Enterprise process automation and data governance to drive AI at scale.

Randoncorp's Corporate Procurement area operates in an environment serving more than 27 companies, with processes distributed across different corporate systems and reliance on manual activities. This scenario generated a high volume of operational tasks, alongside challenges related to data quality, standardization, and consistency. In addition to the operational workload, the complexity of system architectures combined with manual workflows made it difficult to maintain reliable and up-to-date information. In an increasingly data-driven context, this directly impacted process efficiency and the ability to generate consistent business insights. At the same time, the evolution of Artificial Intelligence initiatives highlighted a common corporate challenge: inconsistent databases affected by incomplete records, lack of standardization, historical entry errors, and processes executed in varied ways. Without a trustworthy foundation, AI’s potential to support strategic decisions and analytics remains compromised.

DB initiated this journey through a structured automation strategy, leveraging RPA (Robotic Process Automation) as an enabler for digital transformation and a foundational step toward AI at scale. The effort began with process mapping and review, identifying repetitive, time-consuming activities such as price updates, item registration, creation and maintenance of purchase contracts, delivery date changes, order resends, and tax updates. From this assessment, standardized workflows were established and automations with the highest operational and business impact were prioritized. The initiative involved interviews with end-users and validation sessions with managers from participating departments, allowing the team to identify operational pain points, refine workflows, and align solutions with the department's strategic guidelines. This process laid a robust foundation for RPA deployment, reducing the need for recurring adjustments and increasing long-term automation stability. At the end of the assessment, it became clear that the goal was to reduce operational workload for teams, enhance corporate data quality, improve process standardization, and prepare the organization for scalable AI adoption.

Automation development occurred seamlessly through a partnership between Business and IT teams, ensuring alignment on tool selection, demand intake workflows, and ongoing solution support. Prioritization criteria included operational impact, time savings, required effort, activity frequency, tax and legal risks, compliance, regulatory obligations, audit impact, project continuity, financial impact, and operational efficiency. Beyond automating recurring tasks, the RPAs actively improved data quality by executing system adjustments, updating document statuses, supporting negotiation formalization, and performing data cleansing and standardization. To ensure reliability and scalability, automations adhered to a structured governance framework—including detailed mapping, process documentation, error-scenario definitions, controlled testing, and post-deployment monitoring until stabilization. As a natural evolution of the initiative, AI agents were incorporated to support the construction of analytical databases and strengthen decision-making. This approach captured immediate gains through automation while laying the necessary groundwork for AI at scale. Key project achievements include: - Over 30 automation projects delivered for Corporate Procurement since 2025. - Over 14,000 automated executions performed across corporate systems. - More than 7,000 hours saved annually in operational activities. - Significant reduction in manual tasks and increased team productivity. - Expanded service capacity for requesting areas without a proportional increase in operational effort. - Improved corporate data quality, standardization, and consistency. More than automating tasks, this project established the foundation for sustainable transformation. It demonstrates that combining process standardization, information governance, automation, and AI delivers immediate gains while preparing the organization to evolve in a scalable, data-driven way.