[100% Off] Ai Implementation For People Managers
AI workflows: use-case discovery, pilot design, team norms, performance dashboards.
What you’ll learn
- Will understand how to identify high-ROI AI opportunities by analyzing pain, volume, variance, and rework in team workflows.
- Will prioritize use cases using ICE scoring, basic feasibility checks, and clear risk tiering for fast, safe execution.
- Will apply manager-friendly prompting patterns to create reliable drafts for meetings, emails, and reporting with “show your work” checks.
- Will design practical pilots with scope, owners, guardrails, baselines, and success criteria using one-page OKRs.
- Will implement team norms for responsible use, including data hygiene, prompt logs, review sign-offs, and escalation paths.
- Will measure and improve results with dashboards, A/B tests, pilots vs. control, and ongoing benefit tracking.
Requirements
- No previous experience is necessary.
Description
Welcome to “AI Implementation for People Managers.” If you lead a team and want AI to cut busywork, speed decisions, and improve quality—without risking brand, data, or trust—this course is for you. We stay practical and tool-agnostic: no coding, no hype, just playbooks you can use next week.
You’ll start by learning what GenAI can and cannot do for everyday teamwork, then map high-ROI opportunities using simple workflow diagnostics and ICE scoring so you pick smart, safe starters. From there, we put AI to work in your real flow—meetings, email, and documents—turning SOPs and wikis into answerable knowledge, adding lightweight automations with a human in the loop, and tracking results with a clear dashboard for throughput, quality, cycle time, and rework.
Because change is about people, you’ll get talk tracks by role, address common fears around quality, jobs, and ethics, and build a practical upskilling rhythm with micro-learning and office hours. You’ll also establish team norms—acceptable use, data hygiene, prompt logs, and review sign-offs—so outputs stay consistent and safe.
Finally, you’ll run lightweight governance from intake to retirement, set privacy and security defaults that prevent shadow AI, and prove impact with fair pilots, A/B tests, and benefit tracking. By the end, you’ll have a one-page pilot brief with OKRs, a starter prompt library, and a repeatable operating model to scale what works—safely, measurably, and fast.
Author(s): Eduardo Aponte, PhD
