As AI use spreads across corporate functions, finance has been more deliberate than most in its adoption. That caution is a feature, not a flaw, since there are no small mistakes in finance. Financial outputs flow into audited results, requiring a level of precision, traceability, and data security that early, general-use AI tools could not guarantee.
That landscape is now changing with the emergence of what many call “vibe coding”—or more precisely, AI-driven custom application development. AI models have become more capable, the platforms for building with them no longer require an engineering team, and finance teams have learned from experience which problems are better solved with AI rather than conventional software or ERP systems. Forecasting, anomaly detection, and document review are among the tasks AI handles best.
These developments have created a credible path for finance teams to build targeted custom applications for analysis, matching, anomaly detection, and other specific tasks without waiting for long development queues. (See Exhibit 1.)
For CFOs, the opportunity is not to let finance teams build whatever they want with AI. It is to identify the narrow workflows where custom applications can reduce manual effort, improve control, and produce more reliable insight than spreadsheet-heavy processes allow.
How Coding Agents Create Value in Finance
The change matters because it makes desktop-level automation more accessible. Finance teams can now build targeted solutions faster, including FP&A forecasting tools that surface localized insights and controllership applications that flag anomalies across full transaction populations. (See Exhibit 2.) Analysts still need to define the logic, constraints, and desired outputs. But the AI agent can write the code, test it, refine it, embed AI insights, and help connect it to relevant data sources.
Higher reliability does not come from the model, but from where the work runs. Reliability comes from moving calculations into rule-based code, such as Python or SQL, that runs in controlled environments and provides the same predictable answers. Agent-driven coding makes that path far easier for finance teams that do not have deep software engineering skills in-house.
AI-built custom applications do not replace traditional finance systems and databases. Core platforms for accounting, planning, and reporting remain essential. Systems of record, such as enterprise resource planning (ERP), and systems of execution, including enterprise performance management (EPM) systems, consolidation tools, and upstream procure-to-pay and order-to-cash processes, remain critical to operations. What AI-coded applications provide is an augmentation layer. They do not replace the system of record; instead, they sit on top of it and make the data and insights more accessible, usable, and easier to interpret.
AI-coded applications provide an augmentation layer. They sit on top of the system of record and make the data and insights more accessible, usable, and easier to interpret.
Understanding where that boundary falls is important. Processes that generate reported financials should remain in core systems. For instance, enterprise-level planning and forecasting should stay within EPM platforms, and proven workflow tools such as BlackLine for reconciliations should not be replaced. By contrast, processes centered on interpretation, input shaping, localized insight generation, or anomaly detection are often better candidates for augmentation. This layered architecture, where AI tools augment but do not replace core systems, creates a clear governance boundary. Core financial platforms handle reporting and compliance. AI-coded applications handle speed and insight. Applied well, this model lets finance teams move faster on analysis and exception handling without putting compliance or auditability at risk.
To be sure, this accessibility introduces a new risk: AI sprawl—the uncontrolled spread of custom AI tools across a finance department that can create complexity and management challenges. AI-coded tool sprawl can be as damaging as Excel sprawl, the quiet chaos of a thousand spreadsheets nobody owns. A CFO who allows the team to build freely without a governance framework risks trading “shadow Excel” for "shadow code"—undocumented scripts and applications that exist outside official systems and lack proper oversight. Shadow code is far harder to see until something breaks. To scale AI safely, finance leaders must set clear guardrails around version control, data access, and auditability.
The solution is not to slow adoption but to enforce control in the runtime where apps built by coding agents operate. These apps work best when employees use them to analyze, explore, or automate their own work, not when they touch financial systems, ERPs, or customer-facing flows. Those domains belong to purpose-built enterprise and orchestration agents, which are strictly designed and continuously monitored.
Within safe boundaries, leading organizations embed governance rules directly into the system, run execution in isolated environments, and maintain active oversight. They also keep watching after the app ships, with a record of what happened and checking that behavior remains correct. Governance is built into how these apps operate, not added afterward.
Consider intercompany accounting, where even teams with established processes for eliminating transactions between related entities still spend hours each close updating entries that offset across those entities, and matching by amount and trading partner. A coding agent can turn those activities into a repeatable script that applies matching logic and surfaces an exception list ranked by materiality, so analysts focus on resolving breaks rather than finding them.
With AI agent-driven development, an analyst can build an AI tool that pulls transaction data from multiple ledgers, matches intercompany payables against receivables, and flags discrepancies in minutes instead of hours. This tool does not replace the reconciliation and booking in the core general ledger but uses agentic capabilities to accelerate exception handling.
Today, coding agents such as Claude Code and OpenAI Codex can write and execute code, work with structured data, and build small applications from natural language instructions. Innovation in this space is quickly expanding the breadth of capabilities that agent-driven coding platforms provide. This allows both finance analysts and IT departments to accelerate development cycles.
Recognizing Limits
The pace of innovation, however, does not eliminate the conditions that determine whether these tools create value. Sophisticated output does not fix flawed input and actually poses a risk because the polish makes bad data more convincing. AI-coded tools are no exception. Coding agents also do not replace judgment. CFOs still need employees who can interpret results, apply policy, and make decisions in ambiguous situations.
Sophisticated output does not fix flawed input and actually poses a risk because the polish makes bad data more convincing.
Coding agents also do not reduce governance risk on their own. Without proper controls, the spread of desk-level applications can introduce new risks related to access, version control, and auditability. These governance questions are not secondary concerns, but the requirements for moving from isolated experiments to enterprise-wide deployment.
Toward Orchestration
The next phase of value creation lies in moving from point solutions to end-to-end process orchestration, alongside a more deliberate rationalization of how and where agents are used. (See Exhibit 2.) Many finance workflows follow predictable sequences but remain labor-intensive due to handoffs, approvals, and exception handling.
While orchestration can streamline these workflows, scaling it requires clear decisions about when to rely on agentic capabilities versus traditional automation means. High-volume data transformations and reconciliations remain far more efficient in traditional pipelines such as SQL (Structured Query Language) and ETL (Extract, Transform, Load), with agents applied selectively for exception handling, reasoning, and coordination. The result is a hybrid model that balances automation, cost efficiency, and control.
An orchestration layer can automate multistep processes across systems. In cash application, for example, an agent can match remittance advices against open receivables, route exceptions to the appropriate analyst with supporting detail attached, and track resolution through close. AI agent-led coding is well suited to building the individual components of the workflow. Orchestration connects them into a cohesive, automated process. Orchestration can also “call up” and leverage the custom-coded tools developed in the prior use case.
A new generation of agentic frameworks is beginning to move beyond single-task automation toward more autonomous, outcome-oriented operations. Rather than executing predefined scripts, these agents can navigate applications, coordinate across systems, and adapt dynamically as conditions change, linking multiple steps into end-to-end workflows.
Over time, this enables loosely coupled networks of agents that work together toward defined objectives, such as completing a close process or resolving exceptions, with workers setting goals and guardrails rather than directing each step. Although still in its early stages, this shift nonetheless signals a move to more autonomous operations where agents pursue clear goals with greater independence.
What Finance Leaders Should Do Next
The opportunity is real, but value depends on disciplined use. Finance teams should start by building their analysts’ capabilities so they can frame the desktop-level problems best suited for AI-driven custom applications. Collaboration with IT, security, and risk functions should begin early, not after tool use has spread. Without visibility from these groups, finance teams risk building tools that create compliance gaps or security vulnerabilities. Leaders should also choose initial use cases carefully, favoring solutions where risk is manageable and the boundary between augmentation and system-of-record responsibilities is clear.
AI coding agents give finance teams a credible way to build targeted applications faster than before. Done right, AI-driven development gives finance teams a faster path to the applications they have always needed and rarely had the resources to build. The leaders who empower their teams with these capabilities will achieve better productivity and faster insights, while maintaining financial integrity.
