Key Takeaways
- Agentic AI moves beyond analysis into autonomous action, meaning errors in master data trigger real operational, financial, and compliance consequences.
- Clean, governed master data isn’t a prerequisite you achieve all at once. It’s a targeted, initiative-by-initiative foundation you build strategically to enable specific AI workflows.
- Organizations that start small, govern intentionally, and align master data to defined business goals are the ones turning AI ambition into measurable ROI.
There’s a version of AI that analyzes. It surfaces trends, flags anomalies, generates summaries. Most enterprises have lived in this world for years now.
Then there’s a version of AI that acts. It updates customer records, triggers procurement workflows, routes financial transactions, and makes eligibility decisions autonomously, at speed, without a human in the loop for every step.
That second version is Agentic AI. And it changes everything about how organizations need to think about their master data.
In our recent webinar, The Danger of AI: What Happens When Agentic AI Acts on Bad Master Data, I sat down with Precisely colleagues Max Kanaskar, Senior Value Advisor at Precisely, and Chris Eatmon, Principal Product Manager, to unpack what it takes for organizations to get enterprise master data ready for the agentic moment we’re in.
Here are some of the key ideas that came out of that conversation.
Why Agentic AI Raises the Stakes on Master Data Management (MDM)
One of the most useful mental models from our conversation came from Max. Think of Agentic AI like a high-speed water pipe running through a farm. With clean water, the whole crop thrives. But contaminated water causes the damage to spread fast and far.
That metaphor captures something important: AI agents don’t wait. They act. And when they act on flawed master data, whether that’s a misclassified customer record, an incorrect supplier attribute, or an undefined product hierarchy, it can lead to serious consequences for your operations.
The Air Canada case is a stark example. The airline’s AI chatbot gave a passenger incorrect refund information, and when the case went to court, the airline was required to honor what the chatbot said. The legal and reputational fallout was real, and it stemmed directly from the AI acting on bad information.
That’s the world Agentic AI introduces. By 2028, Gartner projects that a third of all generative AI interactions will involve autonomous agents. The window to get your data house in order is now.
“Clean, governed master data is really the fuel for Agentic AI capabilities. It allows agents to operate seamlessly, go across different systems and functions, and speak the same language.”
Max Kanaskar
Senior Value Advisor, Precisely
What Does Agentic-Ready Mean for Master Data Management AI Workflows?
This is where the conversation got practical. A common misconception is that organizations need 100% clean master data across every domain before they can move forward with AI initiatives. According to Chris, that framing is both unrealistic and unnecessary.
“I don’t think that’s always the case,” he said. “We have to be strategic about what initiative we’re going after and making sure that the foundational data piece supports that initiative.”
Max agreed, and framed it as an agile data strategy rather than a waterfall one. Leading organizations are identifying specific agentic use cases, mapping which data domains those use cases depend on, and getting that data clean, governed, and structured before flipping the switch on automation.
A distributor Max worked with wanted to build agentic supplier collaboration capabilities, but couldn’t get there without first establishing clean, governed supplier master data. That became the scoping exercise: define what a “good supplier master” looks like, build toward that standard, then layer in the agentic workflows.
The pattern holds across industries:
- Manufacturing: Automating item creation and material master management to enable procurement and production planning workflows.
- Retail and CPG: Using customer master data to power dynamic pricing, promotions, and inventory positioning.
- Financial services: Driving know your customer (KYC) automation through clean customer master data, where a bad record creates gaps in both reporting and compliance.
- Logistics: Cross-referencing customer, product, supplier, and location domains to optimize fulfillment and sourcing decisions.
The common thread is intentionality. The organizations seeing real ROI from MDM and AI are the ones who started with a clear use case, identified the data that use case depends on, and governed that data before letting agents act on it.
WEBINARData Integrity Suite Developer Portal
In this session, we explore how MDM enables responsible and trustworthy Agentic AI, and why aligning it with enterprise governance is essential for trust and scale.
Where Does Data Governance Friction Show Up in MDM and AI Initiatives?
Data governance is the part of the conversation that sounds straightforward in theory and proves incredibly complex in practice.
Max shared an example where he was helping a bank with customer segmentation around high-net-worth individuals. On paper, it sounds like a simple definition exercise. In reality, getting marketing, finance, and individual lines of business to align on a single definition of “high-net-worth individual” took months, because each team had different incentives, different metrics, and different stakes in how the term was used.
Chris experienced the same dynamic working in manufacturing, where a team spent three months trying to define what constitutes a brand versus a sub-brand. The reason that effort took so much time and care was that if the definition isn’t standardized, your P&L statements will report differently every time you slice the data differently.
The takeaway from Max and Chris’ points is that AI can’t do what we can’t tell it to expect. Agentic AI follows governance guardrails: the policies and definitions that tell it how to act and on what. If those guardrails don’t exist, or if they’re inconsistently applied, agents will operate on ambiguous foundations. The governance friction organizations experience in master data management only gets amplified when you add AI.
This is also why MDM and data governance aren’t separable in the agentic context. They work in tandem. Master data management establishes the authoritative data; governance defines the rules for how that data is created, maintained, and used. Together, they become the control plane for what your AI agents actually do.
Getting Started: MDM Best Practices for AI
The one practical framework that emerged from this conversation is that it’s faster and more effective to start with the end goal, not the full data estate.
Chris shared that organizations must focus on what the end goal is. “Start with, ‘What is it that I’m trying to get out of this specific initiative? What are the data elements that support that?’ And then work around that model to get that thing right, so that then you can move on to the next thing.”
Research from MIT found that 95% of organizations see no ROI from AI initiatives because of brittle workflows and poor integration. The 5% that do see ROI tend to be the ones who defined their end state, mapped the workflow components required, and integrated tightly around those.
Chris and Max both framed it as building an “AI muscle.” Start small, define a granularly scoped use case, get the relevant data governed and ready, implement the agentic workflow, measure results. Then expand.
That progression of use case first, data second, governance throughout, agents last, is the pattern that separates organizations building durable AI capabilities from those generating headlines about abandoned initiatives.
Master Data Management for the Agentic AI Era
Agentic AI is here. It’s already embedded in enterprise software roadmaps, vendor offerings, and boardroom expectations. And it will only become more deeply integrated into core business workflows from here.
What determines whether that integration becomes an accelerant or a liability is the quality of the master data underneath it. Clean, connected, governed master data is the infrastructure layer that makes intelligent automation safe to run.
The good news is that you don’t have to have it all figured out before you start. You just have to be strategic about where to begin.
Want to go deeper on this topic? Watch the full on-demand webinar, The Danger of AI: What Happens When Agentic AI Acts on Bad Master Data, to hear the full conversation, real-world examples, and practical frameworks for building Agentic-Ready MDM and AI workflows in your organization.
