Revolutionizing Enterprise Operations: The Role of HR in Implementing AI at e&

Enterprises are increasingly turning to AI to revolutionize their operations, starting with the backbone of the organization – human resources. This shift towards AI integration is evident in the way large companies are reimagining their workforce systems. One such example is telecommunications giant e&, which is transitioning its HR operations to an AI-first model, covering a workforce of approximately 10,000 employees. The company is leveraging Oracle Fusion Cloud Human Capital Management (HCM) deployed in an Oracle Cloud Infrastructure dedicated region to facilitate this transformation.

This strategic change goes beyond simply introducing AI features; it involves a fundamental restructuring of how HR processes are managed. The implementation of automated and AI-driven tools is expected to streamline recruitment screening, interview coordination, and employee learning recommendations. The overarching goal is to standardize processes across different regions and provide managers with expedited access to workforce data and insights.

HR as an enterprise AI proving ground

HR serves as a logical entry point for enterprise AI initiatives due to the repetitive nature of many HR tasks, such as candidate matching, onboarding, leave management, and training assignments. These standardized workflows generate consistent data patterns, making them ripe for automation. By integrating AI into HR processes, organizations can test the reliability, governance, and user acceptance of AI technologies in a controlled environment before expanding into more sensitive areas.

The choice of infrastructure also reflects the delicate balance between innovation and compliance. The deployment of the system in a dedicated cloud region by Oracle is designed to address data sovereignty and regulatory concerns. For multinational corporations, managing workforce data involves navigating privacy laws, employment regulations, and corporate governance requirements. By running AI tools in a controlled environment, companies aim to mitigate risks while exploring the benefits of automation.

Governance, compliance, and internal risk management

The adoption of AI in enterprise settings often begins with internal transformations rather than external disruptions. While customer-facing AI systems garner attention, they also pose reputational and operational risks if they malfunction. In contrast, HR platforms operate behind the scenes, allowing for easier monitoring, auditing, and correction within existing governance frameworks.

Research indicates that organizations are increasingly transitioning AI projects from pilot phases to production environments, with a focus on productivity and workflow automation. This shift towards scaled deployment is particularly evident in administrative and operational processes, which serve as practical entry points for AI integration.

Workforce systems offer a natural environment for AI assistants and agents to thrive. HR teams frequently handle employee queries related to policies, benefits, and training, making them ideal candidates for conversational AI tools. By embedding such tools into HR workflows, organizations can reduce manual workload and improve employee access to information.

Scaling AI inside the organization

The evolution of HR automation with AI is not merely about introducing new technology but expanding the scope of what can be automated. Traditional HR software focused on record-keeping and workflow management, while AI adds predictive matching, pattern analysis, and decision support capabilities. This expansion raises important governance considerations such as data quality, bias mitigation, auditability, and employee trust.

While automating certain HR functions reduces the need for manual coordination, it also emphasizes the importance of human oversight in policy interpretation, employee engagement, and exception handling. Organizations implementing AI-driven systems must establish clear escalation paths and review processes to prevent over-reliance on automated outputs.

The current trend in AI adoption involves large-scale deployments covering thousands of employees, transforming AI from an experimental phase into operational infrastructure. This necessitates a real-time focus on reliability, training, and change management to ensure consistent performance across different regions, languages, and regulatory frameworks.

As enterprises seek low-risk entry points for AI integration, workforce operations are likely to remain at the forefront. These operations offer structured data, repeatable workflows, and measurable outcomes that are conducive to automation while still allowing for human judgment. The experiences of early adopters in HR automation will shape the pace at which other internal functions, such as finance and procurement, follow suit.

(Image by Zulfugar Karimov)

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