{ "meta": { "type": "insight_article", "language": "en-GB", "last_updated": "2026-10-16", "ai_endpoint": "/insights/why-ai-projects-start-with-content-not-technology/ai.json", "source_page": "/insights/why-ai-projects-start-with-content-not-technology/" }, "retrieval_summary": { "what": "This article explains that successful AI projects depend on reliable, well-organised content and information architecture, and advises starting with a specific use case and information requirements before choosing technology.", "who": { "organisation": "Feel Created" }, "relevant_for": [ "Digital and website teams planning AI features or integrations", "Organisations exploring AI-powered search, assistants, recommendations or knowledge tools", "Content, digital and communications teams responsible for organisational information" ], "problems_solved": [ "Organisations start AI projects with technology questions instead of information needs", "Inconsistent, outdated, duplicated or poorly organised content undermines AI effectiveness", "AI cannot automatically resolve contradictions or fill gaps in information", "Content that is well-written but not structured or categorised is difficult for AI to reuse", "Different terms used for the same thing", "Several versions of the same information", "Old information remaining available alongside current content", "Important details buried inside long documents" ] }, "page": { "title": "Why AI Projects Start With Content, Not Technology", "slug": "why-ai-projects-start-with-content-not-technology", "url": "/insights/why-ai-projects-start-with-content-not-technology/", "canonical_url": "https://www.feelcreated.com/insights/why-ai-projects-start-with-content-not-technology/", "status": "published" }, "article": { "type": "Insight article", "primary_topic": "Building the information foundation before the AI layer", "intent": "This article explains that successful AI projects depend on reliable, well-organised content and information architecture, and advises starting with a specific use case and information requirements before choosing technology.", "author": { "name": "Jonathan Townend", "organisation": "Feel Created", "bio": "Jonathan Townend is the Lead Front-End Developer at Feel Created, where he leads the development of fast, accessible and high-performing WordPress websites. Specialising in modern front-end technologies, performance optimisation and user experience." }, "audience": [ "Digital and website teams planning AI features or integrations", "Organisations exploring AI-powered search, assistants, recommendations or knowledge tools", "Content, digital and communications teams responsible for organisational information" ] }, "topics": [ "Building the information foundation before the AI layer", "AI project planning", "Content quality", "Information architecture", "Structured content", "Content governance", "Metadata and taxonomy", "AI-powered search and discovery", "Information retrieval", "Content structure", "AI use cases", "Content review", "AI testing and evaluation" ], "content_focus": [ "The importance of information quality for AI applications", "Definition of content in an AI project", "Characteristics of content that supports AI (accurate, current, consistent, structured, categorised, connected, owned)", "The need to start with a use case and work backwards to information requirements", "Information architecture", "Content governance", "Structured content", "AI implementation", "User needs", "Reviewing information requirements before technology decisions" ], "canonical_claims": [ { "claim": "Technology cannot automatically decide what an organisation considers authoritative, resolve every contradiction or fill gaps where reliable information does not exist.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "ai-is-only-as-useful-as-the-information-it-can-work-with", "id": "claim-001" }, { "claim": "Structured content and metadata can make the meaning and relationships within information more explicit, reducing ambiguity for AI applications.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "structure-gives-ai-more-context", "id": "claim-002" }, { "claim": "Starting with a specific user need and working backwards is a more practical approach than reviewing all content before deciding what to build.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "start-with-the-use-case-then-work-backwards", "id": "claim-003" }, { "claim": "An organisation can have thousands of pages and documents while still struggling to answer a relatively simple question consistently.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "what-do-we-mean-by-content-in-an-ai-project", "id": "claim-004" }, { "claim": "Technology choices become easier once you understand the job the technology needs to perform and the information it will work with.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "start-with-the-use-case-then-work-backwards", "id": "claim-005" }, { "claim": "Good source information provides a stronger foundation but does not guarantee that an AI application will produce useful results.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "how-will-you-know-whether-it-works", "id": "claim-006" }, { "claim": "Planning an AI project often reveals existing information problems such as unclear ownership and inconsistent terminology.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "ai-can-expose-existing-content-problems", "id": "claim-007" }, { "claim": "Improving content structure, categorisation, and maintenance can support many parts of a digital platform beyond AI.", "evidence_type": "page_content", "evidence_strength": "medium", "source_section": "better-content-foundations-have-value-beyond-ai", "id": "claim-008" } ], "services": [ { "name": "AI SEO services & AI search optimisation", "relationship": "supporting", "url": "/services/zero-click-ai-search-optimisation/" }, { "name": "Website architecture & integrations", "relationship": "related", "url": "/services/website-architecture-integrations/" }, { "name": "Digital planning & research", "relationship": "related", "url": "/services/digital-planning-research/" }, { "name": "Information architecture", "relationship": "supporting", "url": "/services/information-architecture/" }, { "name": "Content architecture", "relationship": "primary", "url": "/services/content-architecture/" } ], "sections": [ { "id": "ai-projects-often-begin-with-the-wrong-question", "heading": "AI projects often begin with the wrong question", "type": "section", "key_points": [ "How could we add AI search to the website?", "Should we connect an AI system to our CMS?" ], "content": [ { "type": "paragraph", "text": "When organisations start thinking about AI, the conversation often begins with technology. Which AI model should we use? Do we need a chatbot? How could we add AI search to the website? Should we connect an AI system to our CMS? Those are reasonable questions, but they are rarely the best place to start. Most useful AI applications depend on something much more familiar: information. An AI system needs something reliable to search, interpret, summarise, connect or act upon. If the information behind it is inconsistent, outdated, duplicated or poorly organised, changing the technology will only solve part of the problem. This is why many AI projects are also content projects. Before deciding how AI should work, it helps to understand what information it will need, where that information currently lives and whether it is in a form that can support what you want the AI to do." } ] }, { "id": "ai-is-only-as-useful-as-the-information-it-can-work-with", "heading": "AI is only as useful as the information it can work with", "type": "section", "content": [ { "type": "paragraph", "text": "AI systems can do impressive things with large amounts of information, but they do not remove the need for that information to be trustworthy. Imagine an organisation wants to introduce an AI assistant that answers questions about its services. The technology might be perfectly capable of finding and summarising information, but what happens if the website contains three slightly different descriptions of the same service? What if an old PDF contradicts a newer page? What if important eligibility information is missing altogether? The AI still has to work with what it has been given. Depending on how a system is designed, there are ways to select sources, retrieve relevant information and control which content an AI application can use. But technology cannot automatically decide what an organisation considers authoritative, resolve every contradiction or fill gaps where reliable information does not exist. The quality of the application therefore depends partly on the quality of the information behind it. This applies to far more than conversational assistants. AI-powered search, content recommendations, internal knowledge tools, automated content workflows and other AI features all rely on information being available in a form the system can use." } ] }, { "id": "what-do-we-mean-by-content-in-an-ai-project", "heading": "What do we mean by content in an AI project?", "type": "section", "items": [ "Website pages and Knowledge Hub articles", "Product or service information", "Policies, procedures and guidance", "FAQs and support information", "Documents and downloadable resources", "Internal knowledge and documentation", "Metadata and taxonomy", "Structured records stored in a CMS" ], "content": [ { "type": "paragraph", "text": "When we talk about content, it is easy to think only about the words on website pages. In an AI project, the definition can be much broader. Depending on the application, useful information might include: Some of this might be written primarily for people. Other information may already exist as structured fields or records intended to be reused by different systems. The important question is not simply whether the organisation has enough content. It is whether the right information exists, whether it can be trusted and whether the AI application can identify and use it appropriately. An organisation can have thousands of pages and documents while still struggling to answer a relatively simple question consistently." } ] }, { "id": "good-ai-needs-more-than-good-writing", "heading": "Good AI needs more than good writing", "type": "section", "items": [ "Accurate: people and systems need to be able to trust it.", "Current: outdated information should not compete with the latest version.", "Consistent: terminology and important facts should agree across different sources.", "Structured: useful information should be separated into meaningful fields or components where appropriate.", "Categorised: content should have useful metadata and taxonomy.", "Connected: relationships between related subjects should be represented clearly.", "Owned: somebody should know who is responsible for maintaining it." ], "content": [ { "type": "paragraph", "text": "Well-written content is important, particularly when information is ultimately being presented to people. But writing quality is only one part of preparing content for wider digital use. Information also benefits from being: A page can read beautifully while still being difficult to reuse. For example, a university course page could contain the course title, qualification, duration, entry requirements, location and application information in one large editable content field. A visitor can read the page, but other systems have relatively little explicit information about what each part represents. If those details are stored as meaningful fields, they can potentially be reused for filtering, search, comparison, structured data, integrations and AI applications without having to infer everything from a block of text. The content has not necessarily changed. Its structure has." } ] }, { "id": "why-poorly-organised-content-creates-problems-for-ai", "heading": "Why poorly organised content creates problems for AI", "type": "section", "items": [ "Different terms being used for the same thing", "Several versions of the same information", "Old information remaining available alongside current content", "Important details buried inside long documents", "Related information having no explicit connection", "Pages with unclear purpose or ownership", "Information being organised differently across departments" ], "content": [ { "type": "paragraph", "text": "Modern AI systems can work with unstructured information such as articles, documents and long passages of text. The problem is not that every piece of content needs to follow a rigid structure, but that poorly organised information can make it harder to identify what is current, authoritative and relevant. Consider a service that is described differently across a main service page, several PDFs, an FAQ, an old news article and an internal document. A person familiar with the organisation may understand which source is current and which terminology means the same thing. A digital system does not necessarily have that organisational knowledge unless it is represented somewhere. Common problems include: These problems are not unique to AI. They can already make websites harder to search, navigate and maintain. AI simply makes some of the consequences more obvious because the system may need to retrieve and combine information without the organisational knowledge a person would bring to the task." } ] }, { "id": "structure-gives-ai-more-context", "heading": "Structure gives AI more context", "type": "section", "items": [ "Service name", "Description", "Intended audience", "Related sectors", "Locations", "FAQs", "Related services", "Case studies" ], "content": [ { "type": "paragraph", "text": "Structured content can make the meaning and relationships within information more explicit. Instead of treating a service page as one large piece of content, for example, a website might separately understand its: Metadata and taxonomy can add further context by describing what content is about and how different pieces of information relate to one another. This does not mean every sentence needs to become a database field. Too much structure can make a CMS frustrating to use and difficult to maintain. The goal is to identify information that has a meaningful reason to be structured. A useful way to think about the progression is: Content provides the information. Structure identifies what that information represents. Context helps systems distinguish and connect related information. The AI application can then use those foundations to provide a particular experience. The more explicit those foundations are, the less ambiguity there is when an application retrieves and uses that information." } ] }, { "id": "ai-can-expose-existing-content-problems", "heading": "AI can expose existing content problems", "type": "section", "key_points": [ "Which version of this policy is current?", "Are these two services actually different?" ], "content": [ { "type": "paragraph", "text": "Planning an AI project often reveals information problems that were already there. Questions that previously seemed unimportant can suddenly become difficult to avoid. Which version of this policy is current? Are these two services actually different? Why do three departments use different names for the same process? Who is responsible for updating this information? Which source should be treated as authoritative? Why is an important piece of guidance only available inside a five-year-old PDF? These are not really AI questions. They are questions about content governance, information architecture and organisational knowledge. That distinction matters because implementing an AI tool will not resolve them by itself. Before asking a system to find and combine information automatically, an organisation needs to make some decisions about which information can be trusted and how it will be maintained. The aim is not simply to provide an AI system with more information, but to give it access to the right information for the job." } ] }, { "id": "start-with-the-use-case-then-work-backwards", "heading": "Start with the use case, then work backwards", "type": "section", "items": [ "User need", "AI capability", "Information required", "Content sources", "Gaps to fix", "Technology" ], "content": [ { "type": "paragraph", "text": "Starting with content does not mean reviewing every page, document and database before deciding what to build. A more practical approach is to start with a specific user need and work backwards. The process might look something like this: Imagine the goal is to help prospective students find suitable courses using natural-language questions. Rather than immediately choosing an AI search platform, you could first ask what information would be needed to answer those questions well. That might include subjects, qualifications, entry requirements, locations, study modes, course durations and application details. You can then look at where that information currently lives. Is it structured consistently across every course? Are important details hidden in free text? Are the same fields described using different terminology? Is anything missing? How often does the information change? Those answers give you a clearer idea of what the project actually requires. At that point, technology choices become easier to make because you understand the job the technology needs to perform and the information it will be working with." } ] }, { "id": "what-should-you-review-before-choosing-the-technology", "heading": "What should you review before choosing the technology?", "type": "section", "content": [ { "type": "paragraph", "text": "The exact requirements will depend on the project, but there are several useful questions to ask before making significant technology decisions." } ] }, { "id": "what-information-does-the-ai-need", "heading": "What information does the AI need?", "type": "section", "parent_id": "what-should-you-review-before-choosing-the-technology", "parent_heading": "What should you review before choosing the technology?", "content": [ { "type": "paragraph", "text": "Start with the intended experience. What questions should it answer, decisions should it support or tasks should it help someone complete? This gives the content review a clear boundary and helps avoid auditing information that has little relevance to the project." } ] }, { "id": "where-does-that-information-live", "heading": "Where does that information live?", "type": "section", "parent_id": "what-should-you-review-before-choosing-the-technology", "parent_heading": "What should you review before choosing the technology?", "content": [ { "type": "paragraph", "text": "It might all exist in the website CMS, but it could equally be spread across documents, internal systems, databases or third-party platforms. Understanding the sources can reveal integration requirements before technology choices are made." } ] }, { "id": "can-the-information-be-trusted", "heading": "Can the information be trusted?", "type": "section", "parent_id": "what-should-you-review-before-choosing-the-technology", "parent_heading": "What should you review before choosing the technology?", "content": [ { "type": "paragraph", "text": "Look for contradictions, duplicates, missing information and content that has not been reviewed recently. Where several sources exist, it should be clear which one is authoritative." } ] }, { "id": "how-is-the-information-structured", "heading": "How is the information structured?", "type": "section", "parent_id": "what-should-you-review-before-choosing-the-technology", "parent_heading": "What should you review before choosing the technology?", "content": [ { "type": "paragraph", "text": "Some content may already be stored in useful fields and categories. Other information might exist only inside long passages of text or documents. The aim is not to structure everything, but to understand where additional structure would make the information easier to find, reuse or interpret." } ] }, { "id": "who-owns-the-information", "heading": "Who owns the information?", "type": "section", "parent_id": "what-should-you-review-before-choosing-the-technology", "parent_heading": "What should you review before choosing the technology?", "content": [ { "type": "paragraph", "text": "An AI application needs more than a good set of information on launch day. Someone needs to keep that information accurate. Ownership, review processes and publishing workflows therefore matter just as much as the initial content review." } ] }, { "id": "what-happens-when-the-information-changes", "heading": "What happens when the information changes?", "type": "section", "parent_id": "what-should-you-review-before-choosing-the-technology", "parent_heading": "What should you review before choosing the technology?", "content": [ { "type": "paragraph", "text": "If a service changes, a policy is replaced or a new product is introduced, how quickly will the AI application have access to the new information? A system that starts with accurate information but gradually falls out of date will become less useful over time." } ] }, { "id": "how-will-you-know-whether-it-works", "heading": "How will you know whether it works?", "type": "section", "parent_id": "what-should-you-review-before-choosing-the-technology", "parent_heading": "What should you review before choosing the technology?", "content": [ { "type": "paragraph", "text": "Good source information provides a stronger foundation, but it does not guarantee that an AI application will produce useful results. Once the application is working, it needs to be tested against realistic questions and tasks. Does it find the right information? Does it use that information accurately? What happens when the answer is missing, ambiguous or spread across several sources? Testing those situations can reveal problems with the application itself as well as gaps in the underlying content. It also gives teams something more useful to judge than whether an AI-generated response simply sounds convincing." } ] }, { "id": "you-dont-need-to-fix-everything-before-you-start", "heading": "You don’t need to fix everything before you start", "type": "section", "content": [ { "type": "paragraph", "text": "The idea of reviewing content can make an AI project sound much bigger than it needs to be. Not every organisation needs a perfectly structured content estate before it can experiment with AI. In many cases, trying to clean up everything first would create a large project with no clear end. The better approach is to focus on the information required for a particular use case. An internal assistant designed to answer questions about HR policies has different content requirements from AI-powered website search. A tool that recommends related articles has different requirements from a system helping customers compare complex products. That is why AI readiness is rarely a simple yes-or-no question. An organisation may have everything required to test one idea while needing significant content, governance or technical work before attempting another. Starting with a contained use case also makes it easier to learn. You can identify which content problems genuinely affect the application, improve those areas and use what you discover to inform future projects." } ] }, { "id": "better-content-foundations-have-value-beyond-ai", "heading": "Better content foundations have value beyond AI", "type": "section", "items": [ "Find through website search", "Filter and browse", "Reuse across different pages and channels", "Maintain consistently", "Connect to other systems", "Present through structured data", "Manage through editorial workflows", "Adapt as the website develops" ], "content": [ { "type": "paragraph", "text": "One of the advantages of starting with content is that the work does not only benefit AI. Improving the way information is structured, categorised and maintained can support many parts of a digital platform. It can make content easier to: This matters because individual AI technologies will change. The model, platform or integration that seems appropriate today may not be the one an organisation uses several years from now. Well-managed information is much less dependent on a particular technology. That makes content architecture, taxonomy and governance useful foundations in their own right rather than work that only makes sense if a particular AI project goes ahead." } ] }, { "id": "build-the-information-foundation-before-the-ai-layer", "heading": "Build the information foundation before the AI layer", "type": "section", "content": [ { "type": "paragraph", "text": "AI can provide new ways for people to search, explore and work with information, but it does not remove the need to understand the information itself. Before asking which model, platform or integration to use, it is worth asking a more basic set of questions: what does the application need to know, where will that knowledge come from and can we rely on it? Those questions often reveal that the first stage of an AI project is not particularly futuristic. It involves understanding content, resolving inconsistencies, improving structure and deciding how information will be maintained. The technology will continue to change. Organisations that understand and manage their information well will be in a much stronger position to make useful choices as it does." } ] } ], "entities": [ { "name": "Information architecture", "type": "Discipline" }, { "name": "Information foundation", "type": "Concept" }, { "name": "Content architecture", "type": "Discipline" }, { "name": "Structured content", "type": "Concept" }, { "name": "Content governance", "type": "Concept" }, { "name": "Metadata", "type": "Concept" }, { "name": "Content", "type": "Concept" }, { "name": "AI", "type": "Technology" } ], "concept_relationships": [ { "subject": "Structured content", "predicate": "enables", "object": "reuse for filtering, search, comparison, structured data, integrations and AI applications" }, { "subject": "Structured content", "predicate": "benefits", "object": "website search, filtering, reuse, and maintenance" }, { "subject": "Technology choices", "predicate": "are easier once", "object": "information and use case are understood" }, { "subject": "AI application", "predicate": "uses", "object": "foundations to provide a particular experience" }, { "subject": "Context", "predicate": "helps systems distinguish and connect", "object": "related information" }, { "subject": "Content ownership", "predicate": "ensures", "object": "ongoing accuracy of AI applications" }, { "subject": "AI project", "predicate": "requires", "object": "understanding of information needs" }, { "subject": "Structure", "predicate": "identifies", "object": "what information represents" }, { "subject": "AI applications", "predicate": "depend on", "object": "reliable information" }, { "subject": "Content quality", "predicate": "influences", "object": "AI output quality" } ], "implementation_patterns": [ { "pattern": "User need → AI capability → Information required → Content sources → Gaps to fix → Technology", "description": "A process that starts with a specific user need and works backwards to determine the information and content sources needed before choosing technology.", "benefit": "Ensures the AI application is built on a solid information foundation, reducing ambiguity and improving retrieval accuracy." }, { "pattern": "Use case backwards planning", "description": "Start with a specific AI use case, identify the information needed, locate where it lives, and address gaps that affect that use case.", "benefit": "Ensures AI projects are grounded in real information needs and avoids unnecessary content overhaul." }, { "pattern": "Structured content fields", "description": "Store key information (e.g., course title, qualification, duration) as meaningful fields rather than in one large content block.", "benefit": "Enables reuse for filtering, search, comparison, structured data, integrations and AI applications." }, { "pattern": "Information-first AI planning", "description": "Start AI projects by assessing information needs and addressing content issues before technology selection.", "benefit": "Ensures AI applications are built on reliable information, improving their usefulness and longevity." }, { "pattern": "Use case-driven content review", "description": "Focus content review on the information required for a specific AI use case, such as an internal HR assistant or AI-powered website search.", "benefit": "Avoids auditing irrelevant information and gives clear boundaries." }, { "pattern": "Iterative improvement", "description": "Start with a contained use case, identify content problems that affect the application, improve those areas, and use learnings for future projects.", "benefit": "Makes learning easier and avoids large, open-ended projects." }, { "pattern": "Content governance", "description": "Assign clear ownership for maintaining content and ensure it is accurate, current, consistent, and categorised.", "benefit": "Maintains trustworthiness of information that AI systems rely on." }, { "pattern": "Realistic testing and maintenance", "description": "After improving content foundations, test AI applications realistically and maintain information ongoing.", "benefit": "Ensures AI outputs remain accurate and useful over time." } ], "practical_guidance": [ { "guidance": "Start with a specific user need and work backwards to identify the information required, where it lives, and gaps that affect the use case.", "importance": "high" }, { "guidance": "Assess information trustworthiness by looking for contradictions, duplicates, missing information, and clarity on authoritative sources.", "importance": "high" }, { "guidance": "Identify where the required information lives, including CMS, documents, internal systems, databases, or third-party platforms.", "importance": "high" }, { "guidance": "Understand where the required information currently lives and whether it is in a form that can support the AI application.", "importance": "high" }, { "guidance": "Identify information that has a meaningful reason to be structured, rather than over-structuring every piece of content.", "importance": "medium" }, { "guidance": "Test the AI application against realistic questions and tasks, including missing, ambiguous, or multi-source answers.", "importance": "high" }, { "guidance": "Make decisions about which information can be trusted and how it will be maintained before implementing an AI tool.", "importance": "high" }, { "guidance": "Start with the intended experience: what questions should the AI answer, decisions support, or tasks help complete?", "importance": "high" } ], "risk_areas": [ { "risk": "AI-generated responses may sound convincing but be inaccurate if not tested against realistic questions", "impact": "Misleading outcomes and potential harm to users" }, { "risk": "Attempting to fix all content before starting an AI project creates a large project with no clear end", "impact": "Delays AI implementation and may prevent experimentation" }, { "risk": "AI application becomes less useful over time if information is not kept accurate and up to date", "impact": "Reduced trust and effectiveness of the AI system" }, { "risk": "Starting AI projects with technology questions rather than information needs", "impact": "Leads to solutions that only partially address problems because underlying content issues remain" }, { "risk": "Implementing an AI tool without resolving content governance issues", "impact": "The system may retrieve and combine information without the organisational knowledge needed to ensure accuracy and relevance." }, { "risk": "Inconsistent, outdated, duplicated or poorly organised content", "impact": "AI systems may produce less reliable or contradictory outputs" }, { "risk": "Choosing technology before understanding information needs", "impact": "May lead to ineffective AI applications that do not meet user needs" }, { "risk": "Content that is not structured or categorised", "impact": "AI cannot easily identify and reuse specific information, limiting functionality" } ], "common_misconceptions": [ { "misconception": "Every piece of content needs to follow a rigid structure for AI to work.", "clarification": "The problem is not lack of structure but poorly organised information that makes it harder to identify what is current, authoritative and relevant." }, { "misconception": "All content must be perfectly structured before AI can be used.", "clarification": "Organisations should start with a specific use case, identify the information required, and address gaps that materially affect that use case." }, { "misconception": "AI projects are primarily about choosing the right technology.", "clarification": "The most useful AI applications depend on reliable information; technology choices are easier once the information and use case are understood." }, { "misconception": "Structured content alone guarantees accurate AI outputs.", "clarification": "AI applications still require realistic testing and ongoing information maintenance." }, { "misconception": "AI projects should start with technology selection.", "clarification": "The article argues that the first stage involves understanding content, resolving inconsistencies, improving structure, and planning information maintenance." }, { "misconception": "Content means only the words on website pages.", "clarification": "In an AI project, content includes website pages, documents, structured CMS data, metadata, taxonomy, internal knowledge and information in connected systems." }, { "misconception": "AI readiness is a simple yes-or-no question", "clarification": "An organisation may be ready for one use case but need significant work for another." }, { "misconception": "Well-written content is sufficient for AI.", "clarification": "Writing quality is only one part; content also needs to be accurate, current, consistent, structured, categorised, connected and owned." } ], "technical_context": { "content_types": [ "Website pages", "Knowledge Hub articles", "Product or service information", "Policies, procedures and guidance", "FAQs and support information", "Documents and downloadable resources", "Internal knowledge and documentation", "Metadata and taxonomy", "Structured records in a CMS", "Information in connected systems" ], "content_quality_attributes": [ "Accurate", "Current", "Consistent", "Structured", "Categorised", "Connected", "Owned" ], "structured_content": "Structured content can make meaning and relationships explicit, but over-structuring can hinder usability.", "metadata_taxonomy": "Metadata and taxonomy add context by describing what content is about and how pieces relate.", "ai_retrieval": "AI systems need explicit organisational knowledge to identify current, authoritative and relevant information." }, "reasoning_paths": [ { "user_problem": "Poorly organised content creates problems for AI, such as contradictions, duplicates, and missing information", "evidence_refs": [ "claim-001", "claim-002", "claim-007" ], "relevant_sections": [ "can-the-information-be-trusted", "why-poorly-organised-content-creates-problems-for-ai", "how-will-you-know-whether-it-works" ] }, { "user_problem": "AI projects often begin with the wrong question, focusing on technology rather than information needs", "evidence_refs": [ "claim-005", "claim-001", "claim-007" ], "relevant_sections": [ "start-with-the-use-case-then-work-backwards", "ai-projects-often-begin-with-the-wrong-question", "ai-is-only-as-useful-as-the-information-it-can-work-with" ] }, { "user_problem": "Testing AI applications is challenging; realistic questions and tasks are needed to reveal gaps", "evidence_refs": [ "claim-002", "claim-006", "claim-004" ], "relevant_sections": [ "how-will-you-know-whether-it-works", "build-the-information-foundation-before-the-ai-layer", "start-with-the-use-case-then-work-backwards" ] }, { "user_problem": "AI applications can become less useful over time if information is not maintained and updated", "evidence_refs": [ "claim-006", "claim-002", "claim-005" ], "relevant_sections": [ "what-happens-when-the-information-changes", "good-ai-needs-more-than-good-writing", "build-the-information-foundation-before-the-ai-layer" ] } ], "content_profile": { "content_type": "Insight article", "primary_topic": "Building the information foundation before the AI layer", "supporting_topics": [ "AI project planning", "Content quality", "Information architecture", "Structured content", "Content governance", "Metadata and taxonomy" ], "primary_audience": [ "Digital and website teams planning AI features or integrations", "Organisations exploring AI-powered search, assistants, recommendations or knowledge tools", "Content, digital and communications teams responsible for organisational information" ] }, "retrieval_topics": [ "Building the information foundation before the AI layer", "AI project planning", "Content quality", "Information architecture", "Structured content", "Content governance", "Metadata and taxonomy", "AI-powered search and discovery", "Information retrieval", "Content structure", "AI use cases", "Content review" ], "organisation": { "name": "Feel Created", "url": "https://www.feelcreated.com/", "type": "Digital partner specialising in websites and digital platforms", "specialisms": [ "Digital strategy and planning", "User experience (UX) design", "Accessibility and inclusive design", "WordPress design and development", "Content strategy and information architecture", "AI-ready structured content", "AI Features & Integrations", "Search optimisation and technical SEO", "Website performance optimisation", "Website security and technical governance", "Platform architecture and integrations", "Content management systems", "Digital audits and reviews", "Creative and marketing support", "Website maintenance and support" ], "service_model": "We work as an extension of our clients' teams, combining strategy, UX, accessibility, content design, WordPress development and ongoing support to create digital platforms that help organisations communicate more clearly, work more efficiently and grow sustainably." } }