The design work happens
before you open Storyline.

Separate the requested training from the actual gap: Put the request on one card and the observed performance problem on another, then look at the distance between them. Most intake conversations arrive as a solution already chosen, and the first useful act of design is to reopen the question of what problem is being solved.

Map the causes before you accept the brief: Draw the suspected causes under the gap: knowledge, skill, process, tooling, incentives, workload, feedback, or a manager rewarding the opposite. Only the first two are training problems. When the arrows land somewhere else, you have the evidence for a conversation that saves the budget.

Profile the learners and their real conditions: Prior knowledge, job context, how much uninterrupted time they genuinely have, what device they are on, and what happens if they get it wrong at work. A module designed for forty focused minutes fails when the audience gets seven minutes between calls, and that constraint belongs on the board early.

Write the finding down where the sponsor can see it: A needs analysis that lives in your head is a hunch. The same analysis on a shared canvas, with the causes drawn, is a design argument. Share a view-only link and let the sponsor follow the reasoning rather than take your word for the conclusion.

Use a verb you can observe: Understand, know, be aware of, and appreciate cannot be seen and therefore cannot be measured. Identify, classify, calculate, troubleshoot, and demonstrate can. Bloom's taxonomy is useful here as a verb ladder: pick the level of thinking the job actually requires rather than defaulting to recall because recall is easy to quiz.

Add the condition and the criterion: A complete objective says what the learner will do, under what conditions, and to what standard. Given a customer complaint and the refund policy, decide whether a refund applies, correctly in eight of ten cases. That sentence tells you what to teach, what to practise, and what the assessment must look like.

Align objectives, practice, and assessment as a single row: Put each objective at the top of a column with its practice and its assessment beneath it. Misalignment is the most common design defect in corporate e-learning: a module that teaches judgement and assesses recall of the policy text. On a canvas the mismatch is visible in the layout.

Cut the objectives that serve nobody: Every objective costs learner time. If nothing changes at work when it is met, it is content that survived because someone senior likes it. Marking those on the board makes the scope conversation a shared one instead of a private compromise.

Chunk against cognitive load, not against slide count: Working memory holds very little at once. Intrinsic load comes from the difficulty of the material, extraneous load comes from how you present it, and only the second is under your control. Splitting a dense module into shorter segments with a practice point in each one reduces the load that comes from your design.

Design practice first and content second: The reliable finding in the field is that retrieval beats review. A learner who attempts a scenario and gets feedback remembers more than one who watched a better explanation. Sketch the practice activities before the content, then add only the content the practice requires.

Step the difficulty on purpose: Worked examples early, partially completed problems next, independent practice last. Dropping learners into an unscaffolded scenario in module one produces failure that teaches nothing. On the canvas you can see whether the difficulty curve exists or whether everything is flat.

Decide what the module deliberately excludes: Scope creep in e-learning arrives as one more thing they should probably know. Writing the exclusions on the board turns each addition into a visible trade against learner time rather than a silent extension of the runtime.

Turn a rough gap statement into draft objectives: Describe the performance problem and the audience and the AI proposes observable objectives with conditions and criteria. They will need editing, and editing a draft is a great deal faster than facing an empty objectives document at the start of a project.

Ask it to find the alignment holes: Because the AI has the entire canvas as context, you can ask which objectives have no practice, which assessment items test something never taught, and where a module is content heavy with no retrieval. Those are exactly the defects a pilot would surface three weeks later.

Reshape the sequence without rebuilding it: Move a module and its storyboard panels, notes, and assessment move with it. Trying a different structure costs a drag rather than a rewrite, which is what makes it realistic to test two sequences instead of defending the first one.