The need for clean, consistent and accurate data is no longer a convenience—it’s a necessity. Whether using machine learning in data science, preparing it for customer insights, or computing precise representations of AI—the base is always an immaculate Master Data Management (MDM) layer. But keeping up with the fluctuating data sources, credentials and versions scattered around vast repositories is not a small task as it requires both time and effort.
Agentic AI is an emerging group of AI systems that are independently capable of autonomous decision-making, reasoning and task execution. Unlike conventional AI needing explicit instructions for every task, agentic AI can plan ahead, adapt and perform based on its intents, the context provided, or systemic feedback. When applied to MDM, it provides opportunity for efficiency, accuracy and scale.
What Is Agentic AI?
Agentic AI refers to AI agents that are capable of handling a degree of autonomy. These kinds of agents can:
- Understand complex instruction and intent
- Make decisions based on context
- Learn from feedback and past actions
- Collaborate with other agents and humans
Agentic AI does not just react—it proactively works toward a common goal.. They are digital co-workers that don’t need micromanaging. These are displayed by AutoGPT, LangChain (frameworks or tooling ecosystems used to build such agents) and enterprise platforms led by Microsoft Copilot Studio or IBM Watsonx.ai are moving in this direction.
MDM and Agentic AI
Traditional MDM tools are occupied with creating a single source of truth by standardizing, cleaning, without duplication and enriching data. It provides data governance capabilities to govern data in the repository.
Here is how Agentic AI is the game-changer:
- Customer 360 view: Agentic AI can be implemented using interactive chatbots that can provide context aware responses to various stakeholders with the organization as well as to the customer. Customer need not go through the internet banking or mobile application; they can leverage either WhatsApp chat or launch organization specific chatbot for interaction. For banks, it can help customers to transact on routine things like bank transfers, payment, balance enquiries, loan entitlements, credit card blocking or unblocking etc.
- Automated Data Stewardship: Data stewardship is one of the critical activities in an MDM implementation, which usually requires manual intervention. We can implement a framework wherein, based on previous activities, the agent learns and helps perform automated entity resolutions, thus reducing the manual efforts. This agentic AI will run from within the MDM tool.
- Automated filling of online forms: Filling out forms in banks is often tedious and many times this information are already present in the bank’s application, which has to be hand written. Agentic AI can help automate filling of forms like KYC, Loan Application, Credit Card application. It can be employed to read data from Aadhar cards, Pan Cards, etc. and leverage existing address and communication details available within the organizations repository. It can enable digital signing wherever applicable. This could also include Form 15G/H based on existing details.
- Contextual AI for Customer Support: Customers often have queries on various banks offerings but the information provided on websites are usually generic. Agentic AI can be used to create a framework to provide customized and contextual information to customers to help her/him make appropriate decisions. This can greatly assist customers in understanding home loans, Education loans, Personal loans & Motor loans. The same can be extended to small businesses, proprietors etc. Customer can also chat with the bot as if it were a consultant. It can cover the loan procedure quickly, show the eligibility, point out probable issues and provide resolutions, interact with CIBIL scores to provide relevant score for discounts, integrate with the automated filling of on-line forms, etc. This will reduce the number of trips the customer has to do to the bank for various purposes. Chat will also allow for video calls at appropriate times with the respective relationship managers to resolve contextual queries.
Business Impact – Introducing agentic AI into MDM encourages:
- Faster on boarding of new data sources and partners.
- Reduced costs of data management and reduction in manual routine with automation.
- Enhanced decision-making from increased trust in data quality.
- Scalable quality of data across cloud, hybrid and on-prem environments.
For industries dedicated to real-time insights like from retail to healthcare and fintech to logistics, this would be a game-changer.
Agentic AI is not meant to replace human data professionals; it is meant to augment the force. Running alongside appropriate governance models and human management, Agentic AI can make MDM a strategic asset.
Cross-industry alignment with a strong AI-first data strategy will require the use of agentic AI MDM. At CRG Solutions, we partner with our customers to solve complex business challenges by bringing the right balance of consulting, technology and services. We help you build a comprehensive analytics strategy right from data to visualization.
Contact CRG Solutions today!
Recent Posts
How AI Is Transforming Jira Service Management by Atlassian
The rapid growth of Jira Service Management (JSM) by Atlassian depends on Artificial Intelligence (AI) as its main aspect and driving force. The implementation of AI technology within JSM enables this platform to expand beyond basic ticketing functions into smarter...
Why Enterprises Are Switching to Jira Service Management by Atlassian
Modern organizations face the challenge of increasing the speed service delivery while maintaining the quality and operational efficiency and collaboration across IT teams, operations and business departments. Today’s teams overtake traditional IT Service Management (ITSM) tools because these tools have...
Unlocking Viewer Loyalty: How Micro-Segmentation Transforms Streaming Engagement
Streaming is a growing consumer market with stiff brand competition, but with the decreasing attention span among customers, viewership hours work as currency and a constraint. With streaming platforms competing amongst themselves for viewership and having loyal users is as...
Leave a Comments
You must be logged in to post a comment.