Why AI Projects Start With Content, Not Technology

Source page: https://www.feelcreated.com/insights/why-ai-projects-start-with-content-not-technology/

Structured JSON: https://www.feelcreated.com/insights/why-ai-projects-start-with-content-not-technology/ai.json

This page is generated automatically from the same reviewed FC AI data as the JSON version.

Meta

Type: insight_article

Language: en-GB

Last updated: 2026-10-16

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

Problems solved

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

Topics

Content focus

Canonical claims

Item 1

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

Item 2

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

Item 3

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

Item 4

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

Item 5

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

Item 6

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

Item 7

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

Item 8

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

Services

AI SEO services & AI search optimisation

Relationship: supporting

Url: /services/zero-click-ai-search-optimisation/

Website architecture & integrations

Relationship: related

Url: /services/website-architecture-integrations/

Digital planning & research

Relationship: related

Url: /services/digital-planning-research/

Information architecture

Relationship: supporting

Url: /services/information-architecture/

Content architecture

Relationship: primary

Url: /services/content-architecture/

Sections

AI projects often begin with the wrong question

Key points

Content

Item 1

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.

AI is only as useful as the information it can work with

Content

Item 1

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.

What do we mean by content in an AI project?

Items

Content

Item 1

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.

Good AI needs more than good writing

Items

Content

Item 1

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.

Why poorly organised content creates problems for AI

Items

Content

Item 1

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.

Structure gives AI more context

Items

Content

Item 1

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.

AI can expose existing content problems

Key points

Content

Item 1

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.

Start with the use case, then work backwards

Items

Content

Item 1

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.

What should you review before choosing the technology?

Content

Item 1

Text: The exact requirements will depend on the project, but there are several useful questions to ask before making significant technology decisions.

What information does the AI need?

Parent id: what-should-you-review-before-choosing-the-technology

Parent heading: What should you review before choosing the technology?

Content

Item 1

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.

Where does that information live?

Parent id: what-should-you-review-before-choosing-the-technology

Parent heading: What should you review before choosing the technology?

Content

Item 1

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.

Can the information be trusted?

Parent id: what-should-you-review-before-choosing-the-technology

Parent heading: What should you review before choosing the technology?

Content

Item 1

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.

How is the information structured?

Parent id: what-should-you-review-before-choosing-the-technology

Parent heading: What should you review before choosing the technology?

Content

Item 1

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.

Who owns the information?

Parent id: what-should-you-review-before-choosing-the-technology

Parent heading: What should you review before choosing the technology?

Content

Item 1

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.

What happens when the information changes?

Parent id: what-should-you-review-before-choosing-the-technology

Parent heading: What should you review before choosing the technology?

Content

Item 1

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.

How will you know whether it works?

Parent id: what-should-you-review-before-choosing-the-technology

Parent heading: What should you review before choosing the technology?

Content

Item 1

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.

You don’t need to fix everything before you start

Content

Item 1

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.

Better content foundations have value beyond AI

Items

Content

Item 1

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.

Build the information foundation before the AI layer

Content

Item 1

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

Information architecture

Information foundation

Content architecture

Structured content

Content governance

Metadata

Content

AI

Concept relationships

Item 1

Subject: Structured content

Predicate: enables

Object: reuse for filtering, search, comparison, structured data, integrations and AI applications

Item 2

Subject: Structured content

Predicate: benefits

Object: website search, filtering, reuse, and maintenance

Item 3

Subject: Technology choices

Predicate: are easier once

Object: information and use case are understood

Item 4

Subject: AI application

Predicate: uses

Object: foundations to provide a particular experience

Item 5

Subject: Context

Predicate: helps systems distinguish and connect

Object: related information

Item 6

Subject: Content ownership

Predicate: ensures

Object: ongoing accuracy of AI applications

Item 7

Subject: AI project

Predicate: requires

Object: understanding of information needs

Item 8

Subject: Structure

Predicate: identifies

Object: what information represents

Item 9

Subject: AI applications

Predicate: depend on

Object: reliable information

Item 10

Subject: Content quality

Predicate: influences

Object: AI output quality

Implementation patterns

Item 1

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.

Item 2

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.

Item 3

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.

Item 4

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.

Item 5

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.

Item 6

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.

Item 7

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.

Item 8

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

Item 1

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

Item 2

Guidance: Assess information trustworthiness by looking for contradictions, duplicates, missing information, and clarity on authoritative sources.

Importance: high

Item 3

Guidance: Identify where the required information lives, including CMS, documents, internal systems, databases, or third-party platforms.

Importance: high

Item 4

Guidance: Understand where the required information currently lives and whether it is in a form that can support the AI application.

Importance: high

Item 5

Guidance: Identify information that has a meaningful reason to be structured, rather than over-structuring every piece of content.

Importance: medium

Item 6

Guidance: Test the AI application against realistic questions and tasks, including missing, ambiguous, or multi-source answers.

Importance: high

Item 7

Guidance: Make decisions about which information can be trusted and how it will be maintained before implementing an AI tool.

Importance: high

Item 8

Guidance: Start with the intended experience: what questions should the AI answer, decisions support, or tasks help complete?

Importance: high

Risk areas

Item 1

Risk: AI-generated responses may sound convincing but be inaccurate if not tested against realistic questions

Impact: Misleading outcomes and potential harm to users

Item 2

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

Item 3

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

Item 4

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

Item 5

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.

Item 6

Risk: Inconsistent, outdated, duplicated or poorly organised content

Impact: AI systems may produce less reliable or contradictory outputs

Item 7

Risk: Choosing technology before understanding information needs

Impact: May lead to ineffective AI applications that do not meet user needs

Item 8

Risk: Content that is not structured or categorised

Impact: AI cannot easily identify and reuse specific information, limiting functionality

Common misconceptions

Item 1

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.

Item 2

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.

Item 3

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.

Item 4

Misconception: Structured content alone guarantees accurate AI outputs.

Clarification: AI applications still require realistic testing and ongoing information maintenance.

Item 5

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.

Item 6

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.

Item 7

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.

Item 8

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

Content quality attributes

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

Item 1

User problem: Poorly organised content creates problems for AI, such as contradictions, duplicates, and missing information

Evidence refs

Relevant sections

Item 2

User problem: AI projects often begin with the wrong question, focusing on technology rather than information needs

Evidence refs

Relevant sections

Item 3

User problem: Testing AI applications is challenging; realistic questions and tasks are needed to reveal gaps

Evidence refs

Relevant sections

Item 4

User problem: AI applications can become less useful over time if information is not maintained and updated

Evidence refs

Relevant sections

Content profile

Content type: Insight article

Primary topic: Building the information foundation before the AI layer

Supporting topics

Primary audience

Retrieval topics

Organisation

Name: Feel Created

Type: Digital partner specialising in websites and digital platforms

Url: https://www.feelcreated.com/

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.