Finance teams are under growing pressure to adopt AI, yet many are moving forward without addressing the condition of the data beneath it. In this webinar, From COA Choas to AI Confidence: A Finance Data Playbook, panelists Trenton Hubley, Partner at Citrin Cooperman, Brian Kobleur, VP of Ecosystems at Vena Solutions, and Jason Chen, Solutions Consultant at Vena Solutions, made a clear point: AI does not correct weak finance data structures. It exposes them, accelerates them, and in many cases, amplifies their impact.
As Trenton explained, “AI is an output multiplier. It’s a multiplier that doesn’t care about what it’s multiplying.” That distinction framed the entire discussion. The opportunity is real, but so is the risk when foundational data issues remain unresolved.
Why AI Readiness Starts with Finance Data
Many organizations are experimenting with AI tools, but readiness is often misunderstood. It is not defined by access to advanced technology. It is defined by whether finance data can be trusted, reconciled, and consistently interpreted across the business.
The panel described a familiar reality. Over time, chart of accounts structures expands, naming conventions drift, and mappings become inconsistent across entities. These changes are rarely intentional. They reflect growth, acquisitions, and evolving leadership priorities. However, when AI is applied to this environment, those inconsistencies are not corrected. They are learned and repeated.
As Trenton noted, “AI won’t fix your foundational data set. It just learns it as the standard pattern and then it reproduces it at scale automatically.”
This creates a new category of risk. Outputs may appear polished and complete yet lack consistency beneath the surface. In finance, that gap is not acceptable. Numbers must withstand audit scrutiny, executive review, and external reporting requirements.
The Compounding Effect of Weak Foundations
A central theme of the discussion was how small data issues expand once AI is introduced. A minor inconsistency in classification does not remain isolated. It flows into forecasts, reporting, and downstream analysis.
The panel outlined three distinct effects:
- AI does not identify or correct inconsistent structures
- Automation spreads inaccuracies across multiple outputs
- Errors compound as they move through forecasts and reporting cycles
Unlike manual processes, where review checkpoints often catch issues early, AI operates continuously. It does not pause to question inconsistencies. It executes based on the patterns it has learned.
This shift fundamentally changes the control environment. As Trenton emphasized, “There’s really no close enough in our world.”
Moving from Insight to Action Requires Structure
Another key takeaway focused on where finance processes tend to break down. The issue is rarely the insight itself. It is the gap between insight and execution.
AI can generate analysis quickly, but without structured workflows, governance, and data integrity, that analysis cannot be trusted or operationalized. The panel described this as a systems challenge rather than a tooling challenge.
Finance operates in a deterministic environment. The same inputs must produce the same outputs every time. Variability, ambiguity, or unexplained differences introduce risk that extends beyond reporting into decision-making and compliance.
To address this, organizations must ensure that AI operates within a governed system, not across disconnected spreadsheets or fragmented data sources.
The Role of a Governed Financial System
The discussion highlighted a practical framework for enabling AI in finance:
- A clean and rationalized chart of accounts
- Standardized dimensions and consistent mappings
- Documented and auditable data flows
- Defined ownership and governance across the data model
When these elements are in place, AI becomes materially more effective. Outputs are consistent. Scenarios can be repeated and validated. Recommendations can be acted upon with confidence.
Without this structure, AI remains limited to surface-level productivity gains and introduces additional reconciliation challenges.
A Practical Path Forward
To help organizations assess their position, the panel introduced an AI readiness scorecard. This framework evaluates key areas such as data structure, governance, ecosystem integration, and value realization.
The intent is not to assign a pass or fail outcome. It is to provide clarity on where to focus. A lower score highlights where foundational work is required before scaling AI initiatives.
This structured approach allows finance leaders to prioritize efforts, align stakeholders, and build a roadmap that supports both innovation and control.
From Foundation to Confidence
The session concluded with a consistent message. AI adoption in finance is no longer optional. However, the sequence of actions matters.
Organizations that invest in their data foundation first are seeing stronger outcomes. The same tools produce different results when applied to governed, consistent data environments. Teams spend less time reconciling outputs and more time acting on insights.
Those that move too quickly without addressing underlying data issues often face the opposite outcome. Faster outputs, but lower confidence.
For finance leaders, the objective is not speed alone. It is reliable speed, supported by structure.
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About Our Sponsors
Citrin Cooperman is one of the nation’s largest professional services firms, the 19th largest CPA/accounting firms in the U.S. and ranked the 28th largest technology consulting firm in the U.S. by Accounting Today. Citrin Cooperman Digital Services include Microsoft, NetSuite, VENA, Salesforce, Cybersecurity and more. Citrin Cooperman is headquartered in NYC with offices nationally. Learn more at www.CitrinCooperman.com.
Vena is the only agentic AI-powered FP&A platform purpose-built to harness the full power of the Microsoft ecosystem, including Microsoft 365, Microsoft Fabric and Microsoft Azure – for finance and IT teams everywhere. Thousands of leading enterprises rely on Vena to connect planning, analytics and execution. Learn more at venasolutions.com.
