By Rizwan Shaikh, Senior Director, Expleo
While technology evolves at a rapid pace, businesses are still overwhelmed by the sheer volume and complexity of data. Customer records, invoices, product details, and financial transactions are just a fraction of the data deluge organisations manage daily.
For decades, Master Data Management (MDM) relied on rule-based systems and tedious human intervention to maintain clean and accurate data. The emergence of Artificial Intelligence (AI) and Machine Learning (ML) enabled data to be cleaned and organised more effectively, however, human intervention was still necessary.
Today, an AI agent refers to software that can understand its environment, make decisions, and take action to achieve specific goals. Agentic AI, which focuses on AI agents that can act autonomously, has progressed from rule-based systems to more sophisticated systems that leverage Natural Language Processing (NLP) and Large Language Models (LLMs) for reasoning, decision-making, and task completion. These systems enable them to adapt to complex environments and solve problems with minimal human intervention.
Agentic AI brings intelligence, autonomy, and real-time decision-making to MDM.
Yet, many businesses are adopting AI with traditional approaches by testing small-scale automation pilots but are struggling to scale AI to its full potential. In doing so, they overlook the real opportunity—Agentic MDM as a long-term, scalable advantage.
With compliance standards tightening, data silos growing, and legacy MDM processes creaking, it’s time for a rethink. Here’s why—and how—Agentic AI is the future your business needs.
Moving from AI-assisted to AI-led MDM
Just as the internet reshaped communication, Agentic AI is redefining how businesses manage and govern data—only this time, AI is calling the shots. If traditional AI in MDM is like a helpful assistant, Agentic AI is the decision-maker.
What’s the difference?
- AI in MDM (human-led):
- Automates repetitive tasks like data ingestion, data cleansing, standardisation, and deduplication.
- Uses machine learning to detect patterns and flag anomalies in data quality.
- Improves data governance by enforcing rules for compliance and security.
- Agentic AI in MDM (AI-led):
- Ensures data is ingested and stored in the correct bins.
- Self-corrects data inconsistencies without waiting for human approval.
- Learns from past mistakes to continuously improve accuracy.
- Adapts to new data structures without requiring new reprogramming.
- Prevents errors before they happen instead of fixing them after.
The shift from human-led to AI-led data management is about fundamentally reimagining how data quality drives business value. Organisations that view AI as merely a faster way to do the same old processes are missing the transformation potential.
When businesses scale AI to be self-learning and adaptive—reducing human intervention and eliminating inefficiencies before they become business problems—that’s when transformation happens.
AI and data have evolved. Your MDM process should, too.
Agentic AI in action
During our executive dinner with Element 22 (in the USA), we explored how Agentic AI is reshaping Master Data Management (MDM) across industries, and the consensus was that traditional MDM can’t keep pace with today’s data complexity.
Unlike traditional MDM that follows static rules, Agentic MDM applies contextual understanding. For instance, in retail, when encountering a new product variant, it can intelligently classify and link it to existing hierarchies based on similar products—without explicit programming for each scenario, ensuring better inventory and pricing decisions.
Here’s how Agentic AI is delivering value across industries
- Banking and financial services
- Automates compliance checks, ensuring real-time regulatory adherence.
- Reduces fraud risks by identifying hidden patterns in transactional data.
- Improves customer experiences by maintaining a single, accurate customer view across all banking services.
- Retail and supply chain
- Eliminates duplicate supplier records, fixing inconsistencies autonomously.
- Predicts stock shortages based on historical buying patterns.
- Could even negotiate supplier contracts autonomously based on data-driven pricing insights.
- Manufacturing
- Tracks supply chain data, detecting inconsistencies before they cause delays.
- Predicts equipment failures, optimising maintenance schedules.
- Improves inventory planning, reducing excess stock and shortages.
- Life sciences and healthcare
- Harmonises clinical trial data, speeding up drug development timelines.
- Assists doctors by providing instant insights on patient histories.
- Automates healthcare compliance, ensuring patient records meet industry standards.
Beyond governance and automation, Agentic AI also plays a role in intelligent decision-making. By analysing patterns and predicting data trends, it helps businesses make more informed, context-aware decisions in real-time—reducing uncertainty and improving accuracy across operations.
Gartner predicts that by 2028, 33% of enterprise software will incorporate Agentic AI—up from less than 1% in 2024. With 80% of C-suite executives acknowledging that AI is transforming their industries, the clock is ticking for businesses that fail to modernise. (Source: Integrating AI: Navigating the next wave of business transformation)
The data trust paradox
Most discussions around Agentic AI in MDM focus on efficiency, automation, and accuracy. But if AI is making critical decisions about enterprise data, who’s ensuring those decisions are transparent, ethical, and accurate? This is the data trust paradox.
Businesses assume AI gets ‘smarter’ over time, but without the right guardrails, it can drift away from accuracy. Data isn’t static—it ages. And just like expired food in your fridge, expired data can cause serious issues if left unchecked.
With greater autonomy comes greater responsibility. This is why businesses need explainable AI models in MDM—ones that provide a full audit trail of their decisions. AI should justify why it flagged a compliance risk or why it rejected a supplier’s data. Otherwise, we are not managing data—we are managing AI guesswork.
If an AI autocorrects a data record, businesses need to know:
- Who made the change? (AI or human?)
- Why was it changed? (Rule-based or learned from past errors?)
- Can we undo it if it’s wrong? (Auditability)
Balancing autonomy with accountability builds trust, and trust is what turns AI from a tool into a competitive edge.
Implementation challenges
The path to Agentic MDM isn’t without challenges. Organisations typically face resistance around AI trustworthiness, legacy system integration, and data governance restructuring. Other common roadblocks include:
- Data siloes that limit AI’s cross-functional learning
- Organisational resistance to AI-driven decision-making
- Insufficient metadata for AI to understand data context
- Legacy systems with limited API connectivity
Our experience shows that successful Agentic MDM implementations begin with high-impact, low-risk use cases that demonstrate value while building organisational confidence in AI’s capabilities.
The takeaway from our discussion
One thing is clear: AI is no longer just a tool—it’s becoming a decision-maker in its own right. But decision-making without accountability is a risk. And without transparency, Agentic AI in MDM risks becoming an ungoverned ‘black box.’
Move from vision to action in three simple steps:
- Assess your MDM maturity: Map your current data processes—where are the bottlenecks (e.g., silos, manual fixes)?
- Pick a high-impact use case: Start small—say, deduplicating customer records or automating compliance checks.
- Partner with experts: Engage experts with AI as the core of their business to design a pilot that scales seamlessly.
The real competitive advantage won’t come from AI doing things faster—it will come from AI doing things transparently and responsibly. That’s the future of Agentic MDM as we see it.
