Reconciliation Reimagined for Strategic Impact and AI-Driven Growth

Discussion with Christian Schiebl

With over 25 years of experience leading some of the most important developments in the reconciliation technology market, Christian Schiebl has built and scaled solutions across global institutions, from SmartStream to EZOPS to Gresham. As a former COO and strategic advisor, he has overseen everything from global sales and AI strategy to due diligence for private equity investments. In this article, Schiebl explores how reconciliation has evolved into a driver of efficiency, regulatory clarity, and data quality—while highlighting AI’s role, global scaling challenges, and its expanding value beyond compliance.

Turning Compliance Chores into Strategic Infrastructure

Often dismissed as a back-office task, reconciliation has struggled to be seen as a strategic asset. “Reconciliation was and still is perceived as a commodity product”, Schiebl reflected. During his leadership of SmartStream’s Corona business unit, he and his team intentionally distanced themselves from the term; instead framing their work as delivering mission-critical infrastructure that actively safeguards revenue streams, elevates trust in financial data, and informs key executive decisions.

This repositioning was more than a linguistic shift—it was a strategic reframing of reconciliation as an enterprise-wide capability. Modern reconciliation systems are no longer reactive compliance utilities. Instead, they proactively drive operational efficiency by automating manual tasks, minimizing data errors, and serving as early detection systems for risk exposures and regulatory breaches. As Schiebl emphasized, “We were talking about business-critical solutions”, underscoring the shift from back-office cost center to strategic command hub.

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Central to this evolution is the role of data integrity. As organizations are tasked with pulling data from up to eight disparate systems for a single regulatory report, the risk of inconsistency grows exponentially. Reconciliation tools that ensure accuracy, completeness, and consistency at this scale become instrumental not only for compliance but for steering strategic outcomes. “You have to clean the data before you use it”, Schiebl noted. When deployed wisely, these platforms enable real-time KPIs, dynamic dashboards, and predictive analytics—elevating reconciliation from a control mechanism to a decision-making asset.

The Challenge of Unity: Reconciliation in a Fragmented World

Scaling reconciliation platforms globally is less about transaction volume and more about navigating organizational complexity. “Most people think the problem is the number of transactions”, said Schiebl, “but that’s not true anymore”. The real hurdles are fragmented data, legacy systems, varied regulations, and inconsistent workflows.

Human factors pose significant resistance. “There are gaps in skills and you are always confronted with some resistance”, he noted. Centralizing systems often triggers fear among teams reliant on legacy platforms. “If not all the people involved support the change, it could be a problem”.

Regulatory demands vary widely. “Markets like India demand real-time capabilities”, said Schiebl, citing intraday reconciliation under Basel III and India’s real-time equity settlements. In contrast, less mature markets still rely on manual processes. “You can’t automate the whole lifecycle”, he emphasized.

Ultimately, technology alone isn’t enough. “It’s more important than the software in most cases”, Schiebl argued. Change management—securing buy-in, aligning teams, and adapting to local realities—is what determines success.

To move from regional silos to truly global operations, leaders must treat reconciliation as a strategic function, not just a back-office task. This means empowering teams with shared goals, investing in training, and fostering a culture that values agility over routine. Only then can platforms evolve in pace with the markets they serve.

Startups vs. Giants: Lessons in Building Reconciliation Solutions

Having operated in both nimble startups and complex global enterprises, Schiebl offers a grounded warning to emerging reconciliation vendors: landing a Tier 1 client is not always a win if it isn’t strategically managed. At EZOPS, a major deal with a global bank brought immediate prestige and revenue—but also exposed the company’s operational fragility. “All the development resources were suddenly working for a single client”, he recalled, revealing how a single engagement can monopolize internal bandwidth.

This hyperfocus on one demanding account derailed the company’s product roadmap, sidelined other clients, and undercut go-to-market efforts. “We failed to deliver proof of concept because we didn’t have the functionality”, Schiebl admitted. The unintended consequence? The client became both the engine and anchor of growth—pushing EISOPS off its intended course and limiting broader market execution.

The deeper insight here is that true scalability involves more than technology capacity—it hinges on resource allocation, strategic prioritization, and governance discipline. Fast-growing vendors must build internal safeguards to prevent over-customization, maintain parallel client development tracks, and protect their core product vision. A Tier 1 client can unlock scale—or become a Trojan horse that destabilizes the business. The difference lies in how prepared the organization is to absorb and manage growth.

The Real Drivers of Reconciliation Transformation

AI has become a game-changer in exception management, shifting it from a manual bottleneck to an automated, insight-driven process. “No reconciliation engine can match 100% of transactions”, Schiebl explained. With millions of daily transactions, even a 2% failure rate leaves hundreds of thousands of exceptions. “In the past, you needed a big team to investigate what’s wrong”, he recalled.

Firms like EZOPS, where Schiebl helped shape AI strategy, now use machine learning to categorize breaks, assign workflows, and resolve issues when rules allow. “We achieved that at least 50% of those people could do more valuable work”, he noted. This automation frees up resources for higher-impact functions, fundamentally changing how reconciliation teams operate.

But Schiebl is quick to note that AI isn’t a universal fix. “Banks say either it’s a match or it’s not”, he said, highlighting the friction between probabilistic AI models and regulatory expectations for deterministic results. To satisfy compliance and audit requirements, leading platforms combine AI with rules-based engines—enhancing human oversight rather than replacing it.

In his private equity advisory work, Schiebl often sees failed implementations not due to software limitations, but to rigid organizational mindsets. “Projects rarely fail because the software can’t handle the transactions”, he emphasized. “Clients try to keep everything the same and only replace the software. That’s total nonsense”, he added.

For executives, the takeaway is clear: successful reconciliation transformation depends more on people than platforms. “It’s all about people and change management”, Schiebl said. Without cultural readiness, reengineered processes, and leadership buy-in, even the most advanced tools will fall short.

The Road Ahead: From Reconciliation to Enterprise Data Management

Schiebl believes that reconciliation’s future extends far beyond its traditional remit. “It’s much more than fulfilling compliance tasks”, he said. Done well, reconciliation platforms offer business insights, prevent revenue leakage, and uncover system flaws.

Even more promising is the expansion into data quality management. As organizations grapple with ever-growing datasets, reconciliation engines are well positioned to ensure data consistency, completeness, and accuracy—making them invaluable across enterprise data management.

“The reconciliation market is growing double digits”, Schiebl noted. “But the addressable market—if you include data quality—is at least ten times bigger”. For investors, this signals strong tailwinds for platforms that can scale from finance-specific controls to enterprise-wide data governance. In short, reconciliation is no longer just a function. It’s a foundation.

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