7 Feb 2026: A recap on the 2025 Future of WIAA workshops
Insights from WIAA's 2025 Future of IA Workshops on AI transformation, market pressures, and IA's evolution from human comprehension to system logic to semantic models
By Grace Lau, Co-president, WIAA
Contributions from Xander Roozen, Katharina Staszkow, and Ghada Kandil. Reviewed by participants from the December 2025 Future of IA workshops. NotebookLM assisted with translation and compiling themes across audio recordings. Claude helped with rewriting
February 7, 2026 — In 2020, during the pandemic, World Information Architecture Association held its first Future of IA workshops to chart the direction of World IA Day after its separation from the IA Institute. Five years later, in December 2025, we convened again—this time to assess how information architecture has evolved, where the discipline stands today, and where WIAA can make the most impact moving forward.
Across three 90-minute sessions spanning different time zones, we brought together 13 World IA Day organizers, speakers, and UX practitioners from Asia-Pacific, Europe, and North America. These workshops were led by members of the global board, Soyeon Lee, Grace Lau, Xander Roozen, and Andrew Jung.
Read more about the background and context of the workshops.
We structured each 90-minute session into four conversations:
Introductions and context: We started by learning where everyone's coming from—literally and professionally. Participants shared their current roles, what the job market looks like in their regions and how their day-to-day practice has evolved over the past five years.
Generative AI and IA: We explored how AI is already changing specific tasks in our work, grappled with its ethical implications, and imagined how it might reshape information architecture over the next five years.
Supporting the community: We asked what WIAA can do better. What formats would help you learn and grow? Masterclasses? Podcasts? How can we build stronger bridges to adjacent disciplines?
Looking ahead: We closed by asking more questions: What parts of our profession should we keep, what needs to change, and what might be time to sunset? And what experiments could we run together this year to find out?
What we heard
Several themes emerged consistently across our conversations: the challenging state of the job market, how AI is reshaping our work, and what WIAA can do to support the community through these changes.
We’re navigating pressure.
Participants across all regions described navigating sustained complexity: shrinking resources, disappearing traditional roles, and mounting expectations. Yet beneath the fatigue lies a persistent commitment to keeping the field relevant and meaningful.
Mounting pressure: Professionals are expected to accomplish "more in less time", with creative energy depleted by excessive meetings and workplace politics.
Evolution of IA roles: Traditional IA job titles are transforming, rather than disappearing. The work is being absorbed into broader roles such as Product Designer or UX Strategist or elevated into specialized domains like semantic modeling, metadata strategy, and systems architecture.
Junior talent gap: All sessions highlighted the struggle to train and hire new IA professionals. Junior roles have become scarce, forcing emerging designers to compete against experienced, recently laid-off candidates, or meet expectations equivalent to senior-level work. The traditional mentorship system for developing professionals is perceived as "lost".
Community engagement in transition: Multiple participants recalled that the peak interest and engagement for major conferences and events occurred around 2015-6, with conference attendance dropping nearly 75% and World IA Day locations shrinking from 70 cities to 20. However, this may reflect a shift in how the community convenes rather than loss of interest in the discipline itself.
We’re structuring meaning for humans, systems, and AI models.
Generative AI is recognized as an accelerating factor in a transformation that's been building for years: information architecture has evolved through three distinct audiences. What started as organizing information for human comprehension (navigation, content hierarchies, findability) expanded to structuring data for system interoperability (APIs, databases, cross-platform consistency).
Now, IA is entering a third phase: creating semantic models and ontologies that enable machine intelligence to understand, process, and generate meaningful content. Each layer builds on the previous one, but requires fundamentally different architectural thinking.
How this shift manifests in practice:
AI augments surface-level IA tasks: Large Language Models can now draft site maps, generate taxonomies, and conduct content inventories—deliverables that once defined IA work at the human comprehension layer. Rather than eliminating the profession, this shifts IA work from producing these artifacts to evaluating whether they serve strategic goals, align with user mental models, and create scalable semantic infrastructure.
The role of human judgment: Despite AI's capabilities, all sessions emphasized that human expertise remains essential, but its application has evolved. AI outputs are "cookie cutter" because they lack contextual understanding and strategic thinking. Human judgment now operates less on producing site maps and taxonomies and more on evaluating whether those structures align with business logic, user mental models, and long-term scalability.
IA as semantic infrastructure: AI's generation of massive content volumes paradoxically increases the need for strong information architecture, but at the model layer. Organizations need robust ontologies, taxonomies, and conceptual models that give AI systems the semantic structure to generate coherent, strategically aligned content rather than syntactically correct chaos. However, this work is less visible than traditional user-facing navigation structures, making it easier for stakeholders to overlook or undervalue even as it becomes more critical.
The training data architecture problem: AI struggles with foundational fields like IA and accessibility because the vast majority of online resources used for training contain poor-quality information architecture. This isn't just a data quality issue—it's an architectural feedback loop. IAs must now consider how their structural decisions become the examples that train future AI systems. Well-architected systems teach good practice; flawed structures perpetuate problems at scale.
Organizational pressure vs. architectural maturity: Decision-makers are pushing teams to adopt AI and accelerate timelines without recognizing that effective AI implementation requires stronger information architecture across all three layers. The pressure to "just use AI" ignores that models need well-structured inputs (semantic models), systems need clear data architectures (interoperability standards), and users still need comprehensible outputs (interface clarity). Speed without structural integrity creates technical debt at every layer.
WIAA can help with building more knowledge infrastructure.
As IA practice evolves across human, system, and model layers, participants called for WIAA to provide updated resources and stronger connections both within the discipline and to adjacent fields.
Educational resources for contemporary practice: Existing foundational IA books, while theoretically valuable, are often perceived as "too abstract" or "too difficult" for immediate application in today's AI-driven, multi-layer practice environments. Participants called for educational formats that bridge enduring principles with contemporary challenges:
Masterclasses that address working at system and model levels
Learning tracks on existing platforms that progress from interface-level to semantic architecture
Bite-sized, digestible content that make complex concepts accessible
Applied frameworks showing how traditional IA thinking translates to ontology design, data modeling, and AI system architecture
Interdisciplinary bridges beyond UX: As IA work increasingly happens at system and model layers, the discipline must strengthen connections with fields that have always worked at these levels:
Database engineers and data architects who understand structural integrity and schema design
SEO specialists and knowledge engineers working with semantic relationships and discoverability
Library and information scientists who've formalized knowledge organization systems for centuries
Computational linguists and ontologists building semantic frameworks for AI
Participants emphasized that these aren't new relationships—they're reconnections to IA's historical roots outside of digital design. WIAA can facilitate structured knowledge sharing from senior practitioners to emerging IAs, not as nostalgia, but as translating enduring architectural principles for new contexts:
Capturing how experienced IAs approach conceptual modeling (a skill that applies across all three layers)
Documenting decision-making frameworks that remain relevant whether designing for humans, systems, or models
Creating mentorship structures that help junior practitioners develop strategic architectural thinking, not just tool proficiency
While these needs were universal, the Asia-Pacific session placed particular emphasis on formalizing IA's theoretical foundations through academic rigor. Participants called for WIAA to create a "white paper" or canonical definition of information architecture that can anchor the discipline as it evolves. They also discussed the ongoing tension between "Big IA" (high-level conceptual and strategic work) and "Little IA" (hands-on practical tasks like wireframing)—a debate that led to the creation of Japan's Small IA Summit to champion essential practical work.
We need to make our work more visible.
As IA work moves to system and model layers—beneath interfaces and often invisible to stakeholders—participants emphasized the need to demonstrate tangible impact and make structural decisions visible.
Connecting architecture to business impact: IA must articulate how structural decisions affect measurable outcomes: how normalized taxonomies reduce technical debt, how semantic models improve AI accuracy and reduce hallucinations, how ontological frameworks enable system interoperability that affects development costs and time to market. The challenge isn't just doing good IA work at the model layer—it's translating that work into business metrics stakeholders recognize.
Show the work, not just the outcomes: Participants proposed executing and showcasing real-world projects (redesigning a government information system, architecting semantic models for LLM-powered public services) as "show and tell" demonstrations. Making IA processes visible helps stakeholders understand what happens between "we need better content" and "the system works." This is especially critical for system and model-level work that produces no visible interface.
Documenting the architecture of decisions: While IA may not own ethical strategy, it plays a critical role in making structural choices traceable. Documenting why certain taxonomies were chosen, how semantic models prioritize certain relationships, what the ontology excludes—this creates accountability in how systems are architected. At the model layer, these decisions shape what AI can understand and generate, making documentation an ethical necessity.
Teaching critical thinking to AI-native practitioners: WIAA's educational outreach should focus on cultivating curiosity in emerging professionals who've grown up with AI. Rather than accepting AI-generated taxonomies or semantic models at face value, the next generation needs to ask: Why is it structured this way? What assumptions are embedded? What alternatives exist? This critical architectural thinking is what separates using AI from designing intelligent systems.
We're struggling to navigate the workplace.
Workplace advocacy and communication: A major concern for younger designers was the lack of preparation for navigating workplace politics, managing difficult stakeholders, and building confidence to advocate for design decisions.
Management resistance: Mid-to-senior level practitioners expressed frustration with being pushed into management roles despite preferring to remain line-level contributors.
What's Next
The input gathered through these workshops will inform WIAA's strategic direction as we work to prioritize initiatives and determine next steps. While we don't have a specific timeline for implementing changes, your feedback is helping us identify what matters most to our community and where to focus our efforts.
We want to hear from you. Community involvement doesn't end here. We encourage you to:
Share ongoing feedback as ideas and priorities evolve
Participate at World IA Day, World IA Cafe, and Taxonomy Talk
Reach out directly with questions or suggestions at [email protected]
Thank you to everyone who took the time to participate in this process. Your insights, experiences, and perspectives are invaluable as we shape WIAA's future together. We're committed to keeping you informed as we move forward and look forward to your continued involvement.
Tags: Future of WIAA
About World Information Architecture Association
The World Information Architecture Association is a global community of professionals dedicated to the advancement of information architecture, a discipline devoted to the understanding of how information is structured and understood. We are committed to promoting the understanding and practice of information architecture through education, research, and collaboration.
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