From Tech Writers to Architects of Purpose
Purpose, Wisdom, and Excellence in Technical Communication
The dust has settled from ConVEx 2025, an industry conference focused on information and content development, and I find myself processing insights about AI's impact on the world technical communication once again.
This year, I saw a shift in how we understand our role in relation to AI systems that shows how our ancient ways of thinking can help us address modern challenges.
Specifically, our understanding of purpose.
Last year at ConVEx 2024, I reported three key observations about AI in technical writing:
Augmentation, not replacement was the prevailing attitude. Technical writing jobs were actually projected to grow 7% over the next few years – outpacing average growth by 133%.
Content quality matters more than ever with the rise of AI, with speakers emphasizing "IA before AI" (information architecture before artificial intelligence) and the importance of "cleaning your corpus."
Content engineering emerged as a new role for technical writers who understand the entire content ecosystem and can leverage AI tools effectively across contexts.
These insights laid the groundwork for this year's even more significant transformation: technical writers becoming "architects of purpose."
AI doesn't come with purpose out of the box. It's shipped with the capacity to simulate purpose, but the real thing must be designed by humans.
AI and the Philosophical Challenge of Purpose
AI doesn't come with purpose out of the box. It's shipped with the capacity to simulate purpose, but the real thing must be designed by humans.
This distinction kept bringing to mind how Aristotle approached the idea of purpose – or what he called "telos."
For Aristotle, understanding something's purpose wasn't just about answering a simple "why" question or pointing in some general direction – it was about comprehending the ultimate end toward which something is designed.
Consider a chair.
From Aristotle's perspective, a chair's purpose isn't merely "something to sit on" – that's just its function.
Its true purpose involves how it fulfills human needs for rest, comfort, and social gathering. A well-designed chair embodies this purpose in every aspect: its height, materials, angle, and durability all align with this ultimate end.
Similarly, AI can easily generate content that appears to have direction – it can create documentation that explains how software works or answer questions about product features.
But without human architects to infuse it with genuine purpose, AI-generated content lacks the alignment with ultimate human needs and values that gives information true meaning.
An AI might accurately describe all the features of a product, but miss that the real purpose of the documentation is to help users solve specific problems that matter to them.
It might generate comprehensive explanations without understanding why certain information is more important than others in particular contexts.
This philosophical challenge creates both difficulty and opportunity for technical writers and content developers.
We're not just helping AI tools predict the next word or retrieve relevant information – we're infusing the entire knowledge ecosystem with human intention, values, and meaning.
Aristotle’s Four Causes as a Framework for AI
Teaching in Athens around 335 BCE, Aristotle developed a philosophical system that remains surprisingly relevant to our modern challenges with AI and content.
Unlike his teacher Plato, who focused on abstract ideals, Aristotle was deeply interested in understanding how things function in the real world.
You can't just tell AI your purpose. You have to build purpose into the entire system – your data selection, your content structure, your taxonomy, and your governance.
At the heart of Aristotle's approach were his four causes – four different ways to understand why something is the way it is:
Material Cause (what it's made of): For Aristotle, understanding a bronze statue begins with recognizing it's made of bronze.
For AI systems, the material cause involves the data they're trained on, the algorithms that process that data, and the infrastructure that hosts them.
Just as poor-quality bronze creates a flawed statue, poor-quality training data creates flawed AI responses.
Formal Cause (its structure or pattern): This is the form or structure that makes something what it is – the design of a statue or the architectural blueprint of a building.
For content systems, this is our information architecture, metadata schemas, and content models.
When we design these structures thoughtfully, we create the conditions for AI to generate more meaningful outputs.
Efficient Cause (what brings it into being): This is the agent or force that creates something – the sculptor who shapes the statue.
In AI content systems, this includes both the developers who build the systems and the content professionals who feed them with structured knowledge.
We are the efficient cause behind AI-generated content.
Final Cause (its purpose or telos): This is the end toward which something aims – why the statue was created in the first place. Aristotle considered this the most important cause.
For AI content systems, the final cause is the human need they're designed to serve – not just retrieving information but helping people solve problems, learn concepts, or make decisions.
Understanding these four causes helps us see that designing purpose into AI isn't just about defining end goals. It's about ensuring that the material (data), form (structure), and efficient causes (human guidance) all align with the final purpose.
When these elements don't align – when we have quality data but poor structure, or clear purpose but inadequate data – our content systems falter.
Several speakers at ConVEx 2025 emphasized this holistic approach, even if they didn't use Aristotle's terminology.
You can't just tell AI your purpose. You have to build purpose into the entire system – your data selection, your content structure, your taxonomy, and your governance.
Three Guiding Principles for AI Architects
Building on this Aristotelian foundation, three key principles emerged throughout the conference as essential for content developers becoming architects of purpose.
Though speakers at ConVEx did not use this explicit philosophical language, their discussions of best practices, challenges, and case studies consistently aligned with these ancient concepts.
I found myself mentally categorizing presentations into these three "buckets" – some focused on clarifying content purpose, others on developing better judgment in complex scenarios, and many on raising quality standards for AI-ready content.
What's striking is how well these ancient principles map to our modern challenges with AI.
Purpose (Telos)
For Aristotle, everything in nature and human creation has an ultimate end that defines what it truly is. A knife's purpose is to cut, and a good knife fulfills this purpose effectively.
Similarly, our content has specific purposes – to help users complete tasks, understand concepts, or make decisions. As architects of purpose, we need to clearly define these ends and ensure our content systems embody them.
For example, when documenting a complex software feature, we're no longer just describing what it does. We're architecting content that serves multiple purposes simultaneously:
Helping novice users complete basic tasks
Enabling power users to leverage advanced capabilities
Providing troubleshooting guidance for when things go wrong
Supplying context that AI can use to generate accurate responses to user queries
This purposeful architecture requires us to think beyond individual documents to entire knowledge structures. An architect designs not just walls, but experiences. So also, we design not just content but the understandings that emerge from it.
This goes beyond simply stating goals. It involves deeply understanding user needs, organization objectives, and social contexts, then creating content systems that inherently serve these purposes.
When content has clear telos, both humans and machines can better understand its relevance and appropriate use.
Practical Wisdom (Phronesis)
Aristotle distinguished between theoretical wisdom (sophia) – knowing abstract principles – and practical wisdom (phronesis) – knowing how to act well in particular situations.
He saw phronesis as essential for living a good life because principles alone can't tell us how to handle complex real-world scenarios. This is exactly what many technical writers have been doing for years.
This might look like:
A technical writer at a healthcare software company recognizing that their documentation needs different structures for clinical users versus administrative staff, and designing content paths that serve both without overwhelming either
A content team leader deciding which user questions should be answered through traditional documentation versus AI-generated responses, based on complexity and risk
A documentation architect determining how to structure knowledge in ways that prevent AI from confidently providing inaccurate information
In each case, there's no algorithm for the right answer – it requires the practical wisdom that comes from experience and thoughtful consideration of complex factors.
For content developers and technical communicators, phronesis means developing the judgment to balance competing concerns, adapt to changing circumstances, and make sound decisions in ambiguous situations.
It's knowing when to prioritize accuracy over simplicity, when to automate versus when to maintain human oversight, and how to adapt content strategies to different contexts.
Excellence (Arete)
For the ancient Greeks, excellence was more than just skill … it wasthe fulfillment of one's purpose with integrity. A virtuous person doesn't just do the right things but does them for the right reasons and with the right attitude.
Applied to technical communication, arete means creating content that doesn't just functionally work but embodies the highest standards of accuracy, clarity, accessibility, and ethical responsibility.
For technical communicators working with AI, excellence manifests in practical ways:
A documentation team meticulously mapping the conceptual relationships between different product features, ensuring that AI can accurately represent these connections
A content strategist developing ethical guidelines for when AI should acknowledge uncertainty rather than providing potentially incorrect information
A technical writer crafting both positive and negative examples to help AI understand the boundaries of appropriate responses
It's about continuously improving our craft while maintaining a commitment to serving users with integrity.
These three principles – purpose, wisdom, and excellence – form a powerful framework for understanding our evolving role in the AI era.
They remind us that technical communication isn't just about transmitting information – it's about creating understanding toward specific ends, guided by practical wisdom and committed to excellence.
The Architecture of Purpose
The conversation at ConVEx 2025 transcended last year's focus on augmentation to reveal a deeper idea: technical communicators are orchestrating entire knowledge ecosystems through purpose, wisdom, and excellence. This orchestration centers on human relationships rather than technology alone.
The unexamined AI system is not worth using
Like an orchestra conductor who creates harmony from many different instruments, technical communicators now design systems where human and machine elements work together toward meaningful ends.
This alignment with Aristotle's understanding of telos is striking—just as he saw purpose as inherent rather than imposed, effective knowledge architectures align with how people naturally think and work.
The most successful organizations at the conference recognized that breaking down content silos isn't merely a technical challenge but a social one. They invested in governance teams that build community across departmental boundaries while maintaining coherent purpose—what Aristotle might have recognized as the cultivation of excellence within a community sharing values and aims.
This governance operates across multiple layers of the knowledge architecture: content, presentation, semantics, analysis, automation, and distribution. In each layer, the technical communicator applies phronesis (practical wisdom) to balance competing needs while keeping human requirements at the center.
When healthcare documentation teams restructure knowledge bases according to clinical workflows rather than product features, they're aligning information architecture with human purpose in precisely the way Aristotle would have recognized as excellent design.
Perhaps most important is the ethical dimension of this work. When we architect systems that shape how AI understands and represents knowledge, we make choices about what matters and what doesn't—choices inseparable from purpose itself.
Echoing Socrates, "The unexamined AI system is not worth using.”
This fusion of ancient wisdom with modern challenges reveals the true evolution in our field: we've moved from documenting what is to designing what could be, from implementing technologies to designing human understanding.
And at the heart of this evolution lies the recognition that the purpose of all our systems, ultimately, is to serve human needs with integrity and excellence.
Educating Architects of Purpose
Perhaps the most exciting development at ConVEx 2025 was the evolution in how technical communication education is adapting to this philosophical framework. The classroom has become a laboratory for developing purpose architects who understand the relationship between human values and machine systems.
In my own courses, students no longer simply learn to use AI for content generation—they learn to build knowledge FOR AI systems. This shift mirrors Aristotle's approach to education, which wasn't merely about transmitting information but cultivating the capacity for excellence through practice and reflection.
Aristotle's Lyceum emphasized hands-on learning alongside theoretical instruction, modern technical communication education combines conceptual understanding with practical application.
Students engage in exercises that reveal how knowledge architecture shapes AI behavior. One particularly effective approach involves having teams build competing knowledge bases on the same topic, then testing how differently AI systems respond when drawing from each source.
The results demonstrate how our architectural choices influence what machines "know" and how they express that knowledge.
Through these experiences, students discover that:
Clear purpose leads to more focused and useful AI responses
Good judgment in organizing information improves how AI synthesizes knowledge
Excellence in content quality directly affects the accuracy of AI-generated answers
This pedagogical approach transforms students from passive consumers of AI outputs into active architects of AI systems. They move beyond simply asking "what does the research say?" to understanding how knowledge structures determine what can be known and expressed.
In Aristotelian terms, they're learning to shape the efficient cause of AI (prompts) along with its material cause (data), formal cause (structure), and final cause (purpose).
What makes this educational approach particularly powerful is its integration of technical skills with philosophical inquiry. Students don't just learn XML markup or taxonomy design—they grapple with fundamental questions about how knowledge should be structured to serve human flourishing.
They debate which purposes are worth pursuing and how to design systems that embody those purposes with integrity.
This fusion of practical skill with philosophical purpose prepares students not just for today's technical challenges but for the deeper ethical questions they'll face as architects of tomorrow's knowledge systems.
As these systems increasingly mediate how people access and understand information, those who design them need both technical competence and moral wisdom—precisely what Aristotle meant by the unity of knowledge and virtue.
From Prompts to Knowledge Design
The journey to becoming architects of purpose isn't without obstacles. At ConVEx 2025, several significant challenges emerged for technical communicators in this evolution:
Governance and Collaboration - Effective knowledge architecture requires cross-functional teamwork and governance structures that many organizations lack. Siloed information and disconnected teams create fragmented knowledge systems where purpose becomes diluted or conflicted.
Measuring Success - Traditional metrics like page views or time-on-page become less relevant when content is primarily consumed through AI interfaces. How do we evaluate whether our systems are fulfilling their intended purposes?
Ethical Considerations - The knowledge structures we design inevitably privilege certain information and perspectives while potentially marginalizing others. As architects of purpose, we must constantly examine whose purposes are being served.
Skill Development - The transition to knowledge architecture requires significant upskilling in areas like information design, semantic markup, and systems thinking – competencies that weren't traditionally central to technical writing education.
Despite these challenges, I'm increasingly convinced that technical writers who embrace this philosophical framework will thrive in the AI era. My own work is evolving to address these challenges through a developmental approach to knowledge design.
This year, I'm building on my work in prompt design and structured content to delve deeper into knowledge architecture. Through my PromptOps course, I've realized there's a clear progression in learning to design purpose into AI systems:
Structured Prompts: Learning to infuse individual interactions with clear purpose
Prompt Libraries: Organizing collections of purpose-driven prompts for consistency and reuse
Knowledge Bases: Building structured content libraries that embody purpose at scale
This progression mirrors Aristotle's approach to developing excellence – starting with individual actions, moving to habitual practices, and ultimately building toward a coherent way of being. It allows us to design purpose intentionally, starting small and building up systematically.
In the coming months, I'll be exploring these themes further through:
Practical examples of purpose-driven knowledge architecture
Techniques for implementing structured content that embodies telos
Educational frameworks for teaching this philosophical approach
Case studies of successful knowledge ecosystems
What excites me most is how this approach reconnects technical communication with its deeper purpose. We're not just documenting features or engineering content experiences – we're designing purpose-driven, wisely-designed, excellence-focused knowledge systems that serve both humans and machines.
The future of technical communication isn't just technical – it's philosophical. And in an age where machines increasingly mediate how people access and understand information, technical communicators who can architect purpose with wisdom and excellence will be more valuable than ever.
What aspects of this journey from prompts to knowledge design interest you most? I'd love to hear your thoughts in the comments.