2025 L&D Trends: AI, Video, and Smarter Support Models

The most expensive learning program in 2025 isn’t the one with the biggest budget. It’s the one employees don’t finish—or finish and forget because it never connects to real work.

That’s why the most useful “learning transformation” conversations this year weren’t about shiny tools. They were about adoption friction, faster content supply, and operating models that actually scale during a skills shortage. Ayesha Habeeb Omer’s 2025 learning transformation roundup points to the same reality I’m seeing across workforce development teams: AI is moving from experimentation to operations, and L&D has to get much more intentional to keep up.

This post expands those themes into practical decisions you can make in your organization—whether you’re building training for frontline teams, rolling out digital learning transformation across regions, or trying to do more with a small L&D function.

AI adoption in L&D: the hard part isn’t the tool

AI adoption fails or succeeds on workflow design, not tool quality. Most organizations can get access to a generative AI assistant. The real challenge is getting consistent, safe, measurable use inside learning design and delivery.

A pattern has emerged in 2025: teams buy AI features, run a few pilots, then stall because they can’t answer basic operational questions:

• Who’s allowed to use AI for content creation—and for what types of learning?
• Where do prompts, drafts, and source materials live so work is reusable?
• How do we review AI-assisted content for accuracy, bias, and tone?
• What’s the plan when regulations or internal policies differ by country?

The adoption blockers you can actually fix

1) “AI is optional” governance. If AI usage is treated as a personal preference, adoption becomes random. The fix isn’t forcing everyone onto the same tool—it’s defining where AI is the default (e.g., first drafts, quiz banks, scenario variants) and where it’s restricted (e.g., sensitive HR, legal, medical content).

2) No shared prompt standards. High-performing teams in digital learning transformation treat prompts like templates. They keep a prompt library for:

• audience level and job context
• learning objectives
• tone and format
• assessment style
• localization requirements

3) Weak review loops. AI can speed creation, but it can also speed mistakes. Put a lightweight QA gate in place:

1. SME accuracy check (facts, procedures, compliance)
2. instructional design check (alignment, cognitive load, practice quality)
3. inclusion check (language clarity, bias, cultural assumptions)

A useful rule: if the content influences decisions, safety, or money, it needs a documented human review.

People Also Ask: “Will AI replace instructional designers?”

AI won’t replace good learning designers. It will replace unstructured design work—the hours spent staring at blank pages, rewriting objectives, and building first-pass assessments. The value shifts toward performance consulting, practice design, and measurement.

Rapid video creation: speed matters, but trust matters more

Rapid video creation is becoming the default format for workforce learning in 2025 because it matches how work actually happens: quick questions, quick answers, quick refreshers.

But faster production isn’t automatically better learning. The win is when video is used for the right job:

• demonstrating a task
• showing a conversation model (manager coaching, customer de-escalation)
• reinforcing a single decision rule
• providing a refresher right before a shift or busy period

What “good” looks like for microlearning video

If you want video to support skills development (not just content consumption), design around three constraints:

1) Keep it under 3 minutes unless there’s a strong reason. The best-performing internal videos I’ve seen are often 60–150 seconds and answer one question clearly.

2) Build for noisy environments. Many employees learn between tasks. Use:

• clear on-screen steps
• captions
• minimal background music
• tight framing (show hands/tools, not talking heads)

3) Pair video with a practice step. One quick method:

• Video (90 seconds)
• 3-question check (1 minute)
• “Do this on the job” prompt with a manager/peer confirmation

That last step is where training becomes workforce development. Without it, you’re just publishing.

A realistic example: customer support ramp-up

A support team needs faster onboarding for seasonal demand (December is always a pressure test). Instead of a 2-hour course, they produce:

• 12 short videos (2 minutes each) covering the top issue categories
• 12 short branching scenarios (choose the next response)
• a “day 3” live role-play session focused on the 3 hardest calls

Result: new hires don’t just “know the policy.” They rehearse the decisions they’ll make under time pressure.

ChatGPT in learning design: treat it like a junior collaborator

ChatGPT is most useful in instructional design when you treat it like a junior teammate: fast, helpful, and in need of supervision. It’s excellent at generating options—examples, scenarios, quiz items, rubrics, role-play prompts—but you still own alignment and accuracy.

Where it shines in eLearning design and development

1) Scenario design at scale. Skills training often needs repetition across contexts. AI can produce five scenario variants in minutes:

• new manager feedback conversation (remote team)
• feedback conversation (frontline shift)
• feedback conversation (cross-cultural team)
• feedback conversation (high performer vs. underperformer)
• feedback conversation (time-constrained setting)

2) Assessment item generation—with guardrails. AI can draft question banks quickly. Your job is to:

• map every item to an objective
• remove trivia
• increase realism (“What would you do next?”)

3) Plain-language rewrites. For global training, clarity beats complexity. AI can help rewrite dense text into step-by-step guidance.

The non-negotiable: source-grounded content

If your L&D team uses ChatGPT for content, set a rule: no factual claims without a source document. The workflow is:

1. Provide the model with policy/procedure text
2. Ask it to transform, summarize, or create practice
3. Validate output against the source

That single rule reduces hallucinations dramatically and keeps compliance partners calmer.

Global AI readiness: why workforce development now depends on geography

Global AI readiness isn’t a headline—it’s a constraint that determines what training is feasible across regions. If you’re supporting international education programs, multi-country employers, or distributed workforces, “one-size-fits-all” learning tech decisions will backfire.

Here’s what varies sharply by country and even by site:

• data privacy expectations and restrictions
• device access (desktop vs. mobile-first)
• bandwidth and platform reliability
• language coverage and translation quality
• comfort with AI-assisted work (cultural and organizational)

A practical way to plan for uneven readiness

Use a simple three-tier model for learning transformation planning:

Tier 1: AI-enabled creation (central team). Your core team uses AI to speed analysis, drafts, localization prep, and asset creation.

Tier 2: AI-supported learning (targeted groups). Specific roles get AI practice tools (role-play bots, coaching prompts) where policy allows.

Tier 3: AI-aware learning (everyone). All employees get training on AI basics: risks, verification habits, and acceptable use.

This approach respects reality: not every region can roll out the same tools at the same pace, but every region can build AI literacy.

People Also Ask: “What does AI readiness mean for training strategy?”

It means your L&D roadmap needs two layers:

• capability layer: what skills you want employees to build (common globally)
• delivery layer: how those skills are taught and supported (varies locally)

Choosing the right L&D support model: build, buy, or blend

The right L&D support model in 2025 is usually a blend: keep the strategy inside, flex the production outside. Skills shortages have made it harder to hire for every capability—video, learning analytics, LMS admin, UX, localization, accessibility, and AI governance.

The three models—and when they work

1) Build (in-house). Best when:

• training is core to your product/service quality
• you have stable demand and predictable roadmaps
• you need tight control over sensitive content

2) Buy (outsourced or vendor-led). Best when:

• you need speed (launch in weeks, not quarters)
• you need specialized skills temporarily (video studio, AR, translation)
• internal L&D is small

3) Blend (internal backbone + external bursts). Best when:

• you want consistency in standards and measurement
• you need flexible capacity for seasonal or project spikes
• you’re scaling across regions

A simple decision checklist (use this in your next planning meeting)

When deciding whether to outsource a learning initiative, ask:

• Is the knowledge changing weekly? If yes, keep closer to the business.
• Is the content high-risk? If yes, keep governance internal.
• Do we need 10x production for a short window? If yes, augment externally.
• Do we have a measurement plan? If no, fix that before building anything.

My stance: outsourcing without internal ownership produces pretty courses and weak outcomes. Someone inside must own performance results.

What to do next: a 30-day learning transformation sprint

If you want momentum without chaos, run a 30-day sprint that touches the themes in this 2025 roundup—AI adoption, rapid content creation, and the right support model.

Week 1: Pick one business-critical skill problem. Not “communication” broadly. Something like “handle billing objections” or “complete safety checks correctly.”

Week 2: Build a minimum viable learning path.

• 5 microlearning videos (≤ 2 minutes)
• 5 scenario practices (branching or short-answer)
• 1 manager/coach guide (one page)

Week 3: Add AI where it’s safe and useful.

• use ChatGPT to generate scenario variants
• create a question bank mapped to objectives
• rewrite content into plain language for translation

Week 4: Measure and decide. Track:

• completion (basic)
• practice performance (better)
• on-the-job metric (best: QA score, rework rate, incident rate, time-to-proficiency)

This is the thread that ties the whole Education, Skills, and Workforce Development series together: training only matters when it changes performance—and performance is what closes skills gaps.

If you’re planning your 2026 roadmap right now, where are you betting: more content, or better practice plus better operating models?