As organizations increasingly invest in artificial intelligence (AI) to enhance productivity and decision-making amid global volatility, many large enterprises find that the technology’s impact at scale has been less significant than anticipated. Werner van Rossum, a leader in finance and analytics transformation, emphasizes that the main issue lies not with the algorithms themselves, but with the structural challenges inherent in large organizations.
“There’s a widespread assumption that AI will fix decision-making,” van Rossum states. “In reality, it often exposes problems that were already there.” His insights stem from extensive experience leading enterprise-wide transformations in capital-intensive organizations, where AI initiatives were implemented alongside critical changes to data architecture and governance.
Van Rossum recently completed a significant deployment aimed at unifying corporate performance metrics and analytics, describing the program as an extensive rethinking of how information and decision-making interconnect within large organizations. Despite the promise of AI, his experience reveals that pilot projects frequently succeed in isolated environments yet falter when scaled across entire enterprises.
“AI pilots usually work,” he explains. “They’re controlled, localized, and designed around a narrow use case. The moment you try to scale them across an enterprise, all the underlying inconsistencies surface.” These inconsistencies, he notes, are rarely technical; rather, they are structural. In many cases, performance indicators are defined differently across business units, leading to fragmented data architectures and inconsistent governance models that complicate timely decision-making.
Van Rossum points out that when AI is layered on a fractured data environment, it does not resolve existing issues but accelerates them. “You end up with faster answers to questions leaders don’t fully trust,” he says. “When trust is missing, decisions slow down instead of speeding up.” This dynamic underscores why numerous AI initiatives remain confined to experimentation rather than achieving widespread adoption.
He argues that harmonized data is essential for any automation that executives feel confident relying on. “Harmonized data isn’t a technical nice-to-have,” van Rossum asserts. “It’s a prerequisite.” Organizations struggling to scale AI often share traits such as fragmented data systems and varying definitions of performance metrics. Without a coherent semantic layer and clearly defined ownership of core metrics, AI outputs frequently remain advisory, leading executives to return to manual analysis when faced with significant decisions.
While technology teams typically spearhead AI initiatives, van Rossum believes the finance department plays a crucial role in determining whether the technology can scale responsibly. “Finance shouldn’t be trying to become an AI lab,” he advises. “Its real contribution is creating clarity, consistency, and trust in the data that decisions are built on.”
In the transformations he has led, the focus on simplifying performance measures proved more impactful than introducing new tools. By reducing bespoke reporting and aligning analytics with decision forums, organizations found that decision-making improved significantly. “What surprised many people,” he recalls, “was that reducing complexity actually improved decision-making. Once we stopped trying to explain everything, discussions became more focused, and decisions moved faster.”
The broader lesson from his experience is that the success of AI hinges more on organizational design than on technological capabilities. “Technology can accelerate insight,” he concludes. “But it can’t compensate for unclear decision rights, fragmented governance, or inconsistent definitions.”
As investment in AI continues to surge, the gap between expectation and reality may widen for organizations that overlook foundational principles like data harmonization and governance. Conversely, organizations that prioritize these strategic capabilities are likely to be better positioned for effective automation.
“AI will absolutely reshape finance,” van Rossum states. “But not because the models get smarter. It will matter because organizations finally design their data and analytics foundations to work at enterprise scale.” In a rapidly shifting landscape of capital allocation and market conditions, organizations unable to trust their data may find themselves hindered in making decisive actions when it matters the most. For leaders grappling with slow decision-making processes, the key takeaway is clear: before inquiring about AI’s capabilities, they must first assess whether their organizations are ready to trust the answers it provides.
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