How I work

A five-phase methodology, tuned for AI-assisted L&D.

Every case study uses the same framework, built on ADDIE and Kirkpatrick. What's new is the build phase: AI tooling makes hand-built interactions and real data pipelines practical on a small-team budget.

01 Discover 02 Design 03 Build 04 Deliver 05 Measure

Why the framework matters now

AI makes it easy to produce something that looks like training. The hard part is deciding what to build, how to measure it, and whether training is even the right fix. This framework is where those decisions get made on purpose.

Phase 01

Discovery & analysis

Learn what the business actually needs before promising what training can deliver.

What happens

→ Interview sponsors, managers, and learners
→ Find the gap between what people do and what the business needs
→ Root-cause check: knowledge, skill, or system problem?
→ Agree on success metrics before any content decisions

What comes out

• One-page problem statement, signed off by the sponsor
• Baseline metrics (the numbers we're moving)
• Go/no-go recommendation; sometimes the answer is "this isn't a training problem"

""No training" is a legitimate recommendation. If it's a process problem, say so on page one."

Heuristic

Phase 02

Design

Make the hard structural decisions on paper before a single screen gets built.

What happens

→ Write measurable objectives tied to the business metrics
→ Pick the modality on evidence, not taste
→ Storyboard scenarios with realistic detours, not only happy paths
→ Design the assessment before the content

What comes out

• Design doc: objectives, modality rationale, assessment strategy
• Scenario map with branching logic
• Risk list: what's most likely to break in build?

"The modality decision is where most projects fail. "Just make a Rise module" is usually a design problem dressed as a tooling problem."

Heuristic

Phase 03

Development

Prototype early, iterate in small loops, pick tools for the learner rather than the license.

What happens

→ Prototype the highest-risk interaction first, not the intro screen
→ Rise for linear builds; code when the interaction or data matters
→ AI-assisted builds in Cursor and Claude, compressing weeks into days
→ Two rounds of learner testing before handoff

What comes out

• Working prototype of the riskiest piece by week 2
• Production build with accessibility audit (WCAG 2.1 AA)
• Facilitator and IT deployment notes

"If the first demo is a polished intro screen, you built the wrong thing first. Show the hardest part working."

Heuristic

Phase 04

Delivery & change

Treat the launch as a rollout project, not a file transfer.

What happens

→ LMS deployment and smoke test
→ Train-the-trainer or SME briefings, whichever fits
→ Manager comms so people know why this exists and when it's due
→ Support channel for the first 30 days

What comes out

• Deployed experience, access verified
• Manager toolkit (email copy, talking points, tracking)
• Escalation path for the first 30 days

"Managers are the single biggest variable in whether training sticks. Build for them, too."

Heuristic

Phase 05

Evaluation

Measure against the metrics agreed in Discovery, not what's easy to count.

What happens

→ Level 1, reaction: short, pointed surveys
→ Level 2, learning: xAPI from interactions and assessments
→ Level 3, behavior: change in the operational system (tickets, error rates, cycle times)
→ Level 4, results: the sponsor's business metric

What comes out

• xAPI schema and LRS pipeline
• Dashboards for L&D and business partners
• 30/60/90-day review of behavior-change data

"If you can't connect the training to a business outcome, the next project is a harder sell."

Heuristic

See the framework in action

Each case study shows how these phases played out on a specific project: the decisions, the detours, the numbers.

Browse case studies