TL;DR
- Enterprise AI is failing not because of bad models, but because of bad data infrastructure.
- Most data architectures are still fragmented and reconciled after the fact. AI requires a continuously mastered, real-time control layer.
- Agentic MDM replaces point-to-point integrations with a governed, policy-aware data model that enforces trust before autonomous action occurs.
- The organizations that win at AI will not deploy the most models. They will build the control planes those models can be trusted to operate within.
The Real Reason AI Initiatives Are Stalling
Over the last two decades, we have each built enterprise data infrastructure at global scale. Nick across Marketo and Aptrinsic, where he founded and scaled product-led growth infrastructure. Neelesh across Marketo and Adobe, where he was named a Tech Fellow. In every environment, we observed the same structural pattern.
Enterprises do not lack data. They lack a trusted control plane for it.
Fragmented systems produce conflicting records. Brittle integrations break under schema change. Governance models, where they exist at all, slow execution while doing little to reduce operational exposure. Teams spend enormous resources reconciling data after the fact instead of operating on data they can trust in the moment.
That structural gap was the founding thesis for Syncari. It is also, today, the primary reason enterprise AI initiatives are underperforming or stalling entirely.
For us, being named a Visionary in the 2026 Gartner® Magic Quadrant™ for Master Data Management Solutions is a milestone we want to mark honestly. It reflects years of work by an engineering team that made hard architectural bets early, a customer base that trusted us with mission-critical infrastructure before the market caught up, and two co-founders who believed the problem was solvable in a fundamentally different way. We are proud of this recognition. More than that, we are proud of what it took to get here.
“AI is not what’s failing inside most enterprises. The data feeding it is.” — Nick Bonfiglio, CEO
When an AI agent acts on flawed or unauthorized data, the consequence is not a bad report. It is an incorrect decision executed at scale, across every system the agent has been given access to. The exposure compounds faster than any human review process can track.
This is not a model problem. It is an infrastructure problem. And it requires an infrastructure solution.
Access The 2026 GARTNER® MAGIC QUADRANT™ for Master Data Management Solutions Report
What Modern Data Architecture Gets Wrong
Most data architectures today were designed around a different set of requirements: move data from system A to system B, transform it, load it somewhere central, and generate reports. That model worked when decisions were made by analysts reviewing dashboards.
It does not work when the decision-maker is an autonomous agent acting in real time.
The scale of the gap is significant. IDC projects global enterprise AI spending will reach $307 billion in 2025, rising to $632 billion by 2028. Yet according to Gartner, at least 30% of generative AI projects will be abandoned after proof of concept by end of 2025, due primarily to poor data quality and inadequate governance infrastructure (Gartner Data & Analytics Summit, July 2024). Organizations are investing in AI at a pace their data foundations cannot support.
“Most data architectures today are still fragmented. Different systems, different versions of data, and a lot of effort spent trying to reconcile everything after the fact. What’s changing is the ability to create a consistent, mastered layer across all those systems in real time. That means instead of constantly moving and fixing data, you’re actually managing it continuously so everything built on top of it, including AI, can rely on it.” — Neelesh Shastry, CTO
The architectural shift Neelesh describes is not incremental. It is a fundamental rethinking of where governance lives in the data stack.
In traditional architectures, governance is applied downstream: after data has been moved, transformed, and loaded. Quality checks happen retrospectively. Policy enforcement is manual. Lineage is reconstructed rather than captured.
In an agentic architecture, governance must move upstream. It must be enforced at the point of action, before an agent reads a record, before it writes a result, before it triggers a downstream workflow. Data must be semantically structured, policy-aware, and versioned at the moment it is consumed, not corrected hours later in a reconciliation job.
The technical term we use internally is agent-ready data. It is not simply clean data. It is data that carries its own context: provenance, entitlement controls, lineage, and policy state. Data that an autonomous system can act on without introducing risk the organization cannot observe or reverse.
Why MDM Must Become a System of Trust
The industry has historically treated MDM as a data cleaning exercise. Consolidate records. Deduplicate. Standardize formats. Sync periodically. That framing made sense when MDM served reporting systems.
It does not serve AI systems. And it does not scale.
What enterprises need today is not a system of record, but a system of trust: a continuously operating control plane that enforces quality, consistency, and policy across the entire data ecosystem without waiting for a human to intervene. This control plane enforces data behavior and policies across systems, ensuring trust and consistency. Unlike the data plane, it continuously orchestrates and validates data in real time.
Agentic MDM is Syncari’s answer to that requirement: a platform that continuously unifies, governs, and distributes master data in real time, enforcing policy at the point of action so that every system, every workflow, and every AI agent operates on data it can trust.
“We invented Syncari’s patented multidirectional sync engine specifically to replace point-to-point integrations with a data model-driven architecture. The goal was always to enforce provenance, policy, and consistency before data reaches any downstream system. That design decision looks very different today than it did in 2019. It looks like the foundation every AI deployment is going to need.” — Neelesh Shastry, CTO
That architectural decision, made before enterprises were asking for it, is what Syncari’s Fortune 1000 customers now depend on in production. Syncari was built for this moment.
The practical payoff of managing the control plane and data plane together in one system is speed and reliability that fragmented architectures cannot match. One Fortune 1000 customer was onboarded eight times faster than comparable deployments and reclaimed the equivalent of 100 full-time employees worth of operational capacity within their first six months. That is not a reporting improvement. It is what happens when teams stop fighting their data and start operating on it.
Our Agentic MDM platform extends this foundation to AI specifically through our Model Context Protocol, which gives autonomous agents governed access to hierarchies, entitlements, workflow permissions, and health signals. Agents operate within enterprise guardrails. Every action is observable. Every decision is reversible. Every access is logged.
That is what the control plane means in practice.
What This Demands of Data and AI Leaders
Over the next 12 to 24 months, the role of the data and AI leader will shift from steward of data to architect of autonomous control systems. That shift requires three things.
Architectural rigor. Many AI failures trace not to model limitations but to unstable data foundations that cannot support autonomous execution. Leaders must be able to distinguish scalable systems from expedient ones, and must be willing to make that call under pressure to move quickly.
Business fluency. The right architecture is not merely technically elegant. It must be economically defensible, connected to outcomes like financial integrity, regulatory compliance, operational resilience, and customer trust. Engineering decisions that cannot be articulated in those terms will not survive budget cycles.
Governance discipline. In an agentic environment, weak governance does not delay a report. It enables incorrect autonomous action. Leaders must insist on provenance, entitlement controls, version control, and auditability, especially under urgency.
The financial stakes are real and growing. Poor data quality costs organizations an average of $12.9 million annually, according to Gartner (Magic Quadrant for Data Quality Solutions, Melody Chien and Ankush Jain, July 2020). As AI adoption has accelerated, so has the exposure: Forrester’s 2024 research found that more than one-quarter of data and analytics professionals estimate their organizations lose more than $5 million annually due to poor data quality, with 7% reporting losses of $25 million or more. For enterprises operating AI agents at scale, both figures understate the risk: a single autonomous decision executed on bad data can trigger downstream errors across every connected system before any human has the opportunity to intervene.
“Every AI failure I have observed can be traced back to a data shortcut justified under urgency. Ask the governance questions at the moment of maximum momentum. Is this data truly trusted? What is the failure mode if the agent is wrong? Who is accountable for autonomous action? In distributed AI systems, instability compounds until it surfaces at scale.” — Nick Bonfiglio, CEO
The leaders who define the next decade of AI will not be those who deploy the most agents the fastest. They will be those who build the control planes those agents can be trusted to operate within.
Speed creates headlines. Trust creates durable advantages.
What We Are Building Toward
We founded Syncari in 2019 with a deliberate architectural conviction: build for the data requirements of autonomous systems before enterprises were asking for them.
That conviction led to Agentic MDM, to the patented multidirectional sync engine, and to the governed context layer that AI agents now depend on in production environments. We did not build these capabilities because the market requested them. We built them because we could see where the structural gap was going.
We believe recognition of Syncari as a Visionary in the 2026 Magic Quadrant for Master Data Management Solutions reflects what we have seen building in the field: the market is converging on the same conclusion we reached years ago.
Enterprises that succeed with AI will not do so by deploying more models on top of fragmented data. They will succeed by building a governed, real-time, continuously operating master data control plane underneath everything else.
For Syncari, the next frontier is expanding Agentic MDM beyond enterprise data teams to every system that consumes master data: AI agents, copilots, operational workflows, and real-time analytics. The governed context layer we built for MDM becomes the trust infrastructure for the entire autonomous enterprise. That is the direction we are building toward, and the pace of enterprise AI adoption tells us the window for getting this right is now.
That is what Syncari is. That is what we are continuing to build. And if your organization is already feeling the limits of what your current data infrastructure can support, we would like to show you what is possible.
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About the Authors
Nick Bonfiglio is CEO and Co-founder of Syncari. He has more than 20 years of experience building enterprise software, including nearly seven years as EVP of Product at Marketo and as CEO and Founder of Aptrinsic.
Neelesh Shastry is CTO, CISO, and Co-founder of Syncari. He leads engineering, product architecture, support, and security for Syncari’s Agentic MDM platform. His background spans enterprise data infrastructure at Marketo and Adobe, where he was named a Tech Fellow, with a focus on integration, governance, and secure systems design at global scale.
