With generative AI, HR technology is undergoing a fundamental shift and moving beyond administrative automation towards strategic workforce intelligence. For CIOs and CHROs, this transition is prompting a re-evaluation of what human capital management (HCM) platforms are expected to deliver in an AI-driven enterprise.
In an exclusive interaction with Express Computer, Yvette Cameron, Senior Vice President of Global HCM Product Strategy at Oracle, discusses how AI is changing the role of HR systems, the rise of verifiable skills data, and the governance frameworks organisations must build to ensure trust and transparency in workforce decisions.
From record systems to outcome-driven intelligence
The expectations placed on HCM platforms are evolving rapidly. “Traditional HR systems historically focus on recording transactions, payroll cycles, performance reviews, or workforce administration, after events occur. Today, enterprises expect these platforms to become systems of outcomes,” says Cameron.
She observes that CIOs and CHROs increasingly look for intelligent capabilities that synthesise workforce data, identify patterns, and guide decision-making in real time. Rather than relying solely on dashboards or historical reports, organisations are seeking platforms that help anticipate workforce challenges, surface opportunities, and automate routine processes.
This shift reflects a broader change in enterprise technology strategy. As AI becomes embedded across business applications, HR platforms are moving closer to operational intelligence hubs, helping leaders interpret signals from complex workforce data and translate them into actionable insights while preserving human judgement in final decisions.
GenAI moves from experimentation to practical workforce use cases
While generative AI continues to attract attention across industries, Cameron emphasises that enterprises are prioritising practical, high-impact use cases over experimental deployments. Skills intelligence, workforce planning, and learning recommendations are emerging as some of the most immediate areas where AI delivers measurable value.
She highlights two primary themes shaping adoption. The first is growth, enabling organisations to understand skill gaps, forecast workforce needs, and align talent strategies with business priorities. AI-driven insights help organisations infer skills from multiple signals, enabling more dynamic workforce planning and targeted development programmes.
“The second theme is risk mitigation. AI can help reduce human error in interpreting policies, compliance requirements, or regulatory changes that affect workforce management. By automating certain administrative decisions and flagging potential risks early, organisations strengthen governance without increasing operational complexity,” adds Cameron.
However, she also advises enterprises to remain cautious. AI should augment managerial decision-making rather than replace it. Over-reliance on automation without clear governance structures risks undermining trust, particularly when workforce decisions have ethical or legal implications.
Skills data and digital credentials reshape talent mobility
A long-time advocate for digital credentials and self-sovereign career identities, Cameron believes that verifiable skills data will become central to the future of talent mobility. As organisations shift towards skills-based workforce models, AI’s ability to interpret validated credentials and experiential data becomes increasingly valuable.
She notes that while blockchain-based identity models have yet to reach widespread adoption, the concept of verifiable credentials continues to gain traction. Standards around micro-credentialing and skills verification are evolving, enabling organisations to move beyond static job titles or resumes towards more dynamic representations of capability.
“AI plays a critical role in this ecosystem by aggregating signals from learning systems, work platforms, and verified credentials to build a more accurate picture of individual skills. This allows organisations to match employees with opportunities more effectively, support internal mobility, and adapt to rapidly changing market demands,” says Cameron, adding that rather than relying solely on declared skills, AI-driven inference models can analyse real work patterns and outcomes to identify emerging competencies. She believes this approach will enable more transparent and merit-based talent ecosystems across industries.
Beyond GenAI: The rise of agentic enterprise applications
When discussing the future of enterprise technology, Cameron suggests that the next phase of innovation will extend beyond generative AI interfaces. She points to the emergence of agentic applications, intelligent systems capable of acting autonomously within defined governance boundaries.
“Unlike traditional transactional systems, these applications focus on continuous decision-making and orchestration across enterprise workflows. They interpret signals, recommend actions, and automate routine processes, reducing the time required to implement organisational change” she points out.
For HR technology, this could mean moving from static workflows to adaptive systems that respond dynamically to workforce trends, business priorities, and external signals. While generative AI accelerates productivity today, Cameron believes agentic architectures will define the long-term evolution of enterprise software.
Ethics, governance and the need for explainable AI
With AI getting embedded in workforce decisions, questions around governance and ethics are becoming central to technology strategy. Cameron stresses that trust in AI systems depends not only on organisational policies but also on architectural design.
One key principle is bringing AI to the data rather than moving sensitive information into external systems. “Embedding AI within unified data models and secure infrastructure helps organisations maintain control over privacy, access, and regulatory compliance,” she affirms.
Equally important is explainability. Enterprises must ensure visibility into how decisions are generated, what data influences outcomes, and where human oversight remains necessary. Auditability and transparency become essential as AI-driven recommendations grow more autonomous.
For CIOs and CHROs, this means jointly defining governance frameworks that balance innovation with accountability. Ethical AI adoption requires clear guardrails, robust data integrity practices, and an organisational culture that prioritises fairness alongside efficiency.
A new role for HR technology in the AI era
Cameron observes that enterprise adoption of AI within HCM platforms is accelerating rapidly. What begins as pilot programmes around specific use cases is now expanding into broader deployments across workforce functions. Organisations are moving from isolated experimentation towards operationalisation, embedding AI capabilities across recruiting, performance management, and workforce planning.
Yet she emphasises that technology alone does not define success. “Cultural readiness, leadership alignment, and strong governance structures remain critical as enterprises navigate this transition.”
As HR systems evolve from transactional platforms into intelligent workforce engines, the conversation shifts from automation to outcomes, from managing processes to shaping organisational strategy. For enterprises seeking to remain competitive in an AI-driven future, the ability to combine data intelligence with ethical governance may ultimately determine how effectively technology supports the evolving world of work.