Building an AI-driven, human-centric workplace: lessons from Home Credit
AI isn't replacing work. It's redefining what work means and where humans create the most value. That's the point Van Thi Hong Hanh, chief people officer at Home Credit Vietnam, emphasized at recent HR industry events in Vietnam.
Her stance is clear: preserve humanity, move faster on skills, and use AI to remove repetitive tasks so people can focus on judgment, creativity, and connection. This is especially urgent with Gen Z and Alpha entering the workforce at scale.
Why the human core matters
The skills gap is widening as automation outpaces how fast teams can reskill. Employees feel both excited and anxious about what's next. HR's job is to build trust, increase capability, and create clear paths to grow.
That starts with leadership mindsets and a strong culture. Hanh puts it plainly: the chief people officer must also think like a chief information officer-owning digital HR, setting guardrails, and making AI humane, transparent, and empowering.
What Home Credit is doing
Home Credit treats AI as an enabler. It streamlines routine work and frees up time for higher-value projects. The company anchors this shift in culture and measurable outcomes, not slogans.
The results point in the right direction: 96% of employees say they feel proud to tell others they work at Home Credit. The employee Net Promoter Score is 80, and retention continues to improve-among the highest in Vietnam's consumer finance sector.
Personalised HR for a multi-gen workforce
Over 45% of Home Credit's team is under 30. That brings fresh energy and new expectations. The takeaway: one-size-fits-all policies break down fast across generations.
Home Credit's answer is to personalise experiences across the employee lifecycle:
- Career: clear, multi-path progression and internal mobility
- Work design: flexible hours and hybrid options where roles allow
- Learning: varied formats (microlearning, projects, coaching) and topics that match role and ambition
- Talent programs: Home Racer and other initiatives to spot and grow potential early
- Wellbeing: mental health, family support, and financial literacy via Home Sport, Home Smarts, and family insurance
As Hanh puts it: the key is to learn fast, encourage lifelong learning, and let knowledge compound into new value for people and the business.
A practical playbook HR leaders can use now
- Define the work: map tasks by role into automate, augment, or human-only. Prioritise quick wins that save time weekly.
- Set governance: publish an AI-in-HR policy (use, data, fairness, human oversight). Adopt a risk framework such as the NIST AI RMF. See framework
- Skill taxonomy: list the skills your strategy needs; connect them to roles, levels, and learning paths.
- Learning engine: mix microlearning, practice projects, and coaching. Reward application, not seat time.
- Manager enablement: train managers to redesign roles, set expectations, and coach with AI in the loop.
- Change communications: be upfront about why, what changes, and how AI is used. Share before/after workflows.
- Data hygiene: reduce personal data in HR workflows; use secure environments; log model usage.
- Employee voice: create channels for feedback, concerns, and ideas. Close the loop publicly.
- Pilots with purpose: start with 2-3 HR use cases (sourcing, screening support, knowledge search, policy Q&A). Measure time saved and experience impact.
- Scale what works: standardise tools, playbooks, and guardrails. Sunset what doesn't add value.
Metrics that matter
- Trust and experience: eNPS, psychological safety, internal mobility rate
- Capability: skills proficiency by role, completion and application rates for learning
- Productivity: time saved per process (recruiting cycle time, ticket resolution, policy lookup)
- Talent health: quality of hire, regretted attrition, manager effectiveness
- AI quality: model accuracy for HR use cases, fairness checks, human-override rate
Guardrails for responsible AI in HR
- Transparency: tell employees where and how AI is used in decisions or recommendations.
- Consent and control: give employees access rights and opt-out paths where feasible.
- Fairness: run bias tests on datasets and outputs; document mitigations.
- Human in the loop: keep humans accountable for decisions that affect people.
- Data minimisation: store only what you need, for as long as you need it.
- Auditability: log prompts, outputs, and actions for sensitive workflows.
- Wellbeing: monitor workload and stress as automation changes roles; expand mental health support.
Upskilling next steps
If your team is building HR, L&D, or people-ops skills for AI, map roles to learning paths and start small with live projects. A curated catalog by job can help you pick the right courses faster. Explore options
The takeaway
AI can help people do their best work-if culture, skills, and guardrails come first. Home Credit's approach shows the path: lead with purpose, personalise the employee experience, measure what matters, and keep humans in charge.
That's how you build an AI-driven workplace people trust-and want to grow in.
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