Two JLL HR Leaders Steering AI to Boost Hiring Efficiency and Employee Experience

How Two HR Leaders at JLL Are Steering AI-Driven Talent Strategies

JLL's HR leaders, Megan Kleinick and Jane Curran, are putting AI to work with a clear aim: improve employee experience and remove friction across HR. Their approach starts with strong processes and clean data, then layers AI where it reduces cycle time and adds clarity.

They're using AI-driven recruitment to handle high applicant volumes and leaning into generative tools and personalized AI agents across HR functions. The result: faster hiring, sharper content, and consistent service delivery without adding headcount.

Start With Process and Data Quality

• Map the end-to-end flow for recruiting, onboarding, case management, and internal mobility. Remove steps that don't add value.
• Define a single source of truth for candidate, employee, and job data. Lock naming conventions and access rules.
• Run a data hygiene sweep: duplicates, missing fields, stale records, unstructured notes. Fix the root causes, not just the symptoms.
• Set retention, consent, and audit trails before scaling any model or agent.

AI-Driven Recruitment at Scale

• Intake: Convert hiring manager needs into structured requirements. Standardize must-haves vs. nice-to-haves.
• Screening: Use AI to triage large applicant pools, then route shortlists to recruiters. Keep humans in the final decision loop.
• Summaries: Auto-generate candidate summaries and interview guides based on the job profile and resume signals.
• Scheduling: Deploy an agent to coordinate interviews and reminders, integrated with calendars.
• Fairness checks: Monitor adverse impact, remove proxy variables, and document decisions.

Generative AI Across HR

• Job content: Draft JDs, salary ranges (with bands), and structured interview packs. Final review remains with recruiters and HRBPs.
• Knowledge and policy: Turn long documents into clear FAQs, how-tos, and change notices.
• Manager support: Create templates for feedback, performance notes, and development plans.
• Learning: Recommend courses based on skills, role, and goals using profile and performance signals.

Personalized AI Agents That Actually Help

• Employee service agent: Answers benefits, PTO, and policy questions from your HR knowledge base, then escalates complex cases.
• Recruiter copilot: Drafts outreach, ranks candidates against structured criteria, and flags missing info.
• Manager copilot: Guides hiring decisions, comp change requests, and performance conversations with checklists and next steps.

Guardrails, Compliance, and Risk

• Create an AI review routine: use cases, data sources, risks, and approvals tracked in a simple register.
• Document model behavior, inputs, and limitations. Require human sign-off where decisions affect employment.
• Run periodic bias and accuracy tests. Log outcomes and remediation.
• Vendor due diligence: data handling, security, audit rights, and model update cadence.

Useful references: EEOC guidance on AI in employment and the NIST AI Risk Management Framework.

Change Management That Drives Adoption

• Communicate the "why," the limits, and the expected gains for each role.
• Pilot with one business unit. Pick a measurable pain point (time-to-fill, case response time).
• Train with real scenarios. Provide prompt libraries and examples.
• Set up champions and feedback loops. Ship small updates weekly.

90-Day Implementation Blueprint

• Weeks 0-2: Pick 2-3 use cases, map processes, confirm data sources, define KPIs, and legal guardrails.
• Weeks 3-6: Build prompts/flows, integrate with ATS/HRIS, launch pilot for one role family or region.
• Weeks 7-12: Measure results, tune prompts and routing, expand to a second function, and formalize governance.

What to Measure

• Recruiting: time-to-shortlist, time-to-offer, quality-of-hire proxy (first-90-day outcomes), candidate NPS.
• HR service: first-contact resolution, average handle time, deflection rate, CSAT.
• Content accuracy: human edit rate and correction types.
• Risk: bias metrics by stage, incident count, and audit completion time.

Practical Stack Considerations

• Use your ATS/HRIS as the system of record. Add an orchestration layer for prompts, workflows, and guardrails.
• Centralize HR knowledge in a clean repository for retrieval-based answers.
• Secure LLM access with role-based permissions and data masking.
• Log prompts, outputs, and decisions for audits and continuous improvement.

Skills and Enablement

Equip recruiters, HRBPs, and COEs with prompt patterns, evaluation checklists, and a simple risk playbook. If you need structured upskilling, see curated AI programs for HR roles here: AI courses by job.

Bottom Line

Kleinick and Curran show a simple formula: clean processes + reliable data + focused AI use cases. Start small, measure what matters, and scale what proves value.