It sounds reasonable, and it gets said in every other tool selection: for each job, the tool that does it best. An authoring tool for your own courses. A course library for standard topics. An AI video tool for anything that should look like video.
Best of breed. Every vendor is strong in its category, every category has its reason to exist, and nobody has to compromise on features they don't need.
In practice, it does not hold up. Not because the tools are bad, but because the idea contains an assumption that does not apply to learning content.
Why the idea is convincing
Because it is right elsewhere. An accounting system does not need to track time. A CRM does not need an e-signature module. Choosing specialists there and connecting them through interfaces beats a suite that does everything halfway.
And the three categories really do solve different problems. Articulate and Easygenerator are built so that subject-matter experts can create well-structured courses themselves, and they do that well. Where each reaches its limit is covered in the posts on Articulate 360 and Easygenerator. GoodHabitz and LinkedIn Learning deliver ready-made content at a breadth no internal team would ever produce. HeyGen and Synthesia generate talking avatars at a quality that would have needed a studio three years ago.
So this is not a naive belief. It is a rule that works in one place, carried over to another.
Where it breaks with the authoring tool
A classic authoring tool takes the production off your hands, not the work. Script, structure, instructional design, slides, narration: all of that is still done by hand, in your weeks rather than the machine's. The tool makes the result clean. It does not make the road there any shorter.
That is why a company-specific training typically takes four to six weeks, and most of that time is not production but coordination, input from the business and waiting. With external production, price does the sorting as well: one hour of finished e-learning can cost up to 40,000 euros (market benchmark, Raccoon Gang, 2026). The full breakdown is in what a training video costs.
The bottleneck is not the tool. It is the effort per piece of content, and a better authoring tool only moves that by a few percent.
Where it breaks with the course library
Catalogues are convenient, and convenience is a real value. Pick a course, assign it, done. No project, no approval loop, no internal wrangling over who owns the content.
But the content stays generic. It does not know your equipment, your systems or your terminology. For a communication course, that is tolerable. For an instruction on a machine you run differently from how the manufacturer intended, it is the difference between a record and an effect. Only a quarter of learners complete classic mandatory courses (Continu, 2025), and that is not a motivation problem. People abandon content that is obviously not about their workplace.
You can see the result in every L&D team that has both. The catalogue covers breadth, and the company's own topics still get built in-house. The library did not replace the effort. It added to it.
Where it breaks with the AI video tool
This is the break that is easiest to miss, because the output looks so good.
An avatar video is a video. A training is a video plus learning objectives, a knowledge check, evidence of completion, deadline logic and a version that can be updated when the policy changes. The second part stays with you, usually in the LMS, usually by hand. What each tool delivers and where it stops is covered in the post on Synthesia as an alternative.
And there is a point that hits with every update. When one paragraph of a policy changes, a pure video tool means regenerating the video, maintaining the quiz separately and pushing the package into the LMS again. Three systems, three manual steps, for one changed line.
The seam is the real cost
The seam is the point where content moves from one system to the next. From the interview into the authoring tool, from the authoring tool into the video tool, from there into the LMS. Three systems mean three of these handovers.
Up to here, it looks like three separate limitations. Taken together, a different picture emerges, and that is where the idea goes wrong.
Best of breed works when systems are connected through data. Learning content is not data in that sense. It is knowledge, and knowledge does not connect through an API. A course completed in the catalogue does not make your authoring tool any smarter. A training built in the authoring tool does not make your video tool any smarter. What your experts explained in interviews and review rounds ends up as a slide in a course and is lost to the rest of the organisation.
Add the plain overheads of the construction:
- Three contracts with three terms and three renewal dates
- Three admin processes, three permission models, three data processing agreements
- The switching between tools itself, which costs weeks and appears in no quote
- Two or three rounds of training for your own staff, because every tool works differently
- A repository per system, where nobody can say for sure which version is the current one
It also helps explain why 52 percent of companies in the German-speaking market still have no digital learning platform (Masterplan/Amadeus Fire, 2025), a pattern that looks similar across much of Europe. Not out of conviction, but because what they are offered comes in three parts and nobody gets a budget for the assembly.
What holds instead
One system that covers all three categories, rather than three systems that each cover one. Omnora calls it the AI Learning Factory, and the name describes exactly that: not another tool in the stack, but a production line from what the expert says to the finished training. Standard trainings like those in a catalogue, adaptable to your equipment, systems and terminology with a click or a short prompt. The full scope of a classic authoring tool, without the manual work. AI avatars and video at the same quality, plus quiz, SCORM, completion evidence and updates in the same process.
The everyday difference is not the feature list but the effort per piece of content. An AI-guided interview takes around 30 minutes of an experienced person's time and asks follow-up questions wherever a statement stays incomplete. The training format is built from that, with typically up to 80% less production time per training hour, and for one mandatory training in under 24 hours from start to rollout. Both figures are experience values, not promises. The process itself is explained in the article about AI expert interviews for knowledge capture.
The part that never shows up in tool comparisons is the most important one. Because the knowledge for the interviews comes from your experts' heads, every training also leaves behind verified company knowledge that stays in the organisation. Panopto and McKinsey put the undocumented share of job knowledge at 42 percent, and that is the portion that becomes tangible here for the first time. It then carries not only the training but also a colleague's question a year later and the AI application you have not built yet. Why single points of knowledge hinge on this is covered in the post on single points of knowledge.
When best of breed still wins
There are cases where the specialist tools remain the right choice, and they should not be argued away.
If you produce three or four trainings a year and otherwise just tick off mandatory records, no platform pays off against an inexpensive authoring tool. If ninety percent of your needs are soft-skill topics, communication, leadership, self-management, a good catalogue will be stronger on content than anything you would build yourself, and adapting it to your terminology adds little. And if you need one high-end marketing video, a pure video tool is the shorter route.
So the idea is not wrong, just applied too broadly. It holds as long as content is created one piece at a time and may stay that way.
As soon as you turn your own expertise into training, by the dozen rather than as an exception, the calculation flips. The question in procurement is then no longer which tool solves its category best. It is how many systems your knowledge has to pass through before it reaches an employee, and what gets lost on the way every single time.
