If you work in Learning & Development, you’ve almost certainly encountered this reaction from outside the profession:
“Surely modern tools make e-learning quick to produce now?”
On the surface, it feels like a fair question. After all, the tools we use today are dramatically more capable than the ones many of us started with 10 or 15 years ago. Add AI into the mix and it’s easy to assume that development times must have collapsed.
But interestingly, the reality doesn’t seem quite so straightforward.
The surprising persistence of development time
Recently, while sorting through some old folders on my computer, I rediscovered a well-known 2010 report from Chapman Alliance about e-learning development times.
The headline figure from the report was striking. At the higher end of complexity, organisations reported spending roughly 184 hours of development time for every one hour of finished e-learning.
Importantly, that figure included the whole project effort:
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instructional design
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SME input
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media production
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quality assurance
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project management, and
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review cycles
Not just authoring time.
Revisiting those numbers made me wonder whether they still held true today. After all, since 2010 we’ve seen huge changes in authoring tools, media production, collaboration platforms, accessibility tooling and, more recently, AI-assisted workflows
Surely development time must now be dramatically lower?
Yet when you look at more recent industry benchmarking and practitioner data, the modern estimates for complex interactive e-learning often still sit somewhere between 80 and 150 hours per finished learning hour.
Lower than 2010? Yes. But perhaps not lower in the way many people might expect.
The “more powerful, not necessarily faster” effect
I suspect something slightly counterintuitive has happened over the last decade or so. The tools haven’t simply made development faster. They’ve made it possible to produce more sophisticated output within a similar amount of time.
In other words, the capability has increased more dramatically than the efficiency.
Think about tools like Storyline. Many of the core production mechanics still involve significant manual effort:
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building interactions
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configuring triggers
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managing layers and states
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testing logic
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refining navigation
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checking accessibility, and
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iterating through reviews
The interface may have improved. Certain workflows may be smoother. But sophisticated interactive e-learning still involves a considerable amount of detailed point-and-click production work. And interestingly, improved tooling can sometimes encourage greater complexity rather than faster production.
Features that once felt too difficult or time-consuming suddenly become achievable, which naturally encourages designers to create richer, more ambitious experiences.
The result? More sophistication. But not necessarily dramatically less labour.
AI is helping — but mostly around the edges
Of course, AI is already speeding up parts of the workflow. It can help with outlining content, drafting objectives, generating ideas, summarising SME input and producing first drafts
All genuinely useful. But most current AI tools still don’t remove the deeper production effort involved in:
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building interactions
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implementing design decisions
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governance and stakeholder review
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testing and refinement, and
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organisational approval cycles
And in larger organisations, those review and governance layers can account for a substantial proportion of total project time.
So while AI may improve efficiency in certain areas, it hasn’t yet fundamentally transformed the overall production reality of high-quality interactive learning.
What's the significance of this?
I think this creates a real tension for many L&D teams.
Externally, there is often an assumption that modern technology should make production almost instantaneous. Tool vendors don’t always help here either, with messaging that suggests sophisticated learning experiences are quick and effortless to create.
Internally, this can leave L&D professionals feeling that they are somehow inefficient or behind the curve.
But perhaps the more realistic conclusion is this:
Creating genuinely thoughtful, interactive, well-crafted e-learning still requires a significant amount of human judgement, design thinking and production effort.
The technology has become more powerful. But the craft itself (and the labour behind the craft) still matters.
And perhaps that shouldn’t surprise us quite as much as it does.
A fuller exploration of these ideas — including the wider implications for e-learning production, AI and workflow support — can be found in the original Learning Re-Framed article here:
