AI has moved from “nice to have” to required capability. Leading companies—including Shopify—now treat AI fluency as a core job skill and expect everyday use of AI assistants across roles. At the same time, content demand is exploding while budgets stay flat. This course meets that moment for L&D: you’ll learn how to use AI responsibly and practically to speed up LxD work, personalize learning, and link outcomes to performance.

Across two modules, we turn strategy into run-today workflows: AI-assisted content operations (storyboards, scenarios, microlearning with SME review and QA), rules-based personalized pathways using the learner data you already have, and assessment & remediation anchored in your skills taxonomy/competency framework. You’ll also put light-touch governance in place—privacy, audit trail, SME sign-offs—and a simple measurement model that moves the conversation from completions to capability build and on-the-job results.

The cohort is led by Malcolm Werchota, who rolled out one of Europe’s earliest GenAI learning programs in a major pharma company, reaching ~80% adoption in under six months. He runs workshops for C-level, HR, and L&D teams, and teaches CAS courses on AI at ESADE and HSLU. You’ll leave with reusable templates, prompt packs, QA rubrics, an assessment blueprint, an exec-ready measurement view, and a 90-day rollout plan you can socialize with stakeholders on day one.

Designed for busy people: This course is delivered over one weekend — Saturday: 1.5 hours and Sunday: 1.5 hours. Each session mixes quick demos, case walk-throughs, and guided practice you can apply to your own context. Between sessions, there’s optional 45–60-minute micro-practice (templates + prompt packs) so you can learn in the flow of work.

All sessions are recorded and timestamped for easy catch-up, and we keep time for Q&A so you leave with clear next steps each session.

Saturday — Foundations & Personalization

- Understand where AI genuinely helps L&D today (and where it doesn’t) across onboarding, compliance, and enablement.

- Use AI to speed up LxD work: outlining, storyboarding, scenario drafting—paired with human-in-the-loop QA so quality stays high.

- Apply practical prompting patterns for learning use cases (not just generic prompts).

- Translate business goals into a simple plan for AI-assisted learning that fits your existing LMS/LXP.

- Read basic learner data (from LMS/LXP/HRIS) and define decision-rule pathways that route people based on role, level, or performance.

- Design privacy-aware personalization: what data to use, what to avoid, and how to explain it to stakeholders.

- Map skills/competency frameworks to adaptive checks (diagnostics, difficulty variation) and remediation next steps.

Identify lightweight interventions (nudges, job aids, coaching prompts) that keep people progressing.

Sunday — Content at Scale, Governance & Implementation

- Produce multi-format content with AI (scripts, video drafts, simulations) while keeping accessibility, localization, and brand voice in check.

- Run a repeatable content-ops workflow with SME review and QA gates—so faster doesn’t mean sloppier.

- Connect content to outcomes with a simple, role-centric measurement view (not just completions).

- Plan for scale: collaboration flow, approvals, and versioning so the work survives beyond the cohort.

- Set guardrails that make leaders comfortable: privacy, SME review trail, auditability, and risk mitigation.

- Align metrics with stakeholders (capability build, in-role outcomes) and prepare an executive-ready narrative.

- Spot future opportunities and keep your AI-in-L&D practice current without chasing every tool.