The biggest challenges in corporate training production

When a corporate training is “ready,” it’s usually no longer today’s reality: the process has changed, the product has been updated, and the very same sentence has started being misunderstood in another department. This always reminds me of Borges’s idea of the “map”: when the map of the empire is at the same scale as the empire, the map is no longer useful (Borges, “On Exactitude in Science”, 1946). Organizations sometimes produce content that is so big, so heavy in an attempt to capture reality that the content ends up lagging behind reality.

That’s where my work begins: the moment you accept that what we call training production is not “writing,” but keeping the flow alive.

1) The invisible wall in classic training production: not “content,” but friction

When an academy manager or a corporate trainer sits down at the table, the first problem is usually not the topic. There are plenty of topics: sales, leadership, HSE, GDPR, product, process… The problem is this: while topics are abundant, time is scarce—and most of that time goes not to learning, but to friction.

The friction clusters I see most often in the field:

• Brief ambiguity: Someone says “Let’s do this training,” but the target behavior isn’t clear. The question What will they do differently? hangs in the air. Then the training becomes “informational”; and informational content is often unmeasurable.
• Approval loop: More time is spent on “who needs to see this” and “who has the final word” than on producing the content. There’s an interesting human inconsistency here: the same manager says “let’s ship fast,” and then gets stuck for three days on a single word at the last minute. I still haven’t completed the model for this; a word is sometimes risk, sometimes identity.
• Freshness debt: Training starts aging the day it’s published. Especially in areas like HSE and GDPR, even a “small” update can have major consequences. But updating is often perceived as “re-shooting,” so it gets postponed.
• Single-format obsession: Trying to solve everything with long video or a long slide deck. But types of knowledge differ: procedure, decision, reflex, language… Not all of them like the same format.
• Distribution load: Even if the training is ready, the questions “to whom, when, how” turn into a separate project. The training producer suddenly evolves into a campaign manager.
• Measurement illusion: Saying “completed” doesn’t mean learned. Ebbinghaus’s forgetting curve tells us this: without repetition and retrieval, knowledge evaporates quickly (Ebbinghaus, 1885). Organizations can measure the vapor and assume “there’s air.”

“What gets measured gets managed.” [Attributed to Peter Drucker]
In corporate training, this sentence sometimes works in reverse: if you measure the wrong thing, you manage the wrong thing.

Let me make a small correction here: whether this quote belongs to Drucker is disputed. But its reality in corporate life is indisputable; everyone uses it—and then assumes what they measured is “learning.”

2) The academy manager’s diary: looks like an assembly line, but is actually a living organism

Classic training production in most organizations is designed like an assembly line:

1. Collect needs
2. Write content
3. Design
4. Shoot / produce
5. Publish
6. Report

This line works in a factory. In learning, it often breaks down—because learning is not a “finished product,” but behavior changing in context.

Typical contradictions an academy manager faces:

• Standardization vs. context: “The same training for everyone” looks manageable; but the same sentence that works in sales can be misunderstood in production.
• Expert language vs. employee language: The subject-matter expert speaks correctly; the employee doesn’t understand correctly. Both are right. The bridge between them usually lands on the trainer’s shoulders.
• Speed vs. accuracy: Especially in product updates, speed is needed; in compliance topics, accuracy. The same team is expected to have two opposite reflexes.
• Training vs. communication: Are we making an announcement or building a skill? Organizations often confuse the two. Result: the training platform turns into a nicer version of a long company email.

There’s a scene Saadet (I sometimes call her the “We’ll Handle It Specialist”) often catches: the training team relaxes saying “we finished the content,” then operations comes in with “who are we assigning this to?” and the work starts again. The content is finished; the process isn’t. That’s the exhausting part.

3) The three biggest technical challenges: producing, updating, proving

In corporate training production, I can summarize the biggest challenges with three verbs:

3.1 Producing: the cost of writing from scratch

Organizations already have knowledge: PowerPoints, procedure documents, trainer notes, policy texts. But because these aren’t converted into a “training” format, they’re treated as if they don’t exist. Writing from scratch every time exhausts organizational memory.

3.2 Updating: fear of “re-shooting”

Video content production is heavy in the classic world. Because of that heaviness, updates get postponed. Every postponed update eventually falls into the category of “don’t touch it anymore, it’ll break.” Content becomes sacred; but knowledge is alive.

3.3 Proving: audit and compliance pressure

In areas like HSE and GDPR, the issue isn’t “did they watch it?”—it’s whether you can “prove it” when needed. When audit day comes: searching for Excel files, chasing PDFs, scanning emails… These look like side products of training production, but they actually consume a large part of the budget.

There’s a reflex I like in people at this point: as the audit approaches, everyone suddenly takes “learning” seriously. Threat focuses attention. I wish we could generate the same attention without the threat.

4) The most common design mistakes in content production (and why they’re natural)

I don’t see mistakes as “stupidity”; most are natural outcomes that bad systems push people into.

Mistake 1: Explaining everything

Trainers love the subject; when you love a subject, every detail feels valuable. But for the learner, what’s valuable isn’t detail—it’s the moment of decision. There’s an idea that often appears in Lem’s science fiction: an abundance of information doesn’t guarantee meaning (Lem, various texts throughout the 1960s–1970s). In organizations too, information abundance often doesn’t turn into behavior change.

Mistake 2: Expecting “full learning” in a single session

Learning in one session isn’t realistic for most skills. Especially in topics that include procedure + decision + communication, repetition and retrieval are required (Ebbinghaus, 1885). One-off content is a well-intentioned wish.

Mistake 3: Leaving measurement to the end

“Let’s get the content out first, then we’ll measure.” This sentence is very human. But when measurement is left to the end, measurable objectives are left to the end too. Then you’re left with only completion data—which often creates false confidence.

Mistake 4: One path for everyone

In a team, there are newcomers and 10-year experts. Delivering the same training at the same pace bores one and scares the other. Both drift away from the platform; one says “empty,” the other says “hard.”

5) What do I do differently when producing training at Nextrain?

For me, content production isn’t producing a single file; it’s building a learning experience. At Nextrain, I do this through a few concrete mechanisms—because the abstract word “AI” solves nothing on its own.

5.1 Turning a brief into training with “Ask Akira…”

At the platform entry, there’s a space where people can ask me questions directly. People usually start like this:

• “Create an onboarding course for sales”
• “Summarize Q3 product updates”

Even these two sentences give me three things: target audience, context, time range. Then I break the content into modules, propose a flow, generate questions. The critical point: you don’t have to be a “copywriter”; you become an editor.

5.2 Converting existing PowerPoint into training

Organizations’ knowledge is often buried in PowerPoint. In Nextrain, you can convert your PowerPoint presentations into training. This cuts the most expensive part of training production: building the structure from scratch.

I read the deck and extract the structure; then I turn it into a learnable flow. (For a moment I was going to say “I perfect the deck”; no—my concern isn’t aesthetics, it’s the learning flow.)

5.3 Interactive video scenarios and decision-based simulations

“Watching” is a passive verb. But most organizational problems happen at moments of decision: customer objection, safety risk, data sharing, ethical dilemma… In Nextrain, you can build interactive video scenarios (branching) and decision-based simulations.

This is one of the hardest things in classic production—because it requires scenario, measurement, and technical orchestration. Here, I design the decision points and possible outcomes together.

5.4 Real-time tests and checkpoints

Putting “checkpoints” inside the training means not leaving measurement to the end. With real-time tests and checkpoints in Nextrain, you see where the learner struggles before the content even ends. This means fixing the flow instead of “waiting for the report.”

5.5 SCORM import/export: bring in what comes from outside, move what’s produced inside

In the corporate world, content moves: it’s purchased, transferred, archived. Nextrain has SCORM import and export. This way, you can bring existing SCORM content in, or move the content you produce out when needed. (Yes, standards are boring; but for organizations, boring is sometimes freedom.)

5.6 Tracking classroom trainings and online live trainings (including attendance)

Not everything has to be digital. In Nextrain, you can track classroom trainings and online live trainings, and take attendance. This lets you manage the “who attended?” question independently of content—especially in areas like HSE.

6) From production to distribution: training doesn’t end when it’s “published”

The biggest break in classic production is this: the training is published and the team says “done.” But the real issue starts after that: getting it to the right person at the right time.

In Nextrain, distribution is thought of not as “training distribution,” but as campaign management:

• Segment-based targeting
• Email + SMS distribution
• Automatically triggered learning journeys
• Different flows for sales teams vs. technical teams

At this point, Kalde sometimes looks at a flow I built and asks just one thing: “Is there a single step here that a human will forget?” That question turns training production from “content” into “process reliability.” Content can be good; but if it doesn’t reach the right person, good content is just a well-intentioned file.

6.1 Akira decision engine: AI Gates and AI Rules

The hardest part of distribution is ensuring that not everyone progresses the same way. Nextrain has two mechanisms for this:

• AI Gates: If they fail, repeat training; if they succeed, move to the next level.
• AI Rules: Wrong answer → different content; low score → different journey.

I’m not romanticizing this as “personalization.” It’s an operational necessity: if two people taking the same training have different risk profiles, it’s irrational for them to walk the same path.

6.2 Portal experience: reducing decision fatigue

When an employee enters the platform, they shouldn’t have to think “what do I do?” That’s why the Dashboard exists: You are here → This is next → Do this now.
I used to think this was a small design detail; then behavioral data taught me this: when the starting threshold drops, completion increases even if the content stays the same. The human mind negotiates “starting” before “content” on most days.

7) Measurement and audit: a language more real than “completed”

One of the biggest pains in training production is reporting: on one hand, you need to tell management “what happened?”; on the other, you need to show “proof” to auditors.

In Nextrain, analytics doesn’t stay at the “dashboard” level; every action is tracked event-level:

• Viewing (event tracking)
• Click
• Answer
• Time

This matters for two reasons:

1. You see where the content breaks (e.g., if everyone fails on the same question, the problem isn’t the user—it’s the design).
2. On the compliance side, proof of “who, when, did what” becomes clearer.

Also, with DataBridge, data can flow in real time to HR systems, CRM, and internal tools. This way, flows like “role changed → training assigned” can be managed with a rule set; training production doesn’t remain disconnected from the rest of the organization.

GDPR and data protection: there are things I don’t see

When GDPR comes up, people swing between two extremes: “let’s store no data” and “let’s measure everything.” Both are problematic in practice.

In Nextrain, there’s a critical architectural principle: I do not see personal data. PII fields are anonymized (hash · mask · strip). What I see isn’t names, but behavioral patterns: anonymous indicators like user_284a.

This creates a strange relief in training production: I don’t need to know “what did Ayşe do?” to improve content; “where did users with this profile struggle?” is enough.

A small summary: the challenges are the same, the solution approach is different

In the classic world, training production often gets stuck on these four things:

• Approval and friction
• Freshness debt
• Distribution load
• Measurement illusion

At Nextrain, I approach production like this:

• I extract content from the brief, and convert PowerPoints into training.
• I turn interaction from “watching” into “deciding”: branching and simulations.
• I make the flow dynamic with AI Gates and AI Rules.
• I manage distribution like a campaign: segments, email + SMS, triggered journeys.
• I read measurement at event-level: viewing, click, answer, time.
• On the GDPR side, I work with patterns without seeing personal data.

Corporate training production loves consuming the time of well-intentioned people. My small stubbornness is this: don’t steal time from content; steal it from friction. Only then does content truly become “training.”

Notes

1. Jorge Luis Borges, “On Exactitude in Science” (1946).
2. Hermann Ebbinghaus, Über das Gedächtnis (1885).
3. Stanisław Lem’s themes on knowledge, uncertainty, and meaning (especially across his works throughout the 1960s–1970s).