GenAI is no longer a future disruptor—it’s already embedded in the enterprise finance conversation. From automating P2P (Procure to Pay) processes to accelerating R2R (Record to Report) cycles, GenAI is reshaping how finance functions extract value from data. Its ability to generate, classify, and contextualize information is pushing the boundaries of what finance teams can achieve without increasing headcount.
But while the technology has evolved rapidly, finance organizations haven’t kept pace. The real constraint isn’t GenAI’s capability—it’s the operating environment it’s entering. Most finance systems were built for deterministic logic, not contextual reasoning. Legacy architecture, siloed workflows, and governance gaps are preventing GenAI from delivering on its promise at scale.
This blog examines how GenAI is used across finance, why adoption is stalling, and what leaders must do to operationalize it successfully. Tax use cases, while not the central focus, and serve as a recurring example of GenAI’s promise and the pitfalls when infrastructure lags behind.
GenAI Works, but Finance Isn’t Designed to Absorb It
Traditional automation in finance—RPA bots, scripting tools, and basic ML—focused on structured, rules-based tasks. These solutions were effective for tasks like bank reconciliations or journal entry validation. But they lacked adaptability.
GenAI introduces something different: contextual intelligence. It can interpret unstructured inputs, generate first-draft reports, and suggest treatments based on prior patterns. In real terms:
- In P2P, it can extract, classify, and tag vendor invoices.
- In R2R, it can generate variance narratives or prepare reconciliation summaries.
- In tax, it can flag treatment inconsistencies or summarize compliance notes.
And yet, most of these wins remain locked in pilots as shown in Exhibit 1 because the systems surrounding finance workflows cannot yet absorb GenAI’s outputs.
| GenAI Use Case | Works in Pilots | Fails to Scale Because… |
| P2P Invoice Classification |  |
ERP lacks workflow triggers and integration hooks |
| R2R Report Narratives |  |
Outputs aren’t explainable/auditable in current tools |
| Tax Classification |  |
No version control, audit trail, or exception logic |
The Bottleneck Isn’t GenAI—It’s System Readiness
According to a report, 70% of CEOs expect GenAI to significantly reshape value creation over the next three years. But implementation lags behind—because most finance functions weren’t built to integrate with adaptive, generative systems.
Take the case of a Fortune 500 finance team that piloted GenAI for indirect tax classification across multiple jurisdictions. The model delivered high accuracy in tagging transactions. But:
- There was no ERP integration to ingest or act on the results
- There were no governance policies in place to validate GenAI’s decisions
- Ownership was split between tax and IT, creating accountability gaps
As a result, despite technical success, the pilot stalled for over a year. Manual interventions continued, and the company saw no measurable return. Exhibit 2 showcase what’s stopping Gen AI to showcase its full performance.
Even With the Right Systems, Limitations Persist
Now imagine the above pilot had overcome its ERP integration and governance hurdles. Would GenAI have been seamless?
Unlikely.
GenAI still struggles with:
- Regional tax rules that require contextual judgment
- Inconsistent document formats across vendors or jurisdictions
- Auditor demands for explainability that go beyond model outputs
These aren’t technical faults—they’re operational realities. And without guardrails, human validation, and process alignment, even the most accurate models will stall at scale.
GenAI is not a replacement for finance professionals—it’s an accelerator. But only if finance systems, people, and controls are designed to scale it.
What Progressive Finance Teams Are Doing Differently
Forward-thinking finance leaders are getting ahead of the bottlenecks. Instead of launching enterprise-wide deployments, they’re orchestrating structured adoption strategies:
- Starting small: Narrow use cases such as tax memo drafting, indirect tax tagging, or month-end summaries
- Embedding review layers: Human-in-the-loop frameworks to verify outputs
- Investing in enablement: Training finance professionals in prompt design and AI output interpretation
- Aligning governance early: Establishing audit trails, model thresholds, and exception protocols
These steps aren’t just theory—they form a phased approach many progressive F&A teams are already putting into motion. From early experimentation to scalable transformation, the journey is structured, deliberate, and measurable.
See Exhibit 3 for a breakdown of how finance leaders are approaching GenAI adoption—one phase at a time.
- Memo drafts, report summaries
- Quick wins, low risk
- Governance + prompt protocols
- Controlled rollout, high traceability
- End-to-end integration
- Operational efficiency, measurable ROI
Strategic Outlook: The Model’s Ready. Are You?
GenAI isn’t waiting. It’s already at work in adjacent functions—contract summarization, spend analytics, and operational risk scoring. But finance is different. Precision, traceability, and compliance are non-negotiable.
The difference between success and stalling isn’t the model—it’s the ecosystem.
- Can your ERP ingest GenAI outputs?
- Can your governance framework explain them?
- Can your teams challenge or validate them?
That same tax classification pilot proved GenAI can deliver. But it also exposed why even the best models fail without systems built to support them.
Conclusion: Scale Requires Systemic Change—Not Just Smarter Tools
This isn’t about who tested GenAI first. It’s about who is ready to operationalize it—with structure, speed, and accountability.
GenAI doesn’t just demand adoption. It demands orchestration.
Is your finance function structurally equipped to turn GenAI’s potential into real, scalable outcomes or are system realities still holding it back? Let’s discuss and see how our finance and accounting services provider can add value.
The post GenAI in Enterprise Finance: Why Legacy Systems Are the Real Roadblock appeared first on Cogneesol Blog.