AI Upskilling Programs: Advanced — Program Design

A program-design course for the people building AI training at scale: role-based curricula, assessment-driven placement, and measurement that proves impact. Learners design their organization's full upskilling architecture.

What this course covers

The course runs across 10 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Foundations of Enterprise AI Upskilling — Establish the strategic framework for large-scale AI training, aligning program architecture with organizational goals.
Designing Role-Based AI Curricula — Define AI competencies across different job families and map specific skills to technical, semi-technical, and non-technical roles.
Scenario: Mapping Skills to Persona-Specific Pathways — Practice analyzing job descriptions and assigning appropriate AI competencies to diverse organizational roles in a simulated decision-making environment.
Assessment-Driven Placement Strategies — Design diagnostic pre-assessments to accurately baseline learner skills and bypass redundant introductory content.
Tool Practice: Configuring Automated Placement Rules — Perform hands-on configuration within the platform to set up assessment-driven rules for automatic pathway assignment.
Structuring Cohorts for Collaborative Learning — Design high-engagement cohort structures, synchronous milestones, and peer-learning schedules to maximize program completion rates.
Tool Practice: Setting Up Cohorts and Milestones — Step-by-step practice creating, scheduling, and managing learner cohorts and learning paths directly within the administrator console.
Defining AI Training Metrics and KPIs — Identify key performance indicators across operational alignment, learner adoption, capability gain, and business performance.
Scenario: Measuring and Proving ROI to Stakeholders — Analyze training data, calculate program ROI, and navigate stakeholder objections in a scenario-based executive presentation exercise.
Finalizing and Launching Your Upskilling Architecture — Consolidate your curricula, placement rules, cohort design, and measurement framework into a cohesive, ready-to-launch roadmap.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

L&D leaders, enablement. The material is pitched at advanced level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Adoption & Leadership track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including branching scenario, guided lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

All AI Leadership training courses →
AI Center of Excellence: Advanced — Building & Running — A blueprint course for standing up an AI Center of Excellence: charter, structure, staffing, and how it interfaces with governance. Learners draft a CoE design for their own organization.
Scaling AI Adoption: Advanced — Pilot to Organization — The course for the hard step after a successful pilot: sequencing rollout, funding enablement, and building support structures. Learners produce a scale plan with economics attached.
Measuring AI Adoption & ROI: Advanced — Leaders and enablement teams learn to measure adoption, productivity, and ROI of AI initiatives. The course covers adoption metrics, productivity baselines, ROI models, dashboards, and reporting. Participants finish able to prove and improve the value of AI investment.
AI Enablement Delivery: Advanced — Train-the-Trainer — Trainers and enablement leads learn to design and deliver effective internal AI training. The course covers curriculum and delivery, hands-on facilitation, assessment, reinforcement, and measuring impact. Participants finish able to teach AI skills that actually stick.
Leading Teams Through AI Adoption — People managers and team leads learn to guide their team through adopting AI in daily work. The course covers setting vision and expectations, redesigning workflows, coaching, addressing fear, and building momentum. Participants finish able to turn AI from a curiosity into a habit on their team.
AI Adoption Leadership Assessment — Measures whether a leader can identify use cases, plan a pilot, set guardrails and judge results.
AI Program Leader — Stand up and scale an organizational AI program
Helping Your Team Learn AI Pathway — Grow from a one-team learning plan into a designed upskilling program.

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code ADO-UPS.