Why engineers wouldn’t accept it, but learning teams often do.
Imagine hiring an engineering firm to design a bridge.
You ask a simple question.
“How long will the design take?”
The lead engineer replies,
“As long as we have.”
You’d probably hire another engineering firm.
Because no serious profession treats time that way.
Engineering works toward constraints.
Medicine works toward urgency.
Aviation works toward precision.
Construction works toward milestones.
Yet in Learning and Development, something different often happens.
A stakeholder asks,
“How long will it take to build this course?”
The answer might be:
“About twelve weeks.”
Or fourteen.
Or sixteen.
The next question is almost never asked.
Why?
Not why twelve weeks.
Why does it need twelve weeks at all?
This is Parkinson’s Law.
British historian Cyril Northcote Parkinson observed that work tends to expand to fill the time available for its completion.
Most people interpret this as a productivity problem.
I think it is something more significant.
It is a diagnostic problem.
Because the timeline itself often becomes the assumption nobody questions.
Over time, organizations stop estimating work.
They inherit timelines.
“Leadership programs take six months.”
“eLearning takes twelve weeks.”
“Curriculum redesign takes a year.”
These statements begin to sound like facts.
They aren’t.
They’re habits.
Imagine two organizations building the same learning solution.
Organization A gives the team sixteen weeks.
Organization B gives the team six.
Will the sixteen-week solution always be better?
Not necessarily.
One team may spend weeks polishing animations that have little impact on performance.
The other may spend its limited time working directly with managers to ensure learners actually apply the new capability.
The difference isn’t effort.
It’s judgment.
This is where learning leaders should borrow a standard from engineering.
Engineers don’t ask,
“How long do we usually take?”
They ask,
“What constraints must we work within while still achieving the required outcome?”
The outcome drives the design.
Not the calendar.
Learning should be no different.
This doesn’t mean rushing work.
It means questioning assumptions.
Every additional week should earn its place.
Not simply exist because previous projects took that long.
Artificial intelligence makes this even more important.
AI can now draft learning objectives.
Generate scenarios.
Create assessments.
Produce first versions of storyboards.
Summarize SME interviews.
Translate content.
Review tone.
Suggest improvements.
Many tasks that once required days now require hours.
Yet some organizations continue using exactly the same development timelines.
Why?
Because the process changed.
The assumptions didn’t.
AI hasn’t exposed a technology problem.
It has exposed a thinking problem.
If work expands to fill the time available, then AI creates a new responsibility.
Learning leaders must decide which activities deserve human judgment and which activities no longer do.
That is not an AI decision.
It is a capability decision.
This is why reducing development time should never be the goal.
Improving decision quality should.
Sometimes a project genuinely requires twelve weeks.
Sometimes six.
Sometimes two.
Professional judgment determines the difference.
Not inherited timelines.
The organizations that gain the greatest advantage from AI will not necessarily build courses faster.
They will make better decisions about where human expertise creates value.
Everything else becomes a candidate for automation.
That is a much more strategic conversation than asking whether AI can build eLearning.
It asks something far more important.
What work still requires professional judgment?
Parkinson’s Law reminds us that organizations often waste time because no one questions the assumptions surrounding it.
Learning leaders should ask a different question.
If we had half the time…
What would we stop doing?
More importantly…
Would anyone notice?
If the answer is no, that activity probably wasn’t creating capability in the first place.
Professionals don’t earn credibility because they spend more time.
They earn credibility because they know where time creates value…
And where it doesn’t.
Reflection
Before approving your next learning project, ask:
- Which parts of this timeline are based on evidence rather than tradition?
- What assumptions have we inherited without questioning?
- If AI completed the routine work tomorrow, where would human judgment still be essential?
- Which activities genuinely improve workforce capability?
- If we had half the time, what would we stop doing?
The goal is not to build learning faster.
The goal is to make better decisions about where time creates value.
Dr. Ravinder Tulsiani is a Workforce Capability and AI Readiness Advisor. Through L&D Simplified, he helps learning leaders think more clearly, diagnose more accurately, and make better capability decisions in an AI-enabled world.
