AI for HR and Talent Analytics (SF) (Classroom & Asynchronous Elearning))
About This Course
This course covers the application of AI to workforce analytics, recruitment and talent management decisions.
Participants learn core workforce metrics and how to prepare HR data for AI analysis, covering time-to-fill, quality-of-hire, funnel conversion, span of control and regretted attrition. They then run an end-to-end recruiting pipeline: posting a hard-to-fill role through a cloud ATS, extracting and diagnosing the hiring funnel, benchmarking the role against live job-market data, rebuilding a bias-audited job description and screening rubric, and screening candidates with human-in-the-loop review before decisions are written back to the ATS. They widen the lens with a multi-site workforce dashboard, and finally analyse attrition drivers and build a costed retention business case with fairness and privacy checks applied before decisions are actioned.
The work is done in Claude as the primary tool for dataset analysis, dashboard building and document drafting, with live job-market sources and Perplexity AI providing labour-market intelligence and cost benchmarks. Participants use the full Claude workspace: Projects to organise scenario datasets and policies, Artifacts to build interactive dashboards and the leadership presentation, Skills to standardise repeatable analysis workflows, Cowork and its plugins, especially the HR plugin, to run hands-on multi-file HR workflows end to end, and MCP connectors to ground analysis in enterprise HR data.
The course suits HR managers, HR executives, talent acquisition specialists, workforce planners and HR analysts. It supports progression into roles such as HR Analyst, People Analytics Specialist, Talent Acquisition Partner, Workforce Planning Analyst and HR Business Partner.
What You'll Learn
Knowledge Statements
1. Explain how AI and analytics, delivered through the Claude workspace of Projects, Artifacts, Skills, Cowork plugins and MCP connectors, support workforce planning, recruitment and talent management decisions across the employee lifecycle.
2. Describe how Claude and the Cowork HR plugin can be applied to complex HR problems: hiring funnel diagnostics, multi-site workforce performance analysis and attrition driver modelling.
3. Evaluate bias, fairness, data privacy and responsible AI obligations when employee data is accessed via MCP connectors and used to inform talent decisions
Skills Statements
1. Analyse HR datasets extracted from an ATS into Claude, organised in Projects and accessed via MCP connectors, to produce workforce metrics and planning evidence, with external benchmarks gathered from live job-market sources and Perplexity AI.
2. Build and interpret interactive workforce dashboards as Claude Artifacts covering recruitment, performance and attrition scenarios, saving repeatable analyses as reusable Claude Skills.
3. Evaluate and defend AI-generated HR insights, including outputs of Cowork HR plugin workflows, applying bias, fairness and privacy checks before decisions are actioned
Ability Statement
Apply the full Claude workspace, including Projects, Artifacts, Skills, the Cowork HR plugin and MCP connectors, to complex talent problems and translate the findings into fair, defensible workforce decisions and recommendations.
Entry Requirements
Minimum O level or its equivalent
Basic knowledge in HR process and Gen AI tools usage