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Training & Development Specialist

Develop training content and materials

Enhances✓ Available Now

What You Do Today

You create presentations, e-learning modules, job aids, videos, and hands-on exercises — producing the materials that deliver the learning experience you've designed.

AI That Applies

AI generates training content from subject matter input, creates interactive e-learning modules, and produces assessment questions aligned to learning objectives.

Technologies

How It Works

The system ingests subject matter input as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — training content from subject matter input — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Content production becomes dramatically faster when AI generates first drafts of presentations, e-learning modules, and assessments.

What Stays

Ensuring content accuracy with subject matter experts, creating engaging scenarios from real workplace situations, and the instructional design judgment that makes content effective.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for develop training content and materials, understand your current state.

Map your current process: Document how develop training content and materials works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Ensuring content accuracy with subject matter experts, creating engaging scenarios from real workplace situations, and the instructional design judgment that makes content effective. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Content Generation AI tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long develop training content and materials takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your CLO or VP Learning

What content do we produce the most of that follows a repeatable structure?

They're deciding the AI strategy for the L&D function

your LMS administrator

What's our current review and approval process, and would AI-generated first drafts change the bottleneck?

They manage the platform that AI learning tools plug into

your HR business partner

How would we know if AI actually improved develop training content and materials — what would we measure before and after?

They connect training needs to talent strategy

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.