In many organizations, success is still measured by the wrong signals. A sudden drop in the past - due accounts receivable might light up dashboards and earn applause, but often, the underlying problem hasn’t been fixed - it’s just been disguised. In one case, a regional finance center celebrated what appeared to be a 90% improvement, only to discover that customer prepayments had been reclassified as accounts receivable. 

The cash hadn’t moved. The risk hadn’t changed. But decisions were already being made based on a false win.

This is not an isolated issue. It’s a systemic flaw in how Shared Services Centers (SSCs) have been designed and measured. Dashboards show clean data. SLAs are met on paper. Yet the day - to - day friction, manual rework, and customer frustration persist under the surface.

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The Problem with Surface - Level Success

Many organizations still celebrate performance based on misleading metrics. A cleaner dashboard or a dramatic improvement in a metric often obscures reality. As in the earlier example of a finance center that reclassified customer prepayments into AR, the appearance of success can be dangerously misleading. Risk remained unchanged, cash flow didn’t improve, yet strategic decisions were made under the illusion of progress. This false confidence perpetuates a cycle where symptoms are masked, but root causes go unresolved.

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Why the Old Shared Services Model Is Failing

SSCs were built for centralization, standardization, and cost control. They succeeded in driving efficiency through scale, but they weren’t designed for agility, insight, or customer - centricity. These gaps are now critical.

At Unilever’s finance hub in India, 65% of disputes still needed manual intervention, and 28% of supplier payments were delayed - not because of system limits, but due to slow, opaque exception handling. At Procter & Gamble, more than two - thirds of collectors’ time was spent chasing the same payments, often redundantly. Sony had 15% of invoices in dispute, with average resolution times of twelve days. These are not minor inefficiencies - they are symptoms of a model optimized for throughput, not insight or value.

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What AI in O2C Actually Means

Artificial Intelligence in finance isn’t about hype or tools - it’s about solving real operational problems at scale. In Order - to - Cash (O2C), machine learning models are being used to forecast payment behavior, identify cash flow risks, and anticipate disputes. These models don’t just analyze large datasets; they uncover subtle patterns and anomalies that traditional approaches miss. Deep learning, particularly through neural networks, extends this capability into more complex data environments, handling unstructured inputs such as remittance narratives, handwritten notes, or free - text dispute justifications.

Natural Language Processing (NLP) has become indispensable in customer - facing workflows. It enables systems to read and interpret customer emails, classify the content of service requests, and even transcribe and analyze voice calls. Coca - Cola HBC, for example, implemented conversational AI to accelerate response times. These assistants not only answered inquiries faster, but allowed service representatives to focus on complex issues requiring judgment and context.

Meanwhile, computer vision is finding practical use in extracting data from PDFs, scanned remittances, and supporting documents. While less prevalent in finance than in retail or logistics, its value lies in eliminating repetitive data entry and improving accuracy in document handling.

Alongside these core techniques, companies are also applying reinforcement learning and expert systems. These methods are not necessarily more advanced, but they are more targeted. Reinforcement learning models are useful in environments where outcomes evolve based on behavior - collections timing being a strong example. Expert systems and knowledge graphs, while rule - based, still provide meaningful automation where policies and compliance constraints dominate decision - making.

Regardless of the technique, explainability and governance remain non - negotiable. If your model declines a credit extension or flags a customer as high - risk, the logic must be transparent and auditable. Compliance with frameworks like the EU AI Act is already essential. Without proper oversight, even technically accurate models risk being sidelined.

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When AI Is Deployed with Purpose, the Impact Is Transformational

Coca - Cola HBC’s use of GPT - powered assistants led to a 68% reduction in response times, but the true benefit was strategic. The freed - up capacity allowed service teams to move beyond ticket resolution and contribute to customer retention initiatives.

In a predictive collections use case, a multinational using AI models identified high - risk invoices more than two weeks earlier than traditional systems could. This accelerated insight enabled them to reduce their DSO by nine days and improve their annual cash flow by €24 million.

Hershey’s transformation of dispute resolution is another benchmark. With 70% of disputes now automatically classified by AI, volume has declined by 20% and average resolution time has dropped from twelve to four days. This isn’t just about speed - it’s about removing the root causes of customer dissatisfaction before they escalate.

Veolia moved away from periodic, static credit scoring and implemented real - time dynamic credit profiles. The result: an 18% reduction in bad debt. This shift enabled risk decisions to reflect actual behavior, not legacy data.

At XPO Logistics, AI handles 80% of outbound collections communication. But this automation didn’t replace staff - it enabled them to focus on complex, strategic accounts. Their DSO improved by ten days without adding headcount.

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How the Right Infrastructure Drives Sustainable Results

Siemens used Celonis to conduct detailed process mining across its invoicing and finance operations. Within six months, automation increased by 24%, invoice processing became 40% faster, and more than ten million manual steps were eliminated.

At Adidas, Freeda AI was deployed to power internal and external self - service portals. The outcome was measurable: 60% of queries were resolved without human involvement, and customer satisfaction rose by 15 points. Importantly, the success wasn’t due to the AI model alone - it was the integration with clean content, enterprise - grade search, and simple UI that made adoption smooth.

Honeywell’s partnership with Emagia illustrates what’s possible at full scale. By embedding AI across the O2C chain - credit, collections, disputes, and service - the organization unlocked $120 million in working capital, reduced DSO by 14 days, auto - routed 80% of disputes, and improved collection effectiveness by 35%. This wasn’t a tech pilot. It was a structured transformation with defined business outcomes.

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What Leaders Need to Do Now

To fully realize AI’s value, leadership needs to go beyond sponsoring tools. They must rethink the architecture of their operations. That starts with rejecting the notion that adding AI to existing processes is enough. Redesigning for digital - first means reimagining workflows that are built for decision support, not just transaction processing.

Executives also need to build functional fluency in AI. This doesn’t mean learning how to code. It means understanding enough to challenge roadmaps, align investments, and ensure that AI serves a business purpose.

Governance can’t be postponed. Ethical deployment, explainability, and auditability must be built into design. It’s not only about compliance - it’s about trust.

And instead of waiting for a full transformation, leaders should start with clear, high - impact areas. Disputes, collections, and credit risk have immediate value potential and relatively mature AI use cases.

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From Friction to Flow: A Disciplined Path Forward

The most effective AI transformations begin by identifying where the process is stuck. That means mapping not just process diagrams, but shadow work: manual reconciliations, email chains, and the “human glue” that keeps operations moving.

Once mapped, automation should focus on high - volume, repeatable tasks. From there, machine learning and process intelligence tools can layer in prediction and insight. Success in one area becomes the proof point for scale.

Avoiding innovation theater means making hard choices. Kill what doesn’t work. Double down on what does. And move fast enough to build momentum.

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Final Thoughts

AI in Order - to - Cash is no longer a future consideration - it is a present necessity. The organizations pulling ahead are not the ones experimenting with isolated tools. They are the ones embedding intelligence into the core of their processes, structuring around outcomes, and building systems that learn and improve over time.

The challenge is no longer technological. It is organizational. Legacy processes, unclear ownership, and siloed accountability are what hold companies back - not a lack of models or compute power.

What’s needed now is leadership willing to do the harder work: redesigning workflows, aligning teams around real - time data, and enforcing governance that scales. Done well, AI won’t just make Shared Services more efficient. It will make them strategically essential.

The transition from transaction factory to intelligence hub is underway. The pace of change is accelerating. And the cost of delay is rising.

AI is already reshaping the way finance functions operate.The only question left is: are you driving the change - or reacting to it?