabeled – say, a “children’s book” is stored in the same category as “business strategy” – AI might recommend completely irrelevant items, damaging trust and engagement.
   
   
4. AI training suffers long-term
   Poor IA can silently train AI models on bad data. This means even after you fix the structure, the AI might keep making the same errors until it’s retrained, costing more time and resources.

In short, messy IA now confuses both people and machines. Add to the mix the inherent problems of AI pilot sprawl, code overkill, and governance problems, and AI quickly becomes an amplifier not of human capability, but IA flaws inherent to many design systems. 

So what’s the way forward?

3 fundamentals to less IA debt

By addressing core issues now, you can begin to bring down IA debt over the longer term. The goal is to create a structure that’s both logical for users and scalable for the business.

1. Conduct a fearless audit of your current IA

IA debt likes to hide beneath areas of status quo. Its best growing conditions are convenience, code proliferation, and a general lack of closer scrutiny. Keep this in mind when you begin a full audit of your current setup, which should include:

• Page hierarchy – Find any critical pages that are too deep in the navigation. If the majority of users first look for their favorites, it shouldn’t take them three or four clicks to find them.
  
  
• Menus – Look for overlapping or redundant sections that overcomplicate the menu structure and inhibit easy navigation.
  
  
• Duplicates – Things may be technically distinct to you, the designer or engineer; but they may mean the same thing to the average user. For example, in one SaaS dashboard, “Reports” and “Analytics” might lead to almost identical pages.
  
  
• Unclear names – Are we asking users to split hairs in the UX decisions they make? For example, overly similar categories (“Award-Winning Dramas” vs “Critically Acclaimed Dramas”) can fragment content and put too much onus on the end user.
  
  
• “Miscellaneous” buckets – “Other” or “Misc” sections can be convenient catch-alls, but they often hide high-value features that users are looking for.
  
  

Example: Survey data and usage analytics reveal that most visitors to a hospital’s patient portal want to book an appointment. But the “Book an Appointment” option is buried under “Health Services → Outpatient → Scheduling.” Just by moving this option to the main dashboard, the hospital can significantly increase appointment bookings.

2. Decide what to fix first

The hospital example is a clear-cut, high-priority issue (the data proved that page hierarchy was preventing a lot of users from achieving success). But not all IA issues are equally urgent. Here’s how to prioritize the issues that have the biggest impact:

• Critical user blockers – Is the issue costing you customers or impacting revenue? Is it directly affecting operational costs? Think of the checkout flow that’s buried in the UI; you can likely prove, quantitatively, that this is a direct hit to revenue.
  
  
• High-friction areas – Where do analytics show users repeatedly attempting but failing to complete tasks? Look for patterns like high bounce rates on specific pages, excessive time spent navigating between sections, or frequent use of search when browsing should suffice. These can indicate structural barriers between users and their goals.
  
  
• Growth bottlenecks – What organizational choices limit your ability to scale content, features, or user segments? Consider whether your current structure can accommodate new product lines, international markets, or user types without creating confusion or requiring complete restructuring.

Example: IA food delivery app is looking to reduce its chat support volume. It turns out that many of its customers open support chats because they can’t find the “Track Delivery” feature, which is hidden inside “Order Details.” By moving it to the home screen, the delivery app saw a considerable reduction in order-related support chats.

3. Set clear rules

An IA audit and cleanup can pay immediate dividends. But the goal is to arrive at a repeatable framework of standards that can be sustained over time. Establish certain standards that will help you scale healthier IA in the long term, such as:

• Naming conventions – Standardize how you name sections and features. For instance, Google Drive consistently uses “Shared with Me” across all devices instead of mixing terms like “Shared” or “Incoming.”
  
  
• Page creation process – Define formal approval steps for adding new sections. This will act as a safeguard against the proliferation of pages, features, and functionality that contribute to IA debt.
  
  
• Data structure – Use consistent tagging and metadata so search and recommendations work properly.
  
  
• Regular reviews – Audit IA during every product release to catch creeping complexity.

Example: Apple’s App Store enforces strict rules for how categories are named and organized. New app types are tested under subcategories before being promoted to top-level, keeping the navigation predictable and clean.

Improving IA improves UX: real-world examples and tips

Netflix 

Netflix has a long history of refining one of its core differentiators: personalization. The problem? How to increase engagement for a growing catalog and even faster-growing user base? 

Fix: IA is at the core of this ongoing effort by Netflix. The company is constantly innovating more digestible, contextually relevant ways for users to better engage with its massive media catalog. The platform creates what amounts to a unique IA for every viewer, from which content appears, to hear it’s labeled, grouped and prioritized. In A Brief History of Netflix Personalization, for Netflix executive Gibson Biddle explores some of the more notable efforts, from the “percentage match” system to the “do you feel lucky” feature. 

Impact: According to Biddle, 80% of what members watch on Netflix is driven by the company’s personalization engine.

Amazon 

For years, Amazon kept adding new sections to its top navigation until it became overcrowded, and important features (like Prime Video) got lost among less relevant items. More recently, the company wanted to make it easier for mobile users, in particular, to find what they’re looking for. 

Fix: Amazon introduced a “Quick Access” bar on the bottom of the screen with shortcuts to the most frequently accessed features (i.e., homepage, profile, and cart). The navigation links were also made much larger for easier reading on small screens. 

Impact: The redesign provided customers easier access to the features they use most while on the go, reducing navigation complexity. The update was significant enough that Amazon changed its entire main navigation structure and even modified its brand color scheme with a new blue-green gradient.

Spotify‍

Previously, Spotify displayed playlists, albums, and podcasts all mixed together in recommendations. This created significant clutter on the front end and belied significant technical debt on the back. The cluttered experience of having music and podcasts mixed together was cited by some users as a reason to prefer competitors like Apple Music.
‍
Fix: Spotify redesigned its "Your Library" section, allowing users to swipe or tap to switch between music and podcasts. The podcast section features three dedicated areas for podcast management: Episodes (to find new episodes or resume listening), Downloads, and Shows.

Impact: The separation decluttered the experience and enabled better recommendations by preventing music and podcasts from being mixed together, making it easier for users to find the content they want to hear at any given moment. Users welcomed the change. One Reddit comment summed it up: “Finally... I just don't want it all mixed together. This is a huge benefit.”

LinkedIn

LinkedIn’s job search once suffered from “dirty” data – inconsistent job title formats that reduced search relevance.

Fix: LinkedIn introduced an AI-powered job search tool that allows users to describe their ideal roles in natural language. Users can enter queries like "Find me entry-level brand manager roles in fashion" or "Jobs for analysts who love sustainability challenges," and LinkedIn's AI finds job matches based on their descriptions. The tool taps into LinkedIn's Economic Graph to assess skills, experience, and aspirations to generate relevant matches

Impact: The tool makes job hunting less stressful and more intuitive by enabling users to communicate in a way that feels natural. It helps users discover career paths they may not have considered before, even surfacing opportunities where the job title doesn't directly match, but the role aligns with their interests.

Tips

Beneath the surface of all of these real-world examples is the IA debt that was no doubt accumulating as a result of these unaddressed problems. These examples also underscore a few best practices for addressing IA debt, which even the world’s largest brands rely on:

1. Don’t wait for a big redesign. Clean things up gradually.
   You don’t need a massive “version 2.0” launch to improve your information architecture. Fixing small problems as you notice them – like merging duplicate menu items, renaming unclear sections, or reorganizing a cluttered page – prevents issues from piling up (a la Netflix). This approach keeps users happy and avoids the “everything changed overnight” shock that can cause frustration.
   
   
2. Think about the whole system, not just individual buttons.
   IA is like the city plan, not just the street signs. Changing a single button’s label might help on one screen, but if the same label is used differently elsewhere, it creates confusion. Always zoom out: see how each change fits into the bigger structure so that menus, search results, and navigation stay consistent across the product.
   
   
3. Structure data so both AI and humans can use it easily.
   Clean, well-labeled data powers better search, smarter recommendations, and more accurate analytics. For example, instead of free-text “location” fields, use a predefined list of countries and cities. That way, AI algorithms can match results correctly, and human users see predictable, typo-free options.
   
   
4. Show problems visually – diagrams help explain why changes are needed.
   A before-and-after site map or a heatmap of user clicks can make invisible problems visible. Stakeholders and non-technical teammates understand issues faster when they can literally see that 70% of clicks are going to the wrong section or that a menu has 15 near-identical categories.
   
   
5. Measure results: for example, see if people can find things faster.
   After making IA changes, track metrics like task completion time, search success rate, or drop-off rates from key pages. If, for example, the average time to find a specific product drops from 30 seconds to 10 seconds, you’ve got measurable proof that the reorganization works.

Scale depends on structure, and the cost of waiting is high

To scale a product smoothly, you need a solid information foundation. The best time to fix the mess was before it started. The second-best time is now, so both people and AI can work more effectively.

Scale is the operative word here. At a time when investments in AI are soaring, yet 95% of generative AI pilots fail, scalable IA is non-negotiable. It may prove to be the difference between building a house made of bricks or yet another house of cards.

As part of our digital product design specialty at Transcenda, we help digital products scale by building information architectures that work for both users and AI. Let’s connect to see how we can help you build on a stronger foundation.