As the number of use cases for AI in human resource management grows, the opportunity is clear: HR leaders and Chief AI Officers (CAIOs) need to partner up now, or risk falling behind.
Organisations that fumbled the ball when implementing AI in recruitment have suffered the consequences of poor candidate experience, discriminatory hiring practices, and damage to employer branding. With AI already becoming a key part of every organisation’s future-of-work strategy, HR teams need to exercise more oversight when it comes to AI strategy and implementation.
Their key allies? CAIOs. Chief AI Officer roles are no longer rare. According to CIO Dive, CAIO appointments are growing 70 per cent year-over-year, with one-third of global enterprises creating AI-focused executive roles in 2025. This means that AI leadership has now become a C-suite function—and HR must secure a seat at the table to shape strategy, ensure ethical implementation, and collaborate with CAIOs to drive meaningful outcomes.
Why HR and chief AI officers must partner in 2025
On the surface, it may seem that HR leaders and CAIOs hold different agendas. HR owns recruitment, talent management, and workforce planning, whereas CAIOs own data and algorithmic innovation.
But in today’s AI landscape, these mandates overlap. From recruitment to reskilling, every people decision is becoming a data decision—which means that in order to achieve AI deployment that’s smart, fair, and human-centric, HR leaders and CAIOs must be closely aligned on a common mission.
Critical to this is a close partnership between HR leaders and CAIOs to establish and enforce ethical guardrails. This is reflected in the emerging view that HR teams hold shared accountability for the ethical and compliant use of AI. As the saying goes, “With great power comes great responsibility”, and HR now plays a pivotal role in ensuring responsible AI adoption. As the recent Workday lawsuit shows, that responsibility is taken seriously.
How can AI improve HR without increasing bias?
Streamlined talent acquisition & onboarding
AI is powering smarter job ads, faster screening, and bias checks. For example, Natural Language Processing (NLP) tools can surface top-fit candidates while flagging job postings for hidden gendered language. Done right, this cuts time-to-hire and raises candidate quality. But this is not automatic—the risk of amplifying bias is real if AI models are trained on flawed or incomplete data, which is why it is critical that HR and CAIOs align on diversity goals.
Personalised employee engagement & experience
AI tools such as pulse surveys and sentiment engines can spot disengagement before it becomes turnover. But data means little without human context. AI cannot—and should not—be the sole decision-maker when it comes to workforce management, but it can provide deeper insights for HR leaders to identify early warning signs and proactively address issues before they snowball.
Data-driven workforce planning & analytics
Talent forecasting? Succession planning? Attrition prediction? This can all be a reality when you have clean data, shared KPIs, and dashboards set up by CAIOs in partnership with HR leadership. CAIOs supply the infrastructure, but HR is responsible for ensuring that data is used in ways that support culture, not erode it.
Compliance & bias mitigation
From flagging pay inequities to ensuring fair hiring, AI can help monitor everything. But it only works when HR owns the ethical policy and CAIOs build the right audit tools. That’s why HR must lead on ethics and fairness by setting the standards, policies, and red lines for AI use. When done right, it builds internal trust by showing employees that AI isn’t a metaphorical black box—it’s part of a transparent, accountable, and conscious process.
How to use AI in HR: a 5-step playbook
1. Assess readiness and map use cases
Start by auditing your current data, tools, and workflows, and picking a few high-impact areas—like resume screening or sentiment analysis—that can be enhanced with AI.
American Chase, a multinational company, did exactly this and experienced 80 per cent faster screening and a 20 per cent improvement in hiring accuracy after implementing an AI-powered resume parsing and candidate matching system.
2. Prioritise quick-win pilots
Quick wins build confidence, so start with low-risk, high-value projects that can deliver value fast and act as a proof of concept for more advanced initiatives.
Mastercard, for example, launched an AI-powered career-coaching agent in low-risk internal pilots. It has since been used by 90 per cent of employees, with one-third of participants seeing a role change or promotion within the company, effectively opening new pathways for employees to work, grow, and manage their careers.
3. Establish governance
Have CAIO and HR coauthor bias audits, vendor selection criteria, and compliance checks. To ensure ethics stays at the core of their AI ambitions, AstraZeneca formed a Responsible AI Governance Committee comprising privacy, legal, engineering, and HR leaders to conduct ethics‑based audits before any deployment. Specifically for HR workforce analytics, they chose to prioritise privacy, accountability, and ongoing security of their employees’ data.
4. Scale successful solutions enterprise-wide
Once an AI pilot proves its value—whether it’s in hiring, engagement, or workforce analytics—the next step is to operationalise it. That means moving from test environments to full integration of core HR systems such as payroll, HCM platforms, or LMS systems. As success hinges on people, not software, focus on structured onboarding and training for HR personnel so they are equipped with the right skills to interpret and assess AI-driven recommendations, not simply accept them at face value.
This is critical as AI tends to behave differently at scale. Users play a crucial role in watching out for emerging issues such as model drift, fairness anomalies, or unexpected behavior.
5. Monitor outcomes & recalibrate
All business processes are a continuous improvement process, and the same goes for technology. Once you’ve scaled your AI solution, it’s important to continue tracking, auditing, and iterating. Establish a benchmark for measuring outcomes, run regular bias checks, and continuously refine models and practices.
Future outlook: HR, CAIO & the converged people-tech function
The future of AI oversight may land not with a “pure tech” executive—but with HR. As noted by HR Brew, a new role is emerging at the intersection of people and AI stewardship, and it has been dubbed the Chief Human‑and‑AI Resources Officer (CHAIRO). Moderna is already ahead of the curve, recently merging its technology and HR functions into a single unit and appointing its HR chief to the role of Chief People and Digital Technology Officer.
This trend will become increasingly relevant as AI evolves from a tool to be deployed to a core part of who we employ and how we work. Some organisations are already referring to AI models as “digital workers”. As HR leaders already understand workplace ethics, skills, and governance, this makes them a natural steward for AI agents as part of the workforce. It is not such a leap to see the role of a converged People-Tech function as responsible for AI onboarding, retraining, ethics, and lifecycle management—just as if AI were any other employee.
Need an action plan to align HR and AI strategy? Talk to RMI’s consultants to accelerate responsible AI adoption.
