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Chris Sperandio

Co-founder & CEO

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June 19, 2025

The Finance Department's AI Blind Spot: Accuracy and Compliance Realities

The finance department's assumptions about AI accuracy and compliance are understandable — but increasingly outdated. Here's what innovative teams are discovering.

Chris Sperandio

Co-founder & CEO

Many enterprise finance leaders have reached a curious consensus: AI isn't ready for mission-critical operations.

In our workshops with finance teams, controller’s groups, and back office transformation professionals (contact us if you’d like to host one for your team!), we hear a common refrain.

  • “Won’t an LLM give you different answers to the same prompt if you ask it twice?”
  • “Don’t LLMs just make answers up when they don’t know?”
  • “How can we trust something that we can’t audit?”

In many circles, the prevailing wisdom suggests patience — wait for the technology to mature before trusting it with anything important.

This consensus would be more compelling if it weren't contradicted by results elsewhere in the enterprise. Engineering teams have increased productivity by 30% with AI coding assistants that handle complex development tasks. Customer service operations are achieving 80% resolution rates with AI agents that understand context, navigate the ecosystem of available tools and data, and escalate appropriately. Marketing teams are using AI to optimize campaigns in real-time, achieving unprecedented personalization at scale.

Finance, meanwhile, continues tolerating manual processes that would be unthinkable elsewhere in the organization — routine procedures that require manually merging data from three different systems to upload it into a fourth, error-prone data entry that creates expensive downstream reconciliation work, and performance metrics that measure efficiency in navigating broken systems rather than business outcomes.

There’s an irony here: the function most obsessed with accuracy and control is avoiding technology that can deliver both at lower costs.

The disconnect isn't actually about technological maturity. It’s an information asymmetry problem. The assumptions shaping finance industry thinking are based on outdated understanding of how enterprise AI actually operates. But the current reality is fundamentally different. The capabilities are here; knowledge of them and how best to harness them isn’t evenly distributed yet.

Misconception #1: AI Responses Are Too Unpredictable for Financial Processes

The finance industry's AI skepticism stems from reasonable extrapolation: if AI systems can generate different responses to similar questions, they can't be trusted with financial data that demands absolute consistency.

This assumption makes sense when your primary AI reference point is consumer applications designed for creativity and helpfulness. The industry logic flows naturally: systems optimized for interesting conversations can't handle the rigid requirements of financial operations. Better to stick with manual processes that, while inefficient, at least provide predictable outcomes.

The Reality: Enterprise AI Is Engineered for Consistency, Not Creativity

Meanwhile, their peers at forward-looking enterprises are quietly building competitive advantages through enterprise AI systems that operate nothing like ChatGPT.

The fundamental difference: enterprise-grade AI systems combine proven approaches that eliminate the inaccuracy and unpredictability that finance leaders fear. Let’s consider three complementary approaches that work together to deliver reliable results:

Structured outputs

Structured outputs force AI into predefined data structures with required fields, data types, and validation rules. Instead of generating free-form responses that could say anything, enterprise systems force AI into predefined data structures with required fields, data types, and validation rules.

For example, when processing an invoice, you can specify that the LLM returns exactly:

  • Vendor name (validated against master data)
  • Invoice number (checked for duplicates)
  • Amount (verified against purchase order tolerance)
  • Date (formatted consistently)
  • Line items (mapped to correct GL codes)
  • Policy exceptions (categorized using standard classifications)

No creative interpretation. No unexpected formats.

Effective prompt engineering

Strategic prompt engineering provides systematic instructions with clear business context, specific examples of correct edge case handling, and explicit constraints on acceptable responses. These aren't casual conversation prompts—they're business logic specifications written and refined by subject matter experts who understand actual operational requirements.

The process also involves continuous testing and iteration. Teams start by asking the AI system itself to suggest effective prompts based on the specific situation, required outcomes, and example cases—a technique called meta prompting. These initial prompts are then tested against historical data, refined based on results, and improved over time as edge cases are discovered.

Deterministic rules

Deterministic rules create absolute boundaries that cannot be overridden regardless of AI recommendations, such as:

  • Payment amounts above $50,000 require CFO approval — no exceptions.
  • GL codes must match the approved chart of accounts — no creative coding.
  • Vendor payments cannot exceed purchase order totals by more than 5% — no unauthorized overages.
  • Month-end accruals must follow established methodology — no AI interpretation of accounting standards.

AI operates within these constraints by handling pattern recognition, data extraction, and workflow optimization while deterministic rules enforce business requirements. The AI might recommend routing an invoice to a specific approver based on transaction patterns, but the approval matrix determines who can actually authorize payment.

The combination delivers consistent, reliable behavior that follows business rules more precisely than manual processes, with built-in validation that prevents entire categories of errors that plague current workflows.

Misconception #2: AI Creates Compliance and Audit Challenges

Audit trails and regulatory compliance are non-negotiable in finance operations. The concern about AI creating "black box" decisions reflects legitimate worry about explaining automated processes to auditors and regulators. If AI systems can't provide clear documentation of how decisions were made, they create compliance risks rather than solving operational problems.

But, this concern is rooted in a common misconception: AI decisions are mysterious and unexplainable. But for enterprise AI systems, that’s simply not true.

The Reality: Enterprise AI Systems Generate Superior Audit Trails

Systems intentionally developed for enterprise use cases actually create more comprehensive audit trails than manual processes — documenting every step in ways that exceed traditional audit capabilities.

Enterprise-grade AI systems automatically log 100% of their inputs, outputs, decisions, and actions — not the 5-20% sampling typical of traditional process audits.

Every transaction generates detailed logs capturing:

  • Exact input data received with timestamps
  • Business rules applied and their outcomes
  • System interactions and data retrieved
  • Decision logic and supporting evidence
  • Validation checks performed and results
  • Final outputs with confidence scores

Consider expense report processing as an example. Traditional audits might examine 200 reports from 10,000 submitted, hoping this sample identifies issues. AI systems document every decision for every report — policy checks, approval routing, GL coding, exception handling — creating complete audit trails that external auditors can examine in detail.

The compliance advantage extends beyond documentation. Real-time monitoring capabilities mean potential issues are flagged immediately rather than discovered months later during periodic audits. AI systems can enforce policies more consistently than human review, reducing compliance risk while improving audit defense capabilities.

Misconception #3: AI Implementation Is Too Risky

The implementation assumption draws from painful industry experience with enterprise software deployments: ERP projects that consumed years, required massive process overhauls, and created operational chaos during cutover.

But AI implementations are fundamentally different — making the inherent risk significantly lower.

The Reality: AI Implementation Reduces Risk Through Validation

AI initiatives are already delivering value for organizations faster than traditional enterprise software deployments because they follow fundamentally different approaches.

Successful AI adoption starts with augmenting existing processes and systems in a targeted manner, not with onerous migrations or complete process overhauls.

Instead of replacing existing processes, AI systems initially run alongside and within current workflows. Finance and accounting processes already have checks and controls in place (by nature), so teams can test and compare AI outputs to manual results for thousands of transactions.

The implementation approach builds confidence systematically: read-only analysis on historical data with zero operational impact, parallel processing with comprehensive human validation and comparison, gradual automation with systematic exception monitoring, full deployment with continuous improvement and instant rollback capabilities.

The result? Better implementation processes and operating models that accelerate AI adoption and drive exponential value over time.

Why Industry Assumptions Persist — And Why They're Becoming Expensive

Industry assumptions about AI aren't uninformed — they represent reasonable extrapolation from limited information and painful experience with previous technology cycles.

Finance leaders hear "AI" and reasonably think of consumer applications. They hear "automation" and understandably recall brittle RPA implementations. They hear "transformation" and justifiably remember traumatic ERP deployments.

But while finance functions debate readiness using yesterday's frameworks, competitors are building operational advantages.

AI systems improve through use via reinforcement learning. They develop understanding of specific business contexts, learn policy exception patterns, refine decision-making based on feedback loops. Systems processing 10,000 invoices monthly for two years possess fundamentally different capabilities than newly deployed implementations.

Data network effects create additional competitive moats. Organizations leveraging enterprise data locked in existing systems have rich training datasets that enable AI accuracy from initial deployment. Over time, these systems evolve to handle edge cases that weren't explicitly programmed, learning to pursue defined objectives through pattern matching against policies and historical precedents.

In other words, hesitation based on outdated assumptions doesn’t just delay benefits, but compounds competitive disadvantage.

Moving Beyond Industry Misconceptions

Enterprise AI for finance represents current reality, not future possibility. Organizations moving beyond industry assumptions to operational evidence are discovering measurable advantages in accuracy, efficiency, and compliance capabilities.

The most successful implementations target processes offering clear value with minimal risk: high-volume, routine transactions where consistency matters more than creativity; well-defined business rules that can be systematically applied; comprehensive historical data for validation and benchmarking; clear success metrics for measurement and improvement.

Proven examples include invoice data extraction and validation, expense report policy checking and routing, cash application matching and exception handling, vendor master data maintenance and compliance monitoring.

The technical approaches exist. Implementation strategies are validated. Leading organizations are building sustainable advantages through superior accuracy, faster processing, and enhanced compliance while industry consensus remains anchored to assumptions rather than evidence.


CoPlane is building the AI platform for finance and accounting teams to automate their critical procedures. Want to learn more? Drop us a line.