Traditional data governance and management models were built for control rather than adaptability. However, today, with AI being a major driver in global data generation, erstwhile data management models can no longer keep pace with AI fueling the data explosion. According to a Gartner study, Agentic AI is expected to resolve 80% of common customer service issues without human intervention by 2029. According to another Gartner report, 15% of day-to-day work decisions will be made autonomously with Agentic AI over the next three years. Additionally, 33% of enterprise software applications are expected to adopt Agentic AI, up from less than 1% in 2024. Data management is another field where we have observed Agentic AI making significant in-roads.
While Agentic Data Management does indeed redefine how enterprises govern and optimize data, we take this principle one step further with our Agentic Data and Application Management solution (ADAM). ADAM enables enterprises to manage both data and applications using AI agents.
The dual focus of managing data and applications with Agentic AI has helped us create a self-learning ecosystem where apps and data continuously inform and optimize each other. ADAM unites data governance, engineering, observability, and application management under a single framework. It helps enterprises nurture data-driven intelligence and application-level agility and resilience. Think of ADAM as an autonomous AI-driven data ecosystem. AI agents automate every stage of the data lifecycle, from migrating legacy data to ensuring high-quality governance and engineering.
Data governance has long been the foundation of enterprise intelligence; however, static frameworks are no longer sufficient. The rise of Data Governance 3.0 marks a shift from manual oversight to agentic AI for data management, where intelligent AI agents proactively manage quality, lineage, and compliance.
For example, agentic data quality agents now generate governance rules, recommend fixes, and even impute values to improve reliability. Meanwhile, AI-enabled data lineage tools provide end-to-end visibility across disparate systems, tracing data origins and transformations to ensure accuracy at every stage of the lifecycle.
Actionable results from Brillio deployments include:
At the heart of ADM are intelligent data agents—autonomous, adaptive stewards that handle critical functions once reserved for large teams. These agents detect anomalies in real time, enforce governance dynamically, and optimize workflows across hybrid and multi-cloud environments.
Brillio’s ADAM framework, for example, includes log analysis agents that proactively detect and resolve issues, conversion agents that accelerate data engineering workflows, and insights agents that analyze structured and unstructured data to uncover patterns.
Business outcomes powered by Brillio’s agents:
The most compelling promise of ADM is the ability to create self-healing data systems. Rather than waiting for errors or breaches to escalate, these systems autonomously identify and fix issues.
Imagine a scenario where inconsistent data triggers automatic remediation, a compliance gap is patched in real-time, or a failing pipeline is rerouted seamlessly—all without manual intervention. Brillio’s observability and log analytics agents make this possible, monitoring jobs in real time, performing automated root-cause analysis, and even resolving issues without escalating them to human teams.
Measurable impacts include:
ADM is no longer theoretical. Industries are already realizing their impact.
In financial services, intelligent agents automate compliance checks, monitor for fraud in real time, and deliver dynamic risk scoring models. In healthcare, data quality and governance agents unify patient data across fragmented systems, maintaining HIPAA compliance while enabling faster clinical decision-making.
In retail, hybrid insights agents orchestrate data flows across channels, personalizing customer experiences at scale, and drastically reducing workloads through agentic support.
In manufacturing, engineering agents automate data conversion and monitor equipment data to enhance quality assurance and improve supply chain resilience.
Cross-industry results achieved with ADM include:
Adopting ADM is not without its challenges. Many organizations encounter:
The path forward lies in a phased approach. Brillio helps organizations start small with pilots tied to measurable outcomes, using agentic frameworks to demonstrate value quickly.
Before adopting ADM, enterprises must assess their readiness to do so. The first step is infrastructure: cloud-native, API-driven architectures are the backbone of scalable agentic platforms. AI maturity is the next consideration – without well-trained models, agents cannot deliver meaningful governance outcomes.
Enterprises must also examine their security and compliance readiness, ensuring transparency and auditability are built into autonomous processes. Ultimately, readiness hinges on people: organizations must foster a culture that is open to automation, where human teams view agents as collaborators rather than replacements.
The shift to agentic data management is not simply about upgrading tools; it represents a paradigm shift in how enterprises approach data. Static governance gives way to adaptive intelligence, where agents ensure that data is not only managed but also continuously optimized for informed decision-making, enhanced resilience, and sustained growth.
At Brillio, our AI-first approach empowers enterprises to unlock agility, compliance, and business impact by making data ecosystems self-learning, collaborative, and future-ready, catalyzed by enterprise data automation.
At Brillio, data should be more than an asset – it should be an advantage. With our AI-first accelerators, platforms, and domain expertise, we help enterprises transition seamlessly into the agentic era.
Connect with us to explore how Agentic Data and Application Management (ADAM) can transform your enterprise.
Q1. What is Agentic Data Management (ADM)?
ADM utilizes intelligent AI agents to automate the management of data quality, governance, engineering, and insights. It shifts data management from manual processes to adaptive, self-learning systems.
Q2. How does Brillio’s ADAM framework build on ADM?
ADAM, or Agentic Data and Application Management, extends ADM by adding application management. This creates one intelligent ecosystem where data and applications work together to optimize performance and agility.
Q3. What measurable results can enterprises expect with ADM?
Brillio clients have seen outcomes such as:
Q4. How is my organization ready for ADM?
Enterprises should check for:
Q5. Which industries benefit most from ADM?
ADM delivers value across industries:
