Or perhaps you’ve read a post on LinkedIn or been to a conference and heard someone say ‘We don’t need anymore content’.
It is a strange contradiction that appears in many organisations. Teams talk about needing more content while having purchased libraries full of courses, videos, documents, and training materials that already exist.
The problem is rarely the creation of content. The problem is managing it.
It will have been procured from different sources and even departments. You might do a tour of your organisation and even find that you’ve got more than one instance of the same LMS or content library.
Over time the content landscape becomes fragmented. Valuable training resources exist, but they rarely form a coherent system.
This is where learning content chaos quietly takes over. Managing it becomes a project and its becomes at the start, people rarely think about content management.
It does not happen because people are careless. It happens because organisations create and buy content continuously but rarely invest the same energy into structuring, organising, and managing what they already have.
Think about your own digital devices and how hard it is to find your own digital files, that image you try to get amongst the thousands of digital images you’ve got spread across phones, tablets and computers.
Eventually the platform becomes less of a learning system and more of a digital dumping ground. It’s easy to see why people give up, especially if you don’t have an effective schema or data model for people to use.
Several industry studies highlight how frequently learning content inside corporate platforms goes unused:
In corporate training environments, online course completion rates can fall as low as 4%, highlighting how many courses inside learning platforms remain unused or undiscovered. (Training Industry)
Traditional long-form elearning courses often see completion rates of around 20%, showing how large course libraries can contain significant volumes of content that employees never complete. (eLearning Industry)
Well-designed digital training programmes have achieved course completion rates of up to 72%, demonstrating how course structure and delivery can dramatically influence whether learning content is actually used. (Training Industry)
Microlearning courses are typically structured as modules under 10 minutes, making them easier to organise, discover, and complete within large learning libraries. (eLearning Industry)
Microlearning courses can improve learning efficiency by around 17%, as shorter modules reduce cognitive overload and help employees engage with training more consistently. (eLearning Industry)
The Illusion That Organisations Need More Content
A common theme in learning and development conversations is the belief that organisations simply need more content.
In practice, the opposite is often true. Most organisations do not actually know what content they already have. When was the last time your organisation actually did an audit? If you worked with physical products you’d been doing regular stock audits.
Scott Hewitt reflects on this point directly:
*”I started noticing the issue when speaking to L&D directors who clearly had plenty of content but were still talking about poor user experience and people not being able to find training. The conversation is often framed around data, but in reality it is a data management problem.”*
His experience working in a completely different field offered an interesting comparison.
*”It actually reminded me of my time as a picture researcher. When we tagged images, each one needed hundreds of relevant tags so people could find them later through search.”*
Without a clear content strategy, training is frequently developed on a one-off basis. A new initiative appears, a team commissions a course, the training is produced, and it is uploaded somewhere inside the organisation.
Once that project finishes, attention moves on.
Months later someone asks whether a similar course already exists, and no one can quite answer the question. The record of what was built, who it was built for, and where it lives has gradually faded.
The content has not disappeared.
It has simply become invisible.
This is why many learning platforms slowly drift toward chaos. Content is continuously added, but very little attention is given to how it will be organised, surfaced, or reused later.
The Digital Dumping Ground Problem
The same pattern appears in almost every type of digital repository.
Teams place valuable files into shared systems with the intention that everyone can access them. Over time the number of documents grows, the structure becomes inconsistent, and eventually people stop searching altogether.
Instead they recreate what already exists.
Document templates provide a familiar example. An organisation may have carefully designed templates for reports, presentations, or project documentation. Yet employees frequently build their own versions simply because they cannot locate the original files.
The templates exist.
They are just buried inside the system.
Learning content behaves in exactly the same way.
An organisation may already possess excellent training resources, but those resources remain unused because they cannot easily be discovered.
This is why a content audit can be such a powerful starting point. Before commissioning new training, it is often worth asking a much simpler question.
What do we already have?
Many organisations never perform this exercise. They focus on the next learning initiative instead of first mapping the existing landscape.
As more training materials are created, the challenge only grows. How many times do people actually ask ‘Have we already got this?’.
Scott Hewitt raises another reason why these audits rarely happen.
*”One of the reasons organisations rarely audit their content is that they simply do not have the resource, but more importantly it is not usually part of their strategy or professional background.”*
He argues that this challenge is partly cultural.
*”L&D teams often do not think in terms of digital strategy, yet they are managing digital platforms. That means the thinking needs to borrow from other digital disciplines.”*
In his view, common advice such as “think like a marketer” does not go far enough.
*”I am not convinced by the phrase ‘think like a marketer’ because it is vague. In this case it is more about thinking like a digital marketer or even thinking like an SEO specialist.”*
Content Creation Is Accelerating Faster Than Content Management
Although many people argue that organisations should create less content, the reality is that digital learning continues to expand rapidly.
Every new course, video, document, or microlearning resource adds another object to the learning ecosystem.
Without a structured approach to content management, the system gradually becomes harder to navigate.
This is where organisations often misunderstand what content management really means.
It is not just about folders and file names.
Those things matter, but they are only the surface layer.
The real structure of a learning system sits inside the data attached to each learning object.
Why Metadata Matters More Than Folders
Every course or learning resource contains information that describes what it is.
This information may include the course title, learning objectives, topic area, category, audience, format, or difficulty level. Collectively, this information forms the metadata that platforms rely on to organise and surface content.
Scott Hewitt summarises the issue in simple terms.
*”Metadata is the layer that makes search work. Most people never think about how search actually functions inside a platform, but if you want deeper search, filtering, and category grouping, you need more than just a title and description.”*
He argues that this raises a much larger strategic question.
*”That sounds basic, yet it raises the bigger question of why organisations need to think about a data strategy around their learning content.”*
When that metadata is structured properly, the platform becomes far easier to use.
When it is inconsistent or incomplete, the system struggles.
Many learning platforms rely heavily on simple keyword searches. If the only searchable information is a title or short description, the results can be unpredictable.
A user may search for cybersecurity training and receive only a handful of courses, even though many more relevant resources exist within the platform.
The system cannot surface them because it does not understand what the content actually represents.
Platforms that support stronger metadata structures behave very differently.
When courses are categorised consistently and supported by structured data, search results become clearer and far more useful.
The experience begins to resemble browsing an online store.
The Search Experience Matters
Consider how people search for flights when planning a trip.
If someone wants to travel from London to New York, they expect to filter the results by date, airline, departure time, and seat class. Each step refines the search until a manageable set of options appears.
Now imagine a booking system that simply returned every flight containing the word “New York.”
The experience would be frustrating and inefficient.
The most basic learning platforms work in exactly the same way. You want to be working with a learning platform that enhances the learning experience and delivers an enhance search.
Without strong categorisation and metadata, the user journey breaks down. Employees cannot filter or refine their searches, so the content library begins to feel chaotic.
This problem becomes even more visible when organisations attempt to personalise learning experiences.
Personalisation Depends on Good Data
Large organisations often want different groups of employees to see different types of content.
Customer-facing staff may require operational training. Managers may need leadership development programmes. Compliance courses may apply only to certain departments or regions.
Delivering these experiences relies heavily on the metadata attached to each learning resource.
If the content is categorised consistently, administrators can easily surface relevant courses to specific user groups.
If the data is inconsistent, personalisation becomes difficult and time-consuming. This is why you can use AI to help with your tagging. If you have categories, objectives and a schema then an AI model can help with not only automating your tagging but also improving the consistency.
Scott Hewitt also sees AI as a practical solution to the variability that comes from human tagging.
*”AI can help here because human tagging inevitably creates variation. If several people tag the same course, they will all interpret it slightly differently.”*
He notes that properly implemented AI systems introduce two key advantages.
*”When AI tagging models are tested and locked down properly, they introduce two major benefits. The first is speed. The second is consistency.”*
And that consistency has a direct impact on the user experience.
*”When multiple people use the same model, the tagging becomes more consistent, and that consistency improves the quality of search results.”*
The end result is that you get better search results that multiple humans tagging and each tagging in a different way.
In some cases organisations find themselves manually re-tagging content simply to make the platform usable.
The Human Tagging Problem
Historically, categorising learning content has always been a human task.
Someone reviews a course and decides which categories or tags seem appropriate. Another person performs the same task next week but interprets the content slightly differently.
Neither person is wrong.
They are simply applying their own judgement.
The difficulty is that these small differences accumulate.
If forty courses are tagged by different people over time, the results often look broadly correct but lack consistency. That inconsistency gradually affects search results, filtering, and personalisation.
The platform cannot reliably surface everything it should because the underlying data is not structured in a predictable way.
Where AI Categorisation Can Help Content Management
This is where prompts and automated agents can introduce a valuable form of discipline.
If a categorisation prompt is carefully designed and tested, organisations can process batches of courses through the same logic every time. The result is not perfect automation, and human oversight still matters, but it introduces something extremely important.
Consistency.
Instead of relying on individual judgement each time a course is tagged, the same classification logic is applied repeatedly across the entire content library.
This creates a far more standardised structure.
When someone searches for “cyber”, the system is far more likely to return cybersecurity courses consistently because the tagging logic has been applied in the same way across all content objects.
That consistency directly improves search quality and filtering.
It also becomes especially valuable when organisations manage content across multiple platforms or integrate materials from multiple suppliers. Consistent tagging allows the library to behave like a unified collection rather than a group of disconnected files.
The result is a far better user experience.
The Buyer’s Perspective
For organisations purchasing off-the-shelf learning content libraries, the same principles apply.
When evaluating a supplier, it is worth looking beyond the course catalogue and examining how the content is structured behind the scenes.
If searching for a topic such as cybersecurity returns clear and comprehensive results, the metadata is likely well organised.
If the results appear fragmented or incomplete, the underlying structure may not be strong enough to support discovery.
Both the platform and the data attached to the content determine the final experience.
Even the most advanced learning platform will struggle if the metadata behind the courses is inconsistent.
Strong content management practices are often what separate useful learning libraries from ones that feel impossible to navigate.
Working With Existing Taxonomies
In some situations organisations may need to introduce additional categories that reflect specialised internal needs.
Certain industries or business functions require training that sits outside common content taxonomies.
However, it is usually wise to begin by working with the existing categories provided by a content library. Off-the-shelf content is typically designed around widely recognised training themes.
Once that structure is understood, organisations can decide whether additional tagging is required.
In some cases companies may also need to align a new content library with the existing data schema inside their LMS.
Although this can appear complex at first, careful mapping between category structures usually solves the problem.
Content Strategy Cannot Ignore Content Management
Creating learning content is only one part of the equation.
Ensuring that content remains discoverable, usable, and relevant over time is equally important.
Without a clear approach to content management, even the best training materials can quietly disappear inside the system.
This is the irony behind many learning platforms. The organisation may already possess exactly the knowledge it needs, but the people who run the platform don’t know what’s in it, nor do they know how to search it.
What it lacks is the structure that allows that knowledge to be found.
Questions and Answers
Q1: Why can’t employees find training in their LMS?
Many organisations have plenty of courses but poor organisation. Content is spread across LMS platforms, SharePoint, and internal systems. Without clear metadata, categories, and tagging, search results become unreliable, making useful courses difficult for employees to discover.
Q2: Why do organisations keep creating training that already exists?
Teams often build courses for new initiatives without checking existing content. Over time, organisations lose track of what has been created and where it is stored. This leads to duplicate courses being produced even though similar training already exists in the system.
Q3: Why is content management important for learning platforms?
Content management helps organise courses using structured metadata, categories, and tags. When this information is consistent, learning platforms can surface relevant training more easily. Without it, even good courses become difficult to find and the library starts to feel chaotic.
Q4: How can organisations improve course discovery in their LMS?
Improving metadata and categorisation is key. When courses are consistently tagged by topic, audience, and learning objectives, search and filtering become far more useful. This allows employees to find relevant training quickly instead of recreating courses that already exist.
