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Change Management Lead

Training Design & Delivery

Enhances✓ Available Now

What You Do Today

You design the training programs that build the skills people need to work in the new way — role-based curricula, hands-on practice, job aids, and the ongoing reinforcement that prevents the 'trained but not adopted' problem.

AI That Applies

AI-personalized learning paths that adapt training content based on each learner's role, prior knowledge, and demonstrated proficiency, focusing time on gaps rather than material they already know.

Technologies

How It Works

The system ingests each learner's role as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The practice design.

What Changes

Training becomes adaptive. AI adjusts the difficulty, pace, and focus of training content based on individual performance, so experts aren't bored and novices aren't overwhelmed.

What Stays

The practice design. Real skill building comes from doing the work in realistic scenarios with coaching and feedback. AI can deliver content, but designing meaningful practice and providing human coaching is where learning happens.

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 training design & delivery, understand your current state.

Map your current process: Document how training design & delivery works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The practice design. 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 Machine Learning 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 training design & delivery 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 CEO or executive sponsor

Which training programs have the highest completion rates, and which have the lowest — what's different?

They set the strategic priority for transformation initiatives

your CTO or CIO

How do we currently assess whether training actually changed behavior on the job?

They own the technology capability that enables your strategy

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.