Reading time: 12 minutes
How to use AI to create internal training step by step
Artificial intelligence applied to training doesn't require a technical profile or a multinational's budget. It requires a plan, source content, and a clear process: audit, define, create, measure, and scale.
You've been hearing about artificial intelligence applied to training for months. You've seen demos, read articles, and maybe even tried a chatbot. But when it comes to applying AI to your training program, the question remains the same: where do I start?
You're not alone. Many HR teams find themselves at the same point: they know AI can save them time and improve their content, but they don't know the first step. And that hesitation slows adoption more than any technical limitation.
The reality is that getting started with AI in training is simpler than it seems. You don't need a technology team, a special budget, or a master's degree in machine learning. You need to understand what content you have, what problem you want to solve, and follow an orderly process.
In 2025, 20% of enterprises in the European Union were already using AI technologies, according to Eurostat — a jump of 6.5 percentage points from the previous year.¹ In the specific area of L&D, 37% of companies now use AI as a learning technology, nearly double from 2024.² Adoption is accelerating, and teams that don't start now will have to do so in worse conditions a year from now.
In this article, we give you a practical guide with five steps for creating training with AI, from auditing your current content to measuring results. Designed for HR professionals who want to move from theory to action.
The question isn't whether AI will change corporate training. The question is what it costs to wait.
According to ATD (Association for Talent Development) research, producing one hour of eLearning with moderate interactivity requires between 73 and 154 hours of work using traditional methods.³ A 5-minute module can need between 6 and 13 hours of development. That means a small training team can spend weeks producing a single module — and by the time it's published, the content already needs updating.
Meanwhile, Fosway Group reports that only 53% of employees find the training their organization offers genuinely useful, and just 31% find it easy to navigate and complete training through their LMS.⁴ The problem isn't lack of investment. It's that the production model can't keep up with the demand for quality and frequency.
AI doesn't solve instructional design — that remains a human responsibility. What it solves is the production bottleneck: the time between "I have the content thought out" and "it's published, measured, and accessible in three languages."
Before choosing a tool or creating anything new, you need to know what you have. It sounds obvious, but most HR teams don't have a clear inventory of their training materials.
List everything you use for training: PDFs, presentations, manuals, recorded videos, onboarding guides, compliance documents, workplace safety procedures. Everything.
Once you have the inventory, classify each item according to three criteria:
| Criterion | Key question | High priority if... |
|---|---|---|
| Update frequency | How often does this content change? | Changes 2+ times/year (compliance, product, regulation) |
| Audience volume | How many people need to consume it? | Reaches 50+ people (onboarding, safety, processes) |
| Current format | Is it static, long, or hard to measure? | PDF, long PPT, or recording without traceability |
The question that should guide this audit is simple: what content do you update most often and reaches the most people? Start there. That content benefits the most from changing the production model.
What usually appears at the top of this audit: compliance and regulatory training, onboarding, workplace health and safety protocols, product updates, and standard operating procedures (SOPs). These are contents that update frequently, reach many people, and tend to be trapped in static formats — exactly what we call Document Inertia.
AI is not a generic solution. It can do many things, but trying to use it for everything at once is the fastest way for the project to stall.
There are three main uses of AI in internal training:
1. Create new content. Generate video modules from a script or existing document, create automatic voiceovers, produce multilingual content from the start. Useful when you need to produce content that doesn't exist yet or exists only in text format.
2. Refactor existing content. This is what we call Visual SOP Refactoring: taking existing documentation — manuals, presentations, guides — and restructuring it into video modules optimized for digital consumption. The platform analyzes the source document's heading hierarchy and content blocks, then restructures them into a modular script of 3–7 minutes preserving the logical flow of the source material. It's not "making a video from a PDF" — it's restructuring the knowledge architecture for visual consumption.
3. Measure and optimize. Analyze how training is consumed through traceability data — SCORM or xAPI packages that log viewing time per section, responses to interspersed questions, drop-off points, and results. Useful when you already produce content but don't know if it's working.
Our advice: choose one of the three and start there. If you have a lot of outdated content, start by refactoring. If you produce little content because the process is slow, start by creating. If you produce plenty but have no visibility, start by measuring. A focused pilot generates faster results that are easier to defend internally.
Once you're clear on what you need, it's time to find the tool. And this is where many teams get lost: too many options, too many promises, and few clear criteria.
If you don't have a technical profile, focus on five filters:
| Filter | Question | Why it matters |
|---|---|---|
| Autonomy | Can I use it without depending on IT? | If you need another team to create a module, the bottleneck moves but doesn't disappear |
| Language | Does it have support and interface in my language? | For an HR team in Spain, this isn't a minor detail |
| Traceability | Does it export in SCORM or xAPI? | Without granular consumption data, you're repeating the same mistake with more expensive technology |
| Multilingual | Does it enable automatic translation? | A company with teams in multiple countries needs to scale without multiplying costs per language |
| Compliance | Is it GDPR compliant? Does it have ISO 27001? | Corporate content contains sensitive information — security is a selection criterion, not an afterthought |
Traceability deserves special attention. A module without a SCORM or xAPI package is invisible to the LMS — it can't be measured, can't be audited, and doesn't meet the tracking requirements demanded by standards like ISO 9001, ISO 45001, or OSHAS 18001. If the tool you choose doesn't export in these formats, you're creating content that lives outside the learning management system.
This is the step where everything becomes tangible. And where many teams get stuck, because they feel they need to create something perfect from scratch.
You don't. The best starting point is a document you've already written. An onboarding manual, a compliance guide, a standard operating procedure. Something that already exists in text or presentation format and needs to reach many people.
The typical process:
1. Select the document. Choose something manageable: one topic, one process, one module. Don't try to convert all your training at once.
2. Review and adjust the script. AI generates the module from your text, but it's worth ensuring the content is current and well-structured before transforming it. A document with clear headings and defined sections produces a significantly better result than a plain text block — because the platform uses that hierarchy to segment the script.
3. Generate the module. Convert that script into a video with avatar, voiceover, and visual structure. What used to require an audiovisual production team now takes hours, not weeks.
4. Define metrics before launching. Don't publish without knowing what you'll measure. At minimum: completion rate, average viewing time per section, and drop-off points. If possible, add a knowledge assessment at the end — that gives you retention data, not just consumption data.
A study by University College London (UCL), one of Europe's leading universities in educational research, demonstrated that AI-generated videos match instructor-recorded videos in recall and recognition of content.⁵ This means the quality of the outcome doesn't depend on whether there's a person in front of a camera, but on whether the script is well-designed and the content well-structured.
The pilot isn't the end. It's the beginning of the cycle.
Compare the results of the AI-created module against the previous format. Did completion rate improve? Is the average consumption time reasonable? Do employees rate it better? Are operational indicators improving?
If the data is positive, adjust what's needed — duration, tone, structure — and scale to more content. If not, analyze why and try a different approach before expanding.
Scaling doesn't mean transforming everything at once. It means having a validated process you can replicate:
That cycle, once refined, is what transforms a training team into what we call Living Knowledge Infrastructure: a system that keeps content always updated, traceable, multilingual, and consumable — not a department that produces static documents nobody opens.
We've seen these mistakes across dozens of teams. They're easy to avoid if you know them in advance.
Wanting to automate everything at once. The temptation is strong: if AI can create modules quickly, why not transform all training? Because without validation, you also scale the errors. Start with one case, validate, and grow.
Choosing a tool by demo, not by problem. Demos impress, but the real question is: does it solve my specific problem? An HR team of 5 people doesn't need the same as an L&D department at a multinational. Evaluate by functional criteria — traceability, multilingual, autonomy — not by feature lists.
Not involving the training team from the start. If trainers feel AI is replacing them, they'll resist the change. AI doesn't replace trainers: it frees them from the production bottleneck so they can spend their time designing learning experiences, not wrestling with editing tools.
Ignoring traceability. Creating content with AI and not measuring its impact is the modern equivalent of uploading PDFs to an LMS and calling it done. If the module doesn't generate SCORM or xAPI data, you have no real visibility on whether the training is working.
Assuming AI doesn't need supervision. AI-generated modules need human review. Content must be accurate, aligned with company culture, and appropriate for the audience. AI accelerates production, but quality remains the team's responsibility.
The first step is looking at what you already have and deciding what problem you want to solve first.
AI applied to training isn't a digital transformation project that requires months of planning. It's an operational change that starts with a document, a tool, and a metric. The teams that get the best results aren't the most technical — they're the ones with a clear process: audit, define, create, measure, and scale.
If you have an onboarding PDF that nobody opens or a compliance manual you update three times a year, you already have the perfect starting point. AI is what turns that document into a traceable, translatable, measurable video module — without needing an audiovisual production team.
The bottleneck for corporate training is no longer budget or talent. It's the production model. And that model can be changed this week.
No. Current Visual SOP Refactoring platforms are designed for non-technical profiles. If you can use a presentation editor, you can create modules with AI. The learning curve is minimal.
It depends on the complexity, but a 5-minute video module based on existing text can be ready in 1–2 hours — even in minutes with the right template — including review. Without AI, the same content would require between 6 and 13 hours of development according to ATD data — and that doesn’t even include audiovisual production.³
No, and it shouldn't. AI automates content production — voiceover, video, translation, SCORM packaging — but instructional design, pedagogical strategy, and quality oversight remain human responsibilities. AI frees up time; it doesn't replace judgment.
Content that updates frequently (compliance, product, internal policies), content that needs to reach many people (onboarding), and content that must exist in multiple languages. TechSmith has documented that 83% of people prefer video for consuming instructional content.⁶ The more repetitive the production process, the greater the benefit of using AI.
It depends on the tool. Look for providers that comply with GDPR, hold security certifications (such as ISO 27001), and don't use your content to train their models. Data security should be a selection criterion, not an afterthought.
By comparing metrics before and after: completion rate, consumption time per section, drop-off points, and if possible, impact on operational indicators — incident reduction, evaluation improvements, changes in process execution. If the tool you use doesn't export SCORM or xAPI data, you can't get that visibility.
¹ Use of Artificial Intelligence in Enterprises 2025 - Eurostat ² 2025 Training Industry Report - Training Magazine ³ How Long Does It Take to Develop Training? - ATD / Kapp & Defelice ⁴ Digital Learning Realities 2024 - Fosway Group ⁵ AI-Generated Synthetic Video and Adult Learning Outcomes - UCL / Li et al. ⁶ Video Viewer Trends Report 2024 - TechSmith
@ 2026 Vidext Inc.
Newsletter
Discover all news and updates from Vidext
@ 2026 Vidext Inc.
