AI in the employee lifecycle - with Craig Clark and AHEP

Join Craig Clark and AHEP for this free webinar exploring how AI is impacting the employee lifecycle. 

The use of AI is no longer in a pilot phase; it is used daily and extensively. In an HR setting the use cases are extensive, from sourcing and applicant scoring to service delivery, learning, reward, people analytics, monitoring and casework.

For HR, the question is no longer whether AI will feature in the employee lifecycle, but on whose terms, under what controls, and with whom accountability sits when issues arise.

This session takes a balanced, risk-based view of AI across the whole of HR, not just employee relations. It draws a distinction that is routinely missed. Enterprise tools are often bought for their data-protection assurances the vendor contractually won’t train on your data, and it stays inside your boundary. Those assurances are useful, but they address confidentiality and security while leaving a larger issue untouched one that is still very much a data protection matter once you consider accuracy, fairness and purpose limitation. A model that summarises a case file, drafts a decision or scores a candidate can be confidently, fluently wrong, committing that error to a record that persists, propagates and is later disclosable. These risks may not surface until they are material: at a grievance, a disciplinary process, a tribunal or a subject access request.
The session sets out where AI earns its place, where it should be kept out of the decision, how to tell the difference, and the principle that anchors all of it - an AI tool changes who does the work, never who is accountable for it. We will set out clear governance controls that make that accountability practical, and address the information-rights and records questions AI raises across recruitment, monitoring and the employee record.

Learning outcomes

1. Identify where AI adds value across the HR lifecycle, and where it introduces disproportionate risk.
2. Apply a risk-based model to judge when AI use is defensible, when to avoid it, and how to assess a use case: consequence, data sensitivity, bias, explainability, human oversight and vendor/data control.
3. Locate where accountability sits when AI output proves wrong, why it remains with the institution and the decision-owner, and cannot be transferred to the vendor or the tool, and recognise how integrity failures engage the data protection duties of accuracy, fairness and purpose limitation, not only confidentiality.
4. Put in place the governance that makes AI use defensible: human verification proportionate to consequence, provenance and labelling, and a correction process for errors found later, alongside the logging, retention and information-rights questions AI raises.