People Professionals and Artificial Intelligence

About this package

People Professionals and Artificial Intelligence examines how AI can support people practices and where human judgement remains essential. It begins by distinguishing AI from traditional HR systems and rules-based automation, then explains machine learning, generative AI, predictive analytics and natural language processing. Learners compare general-purpose and specialist large language models, open-weight and proprietary models, and hosted and locally deployed options. Data quality, volume and governance are considered throughout, helping learners judge whether an output is dependable enough for its intended use rather than simply plausible. The package follows AI applications across the employee lifecycle. Recruitment content covers attraction, selection and candidate experience, while learning and development topics explore personalised pathways, content generation and skills inference. Further sections examine performance feedback, engagement listening, employee coaching, workforce forecasting, attrition risk and skills gaps. Learners assess these uses against benefits, limitations and success measures, focusing on whether an application improves a real people decision rather than just processing information faster. Practical drafting work addresses people policies, job descriptions, employee communications and people analytics summaries. The emphasis is on using approved information and checking generated text for accuracy, relevance and appropriateness before use. Implementation planning then connects the chosen application to existing HR systems and workflows, including review responsibilities, organisational readiness, change management, practitioner capability and user trust. This links the choice of a tool to the everyday work needed to use it reliably. The final part examines responsible adoption through ethical risk, legal considerations and operational governance. Learners consider bias, discrimination, opaque decisions, privacy breaches, over-reliance and perceived employee surveillance, alongside safeguards based on fairness, accountability, transparency and human oversight. Coverage includes GDPR, relevant AI regulation and employment law, with attention to matters requiring specialist advice. Acceptable-use policies, decision ownership, employee consultation, monitoring and escalation support a wider human-centred strategy that considers job roles, workforce capability, organisational culture and the future contribution of people professionals.

Who it’s for

Suited to HR and people professionals, including those working in recruitment, learning and development, people analytics and workforce planning. It is particularly relevant to practitioners evaluating AI tools, planning adoption or helping establish responsible use within the people function.

What’s covered

1. AI foundations, language models and deployment choices

   Distinguish AI from traditional HR technology and rules-based automation, and explore machine learning, generative AI, predictive analytics and natural language processing. Compare general-purpose and domain-specific language models, open-weight and proprietary models, and hosted and locally deployed options.

2. Data reliability and people decision support

   Examine how data accuracy, completeness, consistency, relevance, volume and governance affect AI reliability. Consider the strengths of AI in bounded tasks and the distinction between processing information and exercising human judgement.

3. Attraction, selection and candidate experience

   Explore AI-assisted attraction, vacancy recommendations, screening, matching and assessment. Judge applications by their effects across the recruitment process and on appointment decisions, rather than speed or novelty alone.

4. Personalised learning, content generation and skills inference

   Consider how AI can recommend and adjust learning pathways, generate learning materials and infer employee skills. Evaluate these applications against role requirements, evidence of capability and workplace performance.

5. Performance feedback, engagement and coaching

   Examine the use of AI to organise work evidence, draft performance feedback, interpret employee comments and support coaching. Consider how summaries, sentiment scores and generated responses can misrepresent employees' circumstances.

6. Workforce planning and people analytics

   Explore forecasting, attrition risk and skills gap identification as inputs to resourcing, retention and development decisions. Use a scorecard approach to compare expected benefits, operating limitations and evidence of success.

7. Drafting and checking people documents

   Use generative AI to draft a people policy, job description, employee communication and analytics summary. Establish boundaries around source information and check outputs for accuracy, relevance and appropriateness before use.

8. Implementation within HR systems and workflows

   Plan how an AI application connects to existing processes, authoritative records and human review. Address workflow hand-offs, organisational readiness, change management, practitioner capability and user trust.

9. Ethical risks and practical safeguards

   Diagnose bias, discrimination, opacity, privacy risks, over-reliance and perceptions of surveillance across an AI-enabled people decision. Translate fairness, accountability, transparency and human oversight into specific checks, responsibilities and routes for correction.

10. Data protection, AI regulation and employment law

    Evaluate proposed uses against GDPR requirements, relevant AI regulation and employment law considerations. Examine the purpose and lawful basis for processing personal data, identify compliance gaps and recognise when specialist legal advice is needed.

11. Operational governance for people AI

    Develop a governance framework covering acceptable use, named decision owners, approval, human review and employee consultation. Establish monitoring, escalation and correction processes rather than relying on policy statements or supplier assurances alone.

12. Human-centred strategy and the future people profession

    Consider AI adoption in terms of people's needs, organisational objectives and the employment relationship. Explore implications for job roles, workforce capabilities, organisational culture and the people profession's contribution beyond process automation.

Learning outcomes

By completing this package, learners can:

• Explain the distinctions between machine learning, generative AI, predictive analytics, natural language processing and rules-based automation, using examples that distinguish AI from traditional HR technology.
• Compare types of large language models and their deployment options, including general-purpose and domain-specific models, open-weight and proprietary models, and hosted and locally deployed solutions, for their suitability for people-related tasks.
• Assess how data quality, volume and governance affect the capabilities, limitations and reliability of AI systems used in people practices.
• Use generative AI to draft a people policy, job description, employee communication and people analytics summary, checking each output for accuracy, relevance and appropriateness before use.
• Evaluate AI applications across attraction, selection, learning, performance, engagement and workforce planning, including screening, personalised learning, sentiment analysis, attrition forecasting and skills gap identification, against defined benefits, limitations and success measures.
• Design an AI implementation plan for a selected people practice that addresses integration with existing HR systems, organisational readiness, change management, practitioner capability and user trust.
• Analyse an AI-enabled people practice to identify risks of bias, discrimination, opacity, privacy breaches, over-reliance and perceived employee surveillance, and recommend safeguards grounded in fairness, accountability, transparency and human oversight.
• Evaluate a proposed HR AI use case against applicable GDPR requirements, relevant AI regulation and employment law considerations, identifying compliance gaps and issues requiring specialist legal advice.
• Develop a governance framework for AI in the people function that specifies acceptable-use policies, decision accountability, human review, employee consultation, monitoring and escalation processes.
• Formulate a human-centred AI adoption strategy that moves beyond process automation towards decision support and insight generation, addressing implications for job roles, workforce capabilities, organisational culture and the future contribution of people professionals.

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