Building Reliable AI for Finance and Operations: A Tested Approach

A manufacturing company recently approached us with a dilemma: their order volume was growing faster than their ability to hire and train customer service representatives, creating capacity constraints that limited business growth. They thought AI might be the answer.

But the organization's finance, operations, and compliance leaders shared a fundamental concern: could they really trust AI systems to get orders right, every time?

Each order involves multiple financial and operational validations — credit policy application, payment term verification, production capacity checks, approval workflow routing — with each decision carrying impacts on lead time and customer profitability, to say nothing of audit and compliance implications. The question wasn't whether AI could process orders faster, but whether it could make decisions that met the accuracy and auditability standards their finance leaders require.

They aren't alone in their hesitation. We've talked with dozens of finance leaders who avoid AI because they fear losing control to a 'black box'. But in the meantime, they're operating under manual processes where they have no idea what decisions are actually being made, why they're approved, or who's accountable when things go wrong.

With the right approach, we've observed that AI can actually outperform manual processes in accuracy, consistency, and transparency.

The key: a comprehensive framework for decomposing workflows and for testing, evaluating, monitoring, and iterating that guides the development and production lifecycle. A systematic framework transforms AI from an unpredictable tool into a reliable automation and decision-making system.

What Goes Wrong Without Decomposition Structured Testing

Without systematic testing frameworks, AI implementations fail in predictable ways that create financial and reputational risks, often generating more manual work than they eliminate.

Consider what happens when AI systems are deployed without proper structure: inconsistent credit policy application across similar customers, unexplainable approval decisions that can't be defended to auditors, undetectable policy violations that surface during compliance reviews, and AI decisions that require more oversight than the original manual process.

This is why most LLM experiments fail enterprise finance and operational requirements. When outputs are unpredictable, validating decisions systematically becomes impossible. Teams end up with systems they can't trust and processes they can't control.

But with systematic testing, risks are mitigated at every step.

Step 0: Decompose Complex Processes into Testable Workflows

Before writing a single prompt, map your business process as a series of discrete, testable workflows. This decomposition transforms an unwieldy "order processing system" into manageable components that can be independently validated and monitored.

For our manufacturing company's order processing, we identified these atomic workflows:

Order Intake & Classification

• Parse incoming emails and attachments
• Extract structured order data
• Lookup and validate requested products against item master
• Classify order type and priority
• Route to appropriate workflow

Customer Validation

• Verify customer identity and status
• Check credit limits and payment history
• Apply customer-specific business rules
• Flag exceptions for review

Inventory & Production Planning

• Check current inventory levels
• Calculate production capacity requirements
• Determine feasible delivery dates
• Generate production scheduling requests

Financial Compliance

• Apply pricing rules and discounts
• Calculate taxes and fees
• Verify payment terms compliance
• Generate approval requirements

Each workflow becomes an independent agent with clear inputs, outputs, and success criteria. This matters because:

1. Testability: You can validate each component independently before integrating them
2. Durability: When one agent encounters an error, the entire process doesn't fail - it pauses, logs the issue, and can resume
3. Auditability: Every handoff between agents creates a checkpoint for compliance and review
4. Flexibility: You can mix AI agents with deterministic code and human review steps exactly where needed

Some workflows benefit from multi-turn, tool-using agents that can explore and reason through complex scenarios. Others work better as deterministic functions that make one or several decisions based on codified business rules. The decomposition lets you choose the right approach for each component.

Between these agents, standard engineering practices apply: a durable execution framework provides reliability and auditability, while business rules and policy engines enable the application of SME-managed decision logic with least-privilege security. Your AI agents become well-behaved citizens in a larger system architecture.

Step 1: Structured Outputs Enable Systematic Validation

Once you've decomposed your process, each agent needs structured outputs that enable systematic validation. Free-form text responses might seem more flexible, but they're impossible to test reliably.

Take our order intake agent. Without structured outputs, it generates conversational responses that vary unpredictably. With structured outputs, every response follows a schema:

{
  "order_type": "STANDARD" | "RUSH" | "CUSTOM",
  "customer_id": "string",
  "line_items": [{
    "sku": "string",
    "quantity": "number",
    "requested_delivery": "date"
  }],
  "confidence_scores": {
    "customer_match": 0.95,
    "sku_identification": 0.88,
    "overall": 0.85
  },
  "exceptions": ["delivery_date_unclear"]
}

This structure enables automated validation of every decision component. You can verify that SKUs exist in your catalog, quantities meet minimum order requirements, and delivery dates fall within feasible windows. When confidence scores drop below thresholds, the system automatically routes to human review.

The structure also enables systematic testing. You can run thousands of historical orders through the agent and validate that outputs match expected results. This forms the foundation of your evaluation framework.

Step 2: Pre-Production Evaluation Through Historical Benchmarking

Here's where most AI projects go wrong: they test on a handful of examples and declare victory. Real reliability comes from systematic evaluation against your actual business history.

Building Your Evaluation Dataset

Your organization already has the perfect evaluation dataset: historical transactions with known correct outcomes. For our manufacturing company, this meant:

1. Gathering diverse examples: 1,000 historical order emails spanning all customer types, order complexities, and edge cases
2. Capturing ground truth: The actual orders that resulted from these emails, as processed by experienced staff
3. Encoding business logic: Payment terms applied, credit decisions made, delivery dates confirmed
4. Including edge cases: Rush orders, custom configurations, credit holds, inventory constraints

This historical data becomes your evaluation benchmark. You're not testing whether the AI can process orders in theory - you're testing whether it processes them the way your best people do.

The Evaluation Framework

Modern evaluation frameworks let you test agents systematically:

def evaluate_order_agent(agent, test_cases):
    results = []
    for case in test_cases:
        # Run agent on historical input
        agent_output = agent.process(case.input_email)
 
        # Compare to ground truth
        accuracy = compare_outputs(
            agent_output,
            case.expected_output,
            tolerance_rules
        )
 
        # Track specific failure modes
        if accuracy < 1.0:
            failure_analysis = identify_failure_pattern(
                agent_output,
                case.expected_output
            )
            results.append(failure_analysis)
 
    return aggregate_metrics(results)

The key insight: these evaluations become your quality gates. No agent moves to production until it achieves 95%+ accuracy on historical cases. When it fails, you know exactly which scenarios need attention.

Evaluation as Gateway to Continuous Improvement

Here's what finance leaders love about this approach: your evaluation framework becomes the foundation for continuous improvement. As you collect more production data, you expand your evaluation set. As you encounter new edge cases, you add them to the benchmark.

This evaluation data also enables future model fine-tuning. Once you have thousands of examples of "input → correct output" pairs, you can train models specifically on your business logic. The evaluation framework you build today becomes the training data for tomorrow's more capable agents.

Step 3: Prompt Engineering Through Systematic Iteration

With evaluation in place, prompt engineering becomes a data-driven discipline rather than guesswork. You iterate on prompts based on specific failure patterns identified in your evaluations.

Consider how our credit validation agent evolved:

Initial Prompt (72% accuracy):

Check if the customer has sufficient credit for this order.
Consider their credit limit and current balance.

After Analyzing Failures (85% accuracy):

Validate customer credit availability following these rules:
1. Calculate available credit: credit_limit - current_balance - pending_orders
2. Compare to order total INCLUDING tax and shipping
3. For orders exceeding 80% of available credit, flag for review
4. For new customers (<6 months), apply 50% credit utilization cap

Final Version (96% accuracy):

Execute credit validation with these specific policies:

CALCULATION:
- Available = credit_limit - current_balance - sum(pending_orders) - sum(uncleared_payments)
- Required = order_subtotal * 1.0875 (tax) + shipping_estimate
- Utilization = (current_balance + required) / credit_limit

DECISION RULES:
- APPROVE if utilization < 0.80 AND customer_age > 180_days
- APPROVE if utilization < 0.50 AND customer_age <= 180_days
- REFER if utilization between 0.80-1.00
- DECLINE if utilization > 1.00
- ALWAYS REFER if customer has any notes in credit_watch_list

OUTPUT: {"decision": "APPROVE|REFER|DECLINE", "utilization": float, "available": float}

Each iteration addressed specific failure patterns discovered through evaluation. The prompts became more precise, encoding actual business logic rather than vague instructions.

Production Safeguards: Deterministic Rules + Audit Superiority

Production deployment requires two things: unbreachable policy controls and audit trails that exceed what any manual process could provide.

Deterministic Logic Enforcement

AI makes recommendations; deterministic rules enforce policies. This separation ensures that no matter what the AI suggests, critical business rules remain inviolate:

def enforce_credit_policy(ai_decision, customer, order):
    # AI made its recommendation, now apply hard rules
 
    # Absolute credit limit enforcement
    if order.total > customer.credit_limit:
        return Decision(
            action="DECLINE",
            reason="Exceeds absolute credit limit",
            override_ai=True
        )
 
    # Regulatory compliance
    if customer.jurisdiction in SANCTIONED_COUNTRIES:
        return Decision(
            action="HOLD_FOR_COMPLIANCE",
            reason="Requires OFAC review",
            override_ai=True
        )
 
    # Business rule enforcement
    if customer.days_past_due > 90:
        return Decision(
            action="REQUIRE_PREPAYMENT",
            reason="Account past due",
            override_ai=True
        )
 
    # If no rules violated, use AI recommendation
    return ai_decision

Or better yet, build and edit them visually, empowering SMEs to control the rules that the agent must check against:

CoPlane Visual Rules Editor

CoPlane Decision Table Editor

This layered approach gives finance leaders confidence: AI handles the routine decisions efficiently, while deterministic rules provide guardrails that prevent policy violations.

Audit Trails That Exceed Manual Processes

Every agent execution generates comprehensive audit logs that document every step of the flow, when it happened, which rules were applied, what prompts were used, what actions were taken, and the reasoning or codified business logic behind them all.

When auditors ask "Why was this order approved?" you have immediate, comprehensive answers. Compare this to manual processes where decisions exist only in emails, chat messages, and human memory.

Continuous Optimization Through Production Learning

Production systems generate rich data streams that enable continuous improvement. Unlike static manual processes, AI systems get better over time.

Performance Monitoring

Track key metrics that matter to finance and operations:

• Decision Accuracy: Compare AI decisions to human expert reviews on a sample basis
• Cycle Time: Measure end-to-end processing time by workflow and identify bottlenecks
• Intervention Rate: Track how often humans need to override or correct AI decisions
• Policy Compliance: Verify that all decisions align with current business rules

Pattern Recognition and Rule Evolution

Production data reveals patterns that inform business rule evolution:

-- Identify common characteristics of orders requiring intervention
SELECT
    intervention_reason,
    COUNT(*) as frequency,
    AVG(order_value) as avg_value,
    ARRAY_AGG(DISTINCT customer_industry) as industries
FROM order_interventions
WHERE date >= CURRENT_DATE - 30
GROUP BY intervention_reason
ORDER BY frequency DESC

These insights drive systematic improvements. When you discover that orders from aerospace customers always require special export documentation, you encode that as a deterministic rule. When certain SKU combinations frequently trigger inventory conflicts, you add pre-flight checks.

The Compound Effect

Each improvement compounds. Better prompts reduce error rates. New rules catch edge cases earlier. Expanded evaluations ensure quality remains high as the system evolves.

The Strategic Choice: Building Confidence Through Engineering

The path to trusted AI in finance and operations runs through systematic engineering practices, not magic. When leaders see structured outputs, comprehensive evaluations, deterministic safeguards, and superior audit trails, their skepticism transforms into competitive advantage.

Organizations that embrace this approach gain more than efficiency. They gain visibility into their operations that manual processes never provided. They gain consistency that human-only workflows can't match. They gain the ability to evolve and improve systematically rather than hoping their best people don't leave.

The manufacturing company that started with skepticism? They're now exploring AI applications across procurement, inventory management, and financial planning. The foundation of trust built through systematic testing opened doors they hadn't imagined.

For finance and operations leaders evaluating AI, the question isn't whether to adopt these technologies. The question is whether you'll build them with the engineering rigor that enterprise processes demand. With the right framework, AI becomes not just trustworthy, but superior to the manual processes it replaces.

CoPlane provides enterprises the platform and hands-on guidance they need to confidently operationalize AI. Want to learn more? Reach out to founders@coplane.com.