Product delivery leadership
Own the roadmap for AIenabled onboarding intake recommendation approvals provisioning audit evidence
Translate onboarding pain points into prioritised use cases user stories and measurable outcomes
Run delivery cadence across IAM ops engineering and data science teams
manage RAID and dependencies
AIenabled onboarding capabilities
Deliver capabilities such as Accessrole recommendations based on joiner attributes and peer patterns
least privilege by default
Intelligent request intake free text to structured entitlements with missinginfo prompts
Automated SoDpolicy prechecks and exception workflows
Approval routing optimisation and onboarding copilot for service deskIAM ops
Define confidence thresholds and humanintheloop decision points
Data integration operating model
Coordinate data sourcing and quality across HR feeds
IAM catalogue historical requests org hierarchy and ticketingworkflow tools
Partner with engineering to integrate into IAM workflows eg ServiceNowIGA tooling
ensuring secure APIs logging and resiliency
Define BAU support model runbooks and training for IAM ops and approvers
Risk controls
Responsible AI
Ensure alignment with privacy information security and model risk management expectations
Drive auditability decision rationale approvals model outputs and change history
Implement guardrails for GenAI where used
data leakage prevention
prompt controls
content filtering
monitoring
Define monitoring for model performance and control effectiveness
drift override rates
exception volumes
Value realisation
Define and track KPIs onboarding cycle time rework rate access errors SoD conflicts prevented approver turnaround time
Drive adoption through stakeholder engagement communications and continuous improvement
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