The CHRO's Job Just Got Bigger: From People Leader to AI Strategist
AI isn't a side project anymore. It's changing how work gets done, how teams are built, and how leaders make decisions. That puts the CHRO at the center of people, technology, and data - building a workforce that learns fast and ships value.
As one professor from the Technical University of Munich put it, "The old model of HR was employees over here, technology over there. But the new model of work is human-AI collaboration." If AI is a coworker, HR's mandate expands. Leaders need fluency in culture, business strategy, and the tech under the hood.
What leading HR teams are doing right now
Susan LaMonica, CHRO, Citizens Bank
HR is becoming the architect of the future of work. Her team is mapping where AI fits, what tasks belong to people, and how to reskill employees for new roles without breaking trust.
There's a push to let teams build their own AI agents, but banking is heavily regulated. That means decentralization with guardrails: strong governance, clear risk tiers, and ethical standards. The emphasis is on shared learning with partners and clients - everyone is figuring out the same questions together.
Alicia Pittman, Chief People Officer, Boston Consulting Group
Workflows and business models are changing at the same time, and HR is adapting to both. About a quarter of the firm's work now involves AI. Roughly 90% of employees use it regularly, and more than half use it daily.
To support that, they built a 1,400-person enablement network, upskilled 100 team coaches, embedded experts into project teams, and ran innovation competitions. HR led core platform changes too - consolidating six IT systems into one and weaving AI through recruiting, performance, and development. They're testing voice tools, chat interfaces, and AI avatars for real-time coaching. These tools don't replace managers; they give them time back for judgment and relationships.
Agi Garaba, Chief People Officer, UiPath
Automation is in the company's DNA, but agentic AI is the next step. One AI agent, close to production, helps with performance reviews: drafting self-assessments, collecting feedback, and consolidating inputs for managers.
The agent won't make rating decisions. It speeds up the admin so managers can focus on feedback quality and HR can focus on career paths. Fear is normal, but it should push learning. This shift reaches every level - not just entry roles - and there's time to prepare.
What this means for your HR roadmap
Set the mandate for AI in HR
- Define where AI adds value: cost, speed, quality, or experience - pick two to prioritize.
- Adopt "human-in-command" by design: AI drafts, people decide.
- Create an AI operating model: central standards with local experimentation.
Build the capability stack
- Baseline fluency for all: prompts, verification, privacy, and bias awareness.
- T-shaped teams: technologists build business acumen; business leaders build digital fluency.
- Communities of practice: coaches, evangelists, and embedded experts inside real work.
Governance you can live with
- Model and tool inventory with risk tiers (low, medium, high).
- Human review on high-impact decisions; clear escalation paths.
- Audit trails for prompts, outputs, and approvals.
- Data rules: red lines, retention, and approved sources for retrieval-augmented answers.
- Reference frameworks like the NIST AI Risk Management Framework.
Start with practical HR use cases
- Talent acquisition: job description drafts, sourcing summaries, interview scheduling, and candidate Q&A - always with bias checks and human review.
- Learning and development: role-based curricula, skill-gap suggestions, and practice scenarios.
- Performance and feedback: self-assessment drafts, multi-source feedback consolidation, and goal clarity; ratings stay with managers.
- Employee services: policy chat for quick answers, routing to humans for nuance.
- Workforce planning: skills inference from profiles and projects to inform reskilling paths.
Your 90-day plan
- Days 0-30: Form an AI council, choose three high-value use cases, and publish a lightweight policy. Launch a short, practical training for managers and HR.
- Days 31-60: Pilot with small teams. Measure time saved, quality lift, and risk indicators. Capture real examples for coaching.
- Days 61-90: Scale the winners. Integrate into systems, add guardrails, and update job expectations. Share stories and numbers.
Metrics that matter
- Cycle time: time to hire, time to fill, time to complete reviews.
- Quality: hiring manager satisfaction, feedback clarity, and learning completion with retention.
- Manager leverage: hours shifted from admin to coaching and talent decisions.
- Adoption and safety: active users, output accuracy, bias findings, and compliance incidents.
- Employee sentiment: confidence using AI and perceived fairness.
Guardrails for AI agents
- Scope the agent: what it can draft, collect, and summarize - and what it never decides.
- Source of truth: connect to approved data only; log every action.
- Clear handoffs: require manager sign-off for performance, pay, and sensitive actions.
- Change control: versioning, reviews, and sunset criteria for every agent.
Lead the change with skill-building and proof
Make AI fluency part of every role. Pair training with real projects, a simple policy, and obvious wins. Keep humans responsible for judgment, relationships, and culture - that's the edge.
If you want structured learning paths for HR and people leaders, explore role-based options here: Courses by Job. To stay current, browse the latest programs: Latest AI Courses.
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