Summary: Most organisations aren't short on training content. They're sitting on years of onboarding guides, SOPs, compliance modules, and recorded sessions that technically exist but practically don't work- wrong language, wrong format, or simply unreadable at the length it was built. MimirAI's Foundation layer is built to fix exactly that gap: turning content that already exists into content people can actually use.

In brief: 37% of organisations now use AI in training, up from 25% in 2024, and 52% of L&D teams say they're increasing their use of AI tools this year. Most of that investment is going toward new content. Very little of it is going toward the content already sitting unused on shared drives and legacy LMS folders, which is usually the larger and cheaper opportunity.

In This Article:

• The problem organisations don't realise they have
• How content becomes legacy without anyone deciding it should
• The accessibility gap that rarely gets named directly
• What leaving it unsolved actually costs
• What MimirAI's Foundation layer does about it
• The shift this makes possible

The problem organisations don't realise they have

Most organisations have a content problem, and it isn't a shortage. If anything, it's the opposite. Years of onboarding guides, process documentation, compliance modules, recorded sessions, SOPs, product manuals, and policy circulars accumulate across shared drives, intranet portals, and old LMS folders. The volume of institutional knowledge sitting in most organisations is genuinely significant.

Almost none of it is doing any work. It exists. People can't find it, can't use it, and in a meaningful number of cases, can't even read it in a format that matches how they actually learn. Ninety per cent of organisations report using a learning management system today, and 37% now use AI somewhere in their training programmes. Nearly all of that AI investment goes toward producing something new. Very little of it goes toward the content already paid for and already sitting there, unused.

How content becomes legacy without anyone deciding it should

Training material doesn't start out as legacy content. It starts as something genuinely useful: a well-designed module, a detailed process guide, a recorded session from a subject matter expert who spent an hour explaining something that took them years to actually understand.

What changes over time is the format. A PDF that made sense in 2018 is now a wall of text nobody reads past the first page. A video recorded for a previous version of a process still sits on the LMS, quietly teaching the wrong thing. A compliance module built for one geography gets assigned across three others that don't share its regulatory context.

The content doesn't become bad. It becomes inaccessible, and inaccessible content contributes nothing to the capability of the people who were supposed to learn from it, regardless of how good it once was.

The accessibility gap that rarely gets named directly

There's a dimension to this problem that gets less attention than it should, and it matters most for organisations operating across geographies or multilingual workforces.

Training content is almost always created in one language, usually the organisation's primary language, or whichever language the team that built the programme happened to speak. Everyone else gets a translated version, if they get one at all, and that translation is often done once and never touched again when the source material changes. Frontline workers, regional teams, and employees in markets where the organisation's primary language isn't the dominant one end up learning something that feels foreign before they've absorbed a single concept.

That gap is closing fast on the technology side. AI captioning now reaches 90 to 98% accuracy on clear audio in common languages, rivalling professional transcription at a fraction of the cost. Modern localisation platforms can re-render a full course into more than 175 languages without a single reshoot. The technology to close this gap has arrived well ahead of most organisations' actual practice of using it.

Accessibility for employees with different needs is an equally real gap, and it's usually treated as optional rather than baseline. Alt text for images, captions for video, transcriptions for audio, screen-reader-friendly layouts, these get built when someone remembers to ask for them, not as a default. The result is a learning environment that excludes part of the workforce before the content has even been engaged with.

What leaving it unsolved actually costs

When institutional knowledge sits locked in formats that don't work, the organisation pays for it in ways that compound rather than stay flat.

New joiners take longer than necessary to get up to speed because the documentation they need is hard to find, hard to read, or simply out of date. Experienced employees who leave take context and expertise with them that the organisation never captured in a reusable form. Teams in different geographies develop inconsistent practices because the training they received was itself inconsistent. And the subject matter experts who built the original content eventually stop being available, which puts the knowledge encoded in that content on a countdown nobody's tracking.

There's a quieter cost too, one that rarely makes it into a business case: the same content gets rebuilt more than once. A regional team that can't find or use the central version often just builds its own, in its own language, at its own level of detail, duplicating work that technically already exists somewhere else in the organisation. Multiply that across enough regions and enough content categories, and an organisation can end up funding the same training material three or four times over, without anyone ever seeing the total bill, because each rebuild gets logged as a separate, small, local project rather than one recurring, avoidable cost.

The cost rarely gets logged as a content problem. It shows up instead as slow onboarding, inconsistent quality, compliance gaps, duplicated production effort, and the recurring expense of rebuilding things that, in a very real sense, already exist.

What MimirAI's Foundation layer does about it

MimirAI's Foundation layer was built to solve this directly, and it goes considerably further than what most people mean when they say "updating" training material.

• AI-powered course summaries take dense, long-form content and generate concise, high-quality summaries that reduce cognitive load and improve retention. A forty-page compliance document becomes something a learner can actually get through.

• Interactive learning conversion takes static material, PDFs, videos, documents, and turns it into interactive modules with quizzes, flashcards, and branching scenarios, generated automatically. The underlying content doesn't change. How a learner engages with it changes completely.

• Multilingual content support provides AI-driven translation and localisation across more than twenty languages, so the same module reaches a team in one region and a team in another without a separate build for each, and without the quality decay that comes from a translation done once and never revisited. The global AI translation market has grown at close to 25% annually in recent years, evidence that this is now infrastructure, not a novelty add-on.

• Accessibility enhancement auto-generates alt text, captions, transcriptions, and screen-reader-friendly content to meet WCAG 2.1 AA standards. The learning environment becomes genuinely accessible, not nominally accessible on paper.

• Video intelligence and indexing applies AI-powered segmentation and chapter creation to existing video, with semantic search so a learner can jump straight to the segment relevant to what they need instead of watching a full recording to find one answer. Producing training video the traditional way runs roughly $4,500 per finished minute; AI-assisted production has brought that figure down to around $400, a gap large enough to decide whether a piece of content gets refreshed at all or simply left to rot.

• Automated skills tagging generates metadata linking course content to a skills taxonomy and competency framework, enabling precise learning path recommendations and making it possible to see, at a glance, which content addresses which capability gaps.

None of this requires touching the LMS an organisation already runs, or replacing content wholesale before any of it has proven its worth in the new format. The Foundation layer works against whatever content already exists, in whatever state it's currently in, which matters practically: a modernisation effort that demands a clean starting point before it can begin rarely gets past the planning stage, because very few organisations actually have one.

The shift this makes possible

Content modernisation changes the relationship between an organisation and the institutional knowledge it has already built. Material that was sitting unused becomes a working part of the learning environment. Expertise trapped in documents nobody reads becomes something people can actually engage with.

The organisation doesn't need to rebuild its content library from scratch. It needs to turn what it already has into something that works for the people who need to use it, in the language they speak, in a format they can access, at the level of detail their role actually requires.

That's a genuinely different starting point than how most organisations approach content management, and it's the one that finally makes the existing investment in institutional knowledge pay off.

If your organisation is sitting on years of training content that technically exists but practically doesn't work, talk to Tarento about MimirAI.