How Boomi’s Agile Data Management Enables Agentic AI to Automate Complex Business Processes
Boomi’s platform enables agile data management and explainable AI, improving data quality by up to 10% for reliable agentic AI. This boosts automation and decision-making across businesses.

How the Boomi Enterprise Platform Enhances Agentic AI with Agile Data Management
Agentic AI is transforming how businesses automate decisions and complex tasks with minimal human input. For these autonomous agents to function well, they require access to diverse and high-quality data. The Boomi Enterprise Platform addresses this by enabling agile master data management and data imputation, making data ready and reliable for AI applications.
Chris Hallenbeck, senior vice president and general manager for AI and platform at Boomi LP, explains that the platform supports lightweight master data management using agents that clean and refine data quickly. This approach allows teams to create golden records and improve data quality by up to 10% without waiting months or years for typical data mesh projects to mature.
Scaling Human Effort with Agentic AI
Agentic AI can automate repetitive business processes such as invoice reconciliation and manual journal entries, reducing errors and speeding up workflows. These tasks often delay closing financial books on time due to the need for heavy manual intervention. With explainable AI integrated into Boomi’s platform, workflows become smoother and more auditable, easing pressure on accounting teams.
Transparency in AI decision-making and clear goal representation are critical. They allow teams to trust autonomous agents and ensure compliance with regulatory requirements. This level of automation not only improves efficiency but also supports better decision-making across departments.
Data Quality and Accessibility: The Foundation for Agentic AI
Data quality issues—such as noisy, biased, incomplete, or outdated data—can severely impact AI outcomes. Shawn Rogers, CEO of BARC US, points out that AI often forces organizations to confront their data challenges head-on before deploying autonomous agents that interact with customers or manage inventory.
Organizations must prioritize data accessibility and orchestration alongside quality to ensure their AI systems operate reliably. Without this foundation, agentic AI risks making poor decisions that can damage business operations and stakeholder trust.
AI Readiness: Preparing for Effective AI Adoption
Building a solid foundation for AI adoption is crucial for growth and resilience. However, many organizations lag in AI readiness. According to Rogers, only about 21% of companies currently using AI have established strong technology and policy frameworks to support it effectively.
The majority are still determining priorities and strategies, often getting distracted by superficial metrics like the number of agents deployed rather than focusing on quality and integration. Establishing clear policies, robust data practices, and scalable platforms like Boomi’s can help organizations move beyond experimentation to real AI-driven value.
- Agile master data management accelerates AI project delivery.
- Explainable AI ensures transparency and auditability in critical processes.
- Data quality and orchestration are essential to prevent costly AI errors.
- Strong AI readiness frameworks distinguish leaders from laggards in adoption.
Management teams looking to adopt agentic AI should prioritize building flexible data infrastructures and invest in platforms that support rapid data cleansing and imputation. This approach reduces delays and boosts confidence in AI-driven decisions.
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