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AI for HR Operations — Deep Dive

From buzzword to buying power. Each chapter: plain-language overview + systems-level deep dive. Built for the ops leader who needs to evaluate, govern, and deploy — not build.
Co-Created with Claude
Core Topics HR Tech Stack Vendor Evaluation Compliance & Risk Workforce Planning Change Management AI Policy & Governance
home Learning Portal play_arrow Start Learning summarize Key Insights dictionary Glossary work Interview Prep 10 chapters · Each with High Level + Under the Hood
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Foundations

Understanding AI Through an Ops Lens

Demystify the buzzwords, map the HR tech stack, and learn the difference between automation and intelligence.
1
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What AI Actually Is
Demystifying ML, LLMs, and agents — what each term means and why it matters to your operations.
2
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AI in the HR Tech Stack
ATS, HRIS, payroll, benefits — where AI already lives in your systems and what “AI-powered” actually means.
3
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Automation vs. Intelligence
RPA vs. ML vs. generative AI — a decision framework for when to automate, augment, or leave alone.
4
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AI for Recruiting & Talent
Resume screening, sourcing, matching — and why “blind screening” isn’t always blind.
Operations

AI in Daily HR Operations

Workforce planning, employee experience, and the compliance landscape you need to navigate.
5
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AI for Workforce Planning
Attrition prediction, skills gap analysis, headcount modeling — and the data quality it all depends on.
6
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AI for Employee Experience
Chatbots, sentiment analysis, onboarding — and how NLP reads surveys vs. how humans do.
7
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Compliance & Risk
EEOC, CCPA, EU AI Act, NYC Local 144 — the legal landscape for AI in employment decisions.
Leadership

Evaluating, Governing & Leading AI Adoption

Vendor due diligence, policy frameworks, and change management for your team and organization.
8
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Evaluating AI Vendors
What to ask, what to demand, red flags — SLAs, data ownership, model cards, and explainability.
9
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Building an AI Policy
Acceptable use, governance structure, RACI for AI decisions — practical policy templates.
10
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Leading AI Adoption
Change management, pilot design, measuring ROI — from first experiment to operational reality.