AI at Work

What you will be able to do

• Select use cases using value, feasibility, and risk.
• Run a bounded pilot with measurable success criteria.
• Plan adoption without hiding workforce or governance impacts.

Recommended first: Responsible AI User

Course modules

1. 1. Use-case selection

   Outcome: Prioritize tasks where AI can create measurable value.

   Practice: Score three candidate use cases on value, feasibility, reversibility, and risk.

   Competencies: Tool selection and comparison · AI-assisted workflow design

   ai workflow automation →ai coding tools →

2. 2. Pilots and experiments

   Outcome: Design a comparison that can reveal whether AI actually helps.

   Practice: Write a pilot plan with baseline, treatment, metrics, stop conditions, and review owner.

   Competencies: Experiment and pilot design · Success metrics and monitoring · Model cost and operational tradeoffs

   ai benchmarks →ai workflow automation →

3. 3. People and jobs

   Outcome: Assess task changes without reducing workers to job titles.

   Practice: Create a task-level impact map and reskilling plan.

   Competencies: Workforce and job impacts · AI career readiness · Stakeholder communication

   ai jobs →ai future →

4. 4. Operational governance

   Outcome: Assign ownership for approvals, monitoring, and incidents.

   Practice: Build a lightweight responsibility matrix for one deployed workflow.

   Competencies: Governance and accountability · AI incident response · Privacy and data handling

   ai governance →ai safety →ai privacy →