The Risks of Building DIY AI for Accounts Receivable 

By Elaine Nowak, Global VP of Product Marketing at Sidetrade 8 May 2026

Artificial intelligence is quickly finding its way onto finance teams work agendas as it becomes the latest mandate for corporate enterprises. And these mandates are turning into execution. According to a PwC report, a third of finance executives are already using area AI in many finance areas. With the rise of easy-to-use AI tools and customizable GPT assistants, many Accounts Receivable (AR) teams are experimenting with building their own internal AI solutions to support collections, dispute management, and customer communication.

At first glance, this approach seems appealing. Generic AI tools are accessible, relatively inexpensive, and capable of generating impressive responses. Many organizations already have them in-house as part of a software package or upgrade. However, when AR teams rely on general-purpose AI to support financial workflows, they often encounter significant limitations and risks.

Understanding these risks is critical for finance leaders evaluating how AI should support their Order-to-Cash processes.

 

The Appeal of Do-It-Yourself (DIY) AI in Finance

Modern AI tools make it easy for business users to create assistants that draft emails, summarize customer communications, or answer operational questions. For AR teams, this may appear to be a fast way to improve productivity.

Common use cases include drafting collection emails, summarizing dispute conversations, categorizing deductions, suggesting follow-up actions for overdue invoices, and creating internal workflow guidance.

However, these tools are designed to be general-purpose language models. They are not built to manage the complexity, governance, or operational rigor required in financial processes. Let’s talk about some of the risks of using a DIY generic AI workflow tool.

 

1. Data Security and Confidentiality Risks

Accounts receivable teams work with highly sensitive information, including customer payment data, invoice details, and contractual terms. When finance staff input this information into generic AI tools, it may be transmitted to external systems outside the organization’s secure environment.

This creates several potential concerns:

1. Exposure of customer financial information
2. Transmission of personally identifiable information
3. Uncontrolled sharing of contract terms and payment behavior
4. Potential conflicts with data protection regulations or internal security policies

Even when AI providers offer privacy controls, many teams lack clear governance over how sensitive financial data is used in these tools.

 

2. Limited Understanding of Receivables Management

Generic AI models are trained on broad internet data rather than the specialized workflows of Order-to-Cash operations.

As a result, they lack deep understanding of areas such as:

1. Dispute lifecycle management
2. Deduction classification and validation
3. Collections prioritization and customer segmentation
4. Cash application processes
5. Credit management policies
6. AR performance metrics such as DSO or CEI

This gap may lead to operational errors.

For example, if a customer explains that they short-paid an invoice due to damaged goods, a generic AI model may suggest sending another payment reminder. In actuality, this scenario should typically trigger a dispute workflow, internal validation, and coordination with the claims team.

Without domain context, AI may produce responses that sound helpful but are operationally incorrect or inaccurate.

 

3. Lack of Workflow Integration

Accounts receivable operations rely heavily on structured workflows that interact with enterprise systems such as ERP platforms, CRM tools, and dispute management systems.

Generic AI assistants usually operate as standalone chat tools. They may suggest actions, but they do not:

1. Update invoice records
2. Log customer interactions
3. Track promise-to-pay commitments
4. Route disputes to the appropriate teams
5. Adjust collections strategies

This creates a disconnect between AI recommendations and actual operational execution for O2C operations.

 

4. Governance and Auditability Challenges

Finance functions operate under strict governance requirements. Decisions must be traceable, compliant with internal policies, and auditable. Transparency and explainability are non-negotiable when it comes to finance.

Generic AI systems present several challenges in this area:

1. Outputs may vary from one interaction to the next
2. Decision logic is often opaque
3. There may be no record of why a recommendation was made
4. Internal control policies may not be enforced

For example, if an AI assistant recommends extending payment terms or sending an aggressive collections message, there may be no structured approval process or audit trail.

This lack of governance creates risk for finance teams.

 

5. Operational Reliability and Accuracy

Generic AI models are designed for conversational tasks rather than operational execution. As a result, they may struggle with the structured, high-accuracy requirements of financial workflows or working towards an objective or outcome.

Common issues include:

1. Hallucinated invoice details or payment statuses
2. Inconsistent responses due to prompt sensitivity
3. Free-text outputs that cannot easily be integrated into financial systems
4. Difficulty handling structured financial data such as aging buckets, deduction codes, or credit thresholds

In finance operations, even small inaccuracies could lead to incorrect decisions or customer communication issues.

 

6. Long-Term Maintenance Burden

Building internal AI tools also creates an ongoing maintenance responsibility. Gartner says at least 30% of generative AI projects will be abandoned after proof of concept because of poor data quality, inadequate risk controls, escalating costs, or unclear business value.

Teams must continually manage:

1. Prompt design and optimization
2. Workflow updates
3. System integrations
4. Data governance policies
5. Regulatory changes
6. Escalating costs

Most AR teams are not structured to maintain AI systems over time. AR teams are optimized for execution, not system stewardship. Maintenance such as monitoring model bias or drift, retaining cycles, data quality governance, and feedback loop management are not necessarily skills honed by traditional finance teams. As AI models evolve and business processes change, these DIY solutions often become difficult to maintain and gradually lose reliability without more appropriately trained or hired management, or experience in building AI agents.

 

Why Domain-Specific AI Often Performs Better

Because of these limitations, many organizations are turning toward domain-specific AI platforms designed for Order-to-Cash operations.

Unlike general-purpose AI tools, these systems are built around financial workflows and enterprise integrations.

Domain-specific AI solutions typically offer:

1. Embedded knowledge of receivables processes
2. Integration with ERP and financial systems
3. Automated workflow orchestration
4. Predictive analytics based on payment behavior
5. Built-in governance and auditability

For example, rather than simply suggesting a collections action, a domain AI platform may analyze customer payment patterns, predict the likelihood of payment, prioritize accounts automatically, and trigger the appropriate follow-up actions.

 

A Hybrid Approach to AI in Finance

This does not mean that generic AI tools have no place in finance teams.

In many organizations, the most effective approach is a hybrid model:

1. Domain-specific AI platforms manage workflows, predictions, and financial decision support.
2. General AI tools support language-related tasks such as drafting emails, summarizing conversations, and assisting with documentation.

This combination allows finance teams to benefit from AI while maintaining the operational rigor required for financial processes.

 

The Strategic Takeaway

The biggest risk with DIY AI in Accounts Receivable is not simply technical. It is the risk of false confidence.

Generic AI systems may produce convincing responses that appear helpful, even when they are operationally incorrect or incomplete. In financial workflows where accuracy, compliance, and traceability are essential, this gap creates significant operational risk.

As AI adoption accelerates in finance, the key question is not whether AI should be used. It is how to apply it in a way that aligns with the complexity and governance requirements of Order-to-Cash operations.

For most organizations, that means combining the strengths of domain-specific financial AI with the flexibility of modern language models.

Platforms such as Sidetrade’s O2C Intelligence Platform combine domain-specific intelligence, governed execution, and AI-guided workflows to help finance teams act with more precision and improve cash performance with greater confidence.

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