ADDIE—Analysis, Design, Development, Implementation, Evaluation—has been the backbone of instructional design for decades. Walk into any L&D team and you’ll find people who learned ADDIE as the foundational framework for creating training programs. The Association for Talent Development still teaches it as a core competency for instructional designers.

But the model was developed in an era of classroom training, physical materials, and stable skill requirements. Does it still work for AI-era learning design?

I believe it does, with thoughtful adaptation. Here’s why the framework endures and how to apply it to contemporary challenges.

What ADDIE Gets Right

Before discussing adaptations, let’s acknowledge why ADDIE has persisted while other models have faded.

It’s systematic without being rigid. ADDIE provides a logical sequence for developing training without mandating specific techniques at each stage.

It separates what from how. Analysis clarifies what you’re trying to achieve before design determines how to achieve it. This prevents solutions looking for problems.

It includes evaluation. Many approaches treat assessment as an afterthought. ADDIE builds it into the framework from the start.

It’s learnable. New instructional designers can grasp ADDIE quickly and apply it immediately, then refine their approach with experience.

These strengths remain relevant regardless of technology changes.

The Classic Criticism: Waterfall Thinking

The main criticism of ADDIE is that it implies linear, sequential development: finish Analysis before starting Design, finish Design before Development, and so on.

This “waterfall” approach is slow and inflexible. By the time you’ve methodically moved through all phases, requirements may have changed. And discovering problems late in the process means expensive rework.

The criticism is valid, but it’s not fatal. ADDIE doesn’t actually require strict sequential execution. Experienced practitioners iterate between phases, run parallel workstreams, and use prototyping to validate design decisions before full development.

The problem isn’t ADDIE itself—it’s overly rigid interpretation.

Applying ADDIE to AI Training

Here’s how I adapt each phase for AI-related training development.

Analysis

Traditional analysis asks: What gap exists between current and desired performance? What knowledge, skills, or attitudes need to change?

For AI training, analysis must also address:

Capability baseline variation. AI experience ranges from zero to expert, even within similar roles. Analysis needs to segment the audience by current capability, not just demographic.

Use case identification. What specific tasks could AI assist? This requires understanding actual work, not just job descriptions. Observe people working, interview them, analyse their outputs.

Motivation and resistance factors. What concerns do people have about AI? What would make them want to learn? Analysis that ignores the emotional dimension will produce technically correct but ineffective training.

Rate of change considerations. Will the skills you’re analysing remain relevant? AI capabilities evolve rapidly. Build into your analysis an assessment of skill durability.

A thorough analysis phase for AI training might take longer than traditional topics, but it saves significant time later.

Design

Design translates analysis findings into learning architecture. For AI training, emphasise:

Practical application from the start. AI skills develop through use, not through passive instruction. Design should integrate hands-on practice with actual tools from early stages.

Scaffolded complexity. Begin with simple, high-success-probability tasks before introducing complexity. Early wins build confidence that enables later learning.

Error-based learning. People need to encounter AI limitations firsthand—hallucinations, inconsistent outputs, prompt sensitivity. Design should create controlled opportunities for these experiences.

Transfer design. The goal isn’t to learn AI in training—it’s to use AI at work. Explicit attention to transfer mechanisms during design increases real-world application.

Assessment that demonstrates capability. Multiple-choice questions don’t prove AI fluency. Design assessments that require actual tool use to produce work outputs.

Development

Development creates the actual learning materials. For AI content, key considerations include:

Rapid obsolescence. AI capabilities change faster than traditional subjects. Develop content in modular formats that can be updated individually. Avoid tight coupling between modules that makes updates cascade.

Real tool integration. Simulated AI interfaces are never quite right. Where possible, integrate actual tools so learners work with real systems.

Example currency. AI examples from even six months ago may show outdated interfaces or capabilities. Review examples regularly and update or replace stale material.

Facilitator preparation. If humans are delivering AI training, they need more preparation than traditional topics. AI questions are less predictable. Facilitators must be genuinely capable, not just following scripts.

Development timelines for AI content should build in maintenance from the start. This isn’t a one-time development—it’s ongoing content curation.

Implementation

Implementation delivers the training to learners. For AI programs:

Technology access verification. Learners can’t practice with AI tools they can’t access. Verify that technology access is resolved before training, not during.

Manager briefing. Prepare managers to support application. Without manager involvement, training often doesn’t transfer. Brief them on what their people will learn and how to reinforce it.

Peer learning facilitation. Create mechanisms for learners to help each other—communities of practice, buddy systems, sharing forums. Peer support significantly improves AI skill development.

Immediate application opportunities. Schedule training close to opportunities to use the skills. AI training followed by two weeks of normal work before application is largely wasted.

Evaluation

Evaluation assesses whether training achieved its objectives. For AI training:

Capability assessment, not just completion. Traditional evaluation often tracks completion rates and satisfaction scores. For AI training, assess whether people can actually do the tasks you trained them for.

Behaviour tracking at intervals. Check whether people are using AI in their work at multiple points after training—two weeks, six weeks, three months. Early assessment misses the decay that often occurs.

Results connection. Link AI training to business outcomes where possible. This is methodologically challenging but essential for demonstrating value.

Continuous improvement. Evaluation should inform updates to the program. In fast-moving domains, evaluation cycles should be shorter and feed back more quickly.

ADDIE Variations for Faster Iteration

When speed matters, consider variations that compress the traditional sequence:

SAM (Successive Approximation Model): Iterative cycles of design, development, and evaluation replace the linear sequence. Useful when requirements are uncertain or when rapid prototyping can validate design choices.

Rapid prototyping: Build a minimal version quickly, test with real users, refine based on feedback. This validates design decisions before committing to full development.

Agile instructional design: Apply software development agile principles to learning development. Work in sprints, prioritise ruthlessly, iterate based on feedback.

These approaches aren’t replacements for ADDIE—they’re adaptations that preserve the core logic while increasing flexibility.

When to Use a Simpler Approach

ADDIE is appropriate for substantial training development. It’s overkill for:

• Quick job aids or reference materials
• Informal peer learning facilitation
• One-off sessions addressing immediate needs
• Simple information sharing

Not every learning intervention needs a full ADDIE process. Match methodology to complexity and stakes.

The Practitioner’s Reality

In practice, experienced instructional designers don’t mechanically follow ADDIE. They’ve internalised the underlying logic and apply it flexibly.

Analysis happens continuously, not just at the start. Design decisions get tested through informal development. Implementation reveals evaluation insights. The phases blur together.

This is fine. The framework provides structure for thinking, not a rigid process to follow blindly.

The Enduring Value

ADDIE endures because it asks the right questions:

• What are you trying to achieve? (Analysis)
• How will you achieve it? (Design)
• What materials do you need? (Development)
• How will you deliver it? (Implementation)
• Did it work? (Evaluation)

These questions apply regardless of the topic, the technology, or the delivery method. They applied to classroom training in 1975 and they apply to AI training in 2024.

The specific techniques within each phase evolve. But the logic remains sound.

If you’re designing AI training—or any training—ADDIE provides a framework that helps ensure you think through what matters. That’s why it’s stuck around, and why it will likely persist through whatever comes next.