Build vs. Buy Is the Wrong Question for Finance AI

In a lot of finance teams, nobody decided to build AI. It arrived one prompt at a time. Microsoft’s 2024 Work Trend Index found that 78% of AI users bring their own AI tools to work. In finance, that can look like a reconciliation script in one analyst’s folder and a variance-commentary prompt in another’s chat history.

Sooner or later, a controller or tax leader has to decide what happens to all of it. Do you formalize it in-house, or replace it with a platform built for finance? In Savant’s 2026 Trends Report, 76% of senior finance, tax, and accounting leaders named 2026 as the year to invest in agentic automation and generative AI, but only 6% have an advanced strategy with broad-scale implementation across their teams. The biggest barrier, cited by 37%, is the ability to govern, audit, and control AI automation workflows.

That barrier is what the build-versus-buy debate is really about. Both options have to answer the same question. What has to surround the AI, whichever model you use, before it can run a process your auditor will rely on?

Where Building It Yourself Breaks Down

Building has a real case. The people who know and run your processes for each use case write the automation, using AI models that IT has already approved, in just hours or days. The catch is that a DIY build is designed for its author. It starts to break when someone else has to run it, review it, or rely on it.

When Someone Else Runs It

If the workflow is a prompt or a Claude skill, the model interprets the instructions fresh each time. Interpretation is heavy on token usage, and AI models are non-deterministic, so the same instructions can produce a slightly different reconciliation from one close to the next. If it’s a script the model wrote, it runs consistently, but its business rules sit in code only its author can read and maintain. When that person changes roles, the thresholds and judgment calls leave with them.

When the Auditor Asks How a Number Was Produced

The evidence is a chat history or a file on someone’s desktop. Nobody approved the logic before it ran, there’s no record of which version produced last quarter’s numbers, and the data at each step wasn’t saved. None of that is audit-ready evidence.

Teams are then forced to fill the gap with a new kind of manual work where someone checks every AI output by hand. That review eats much of the time the automation was supposed to save, and it’s how promising projects stall. Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027, citing escalating costs, unclear business value, or inadequate risk controls.

Why Buying Isn’t the Clean Answer Either

Buying a purpose-built platform solves the control problem on paper. But before you sign, check what else comes with it.

Whose AI Are You Buying? 

Your company may already have standardized on Microsoft Copilot, Claude, ChatGPT, or even a private LLM, with security reviews, data agreements, and usage policies in place. A platform that requires its own model gives IT a second AI to approve and govern. When a stronger model ships, your finance team waits for the vendor to adopt it.

What Happens to the Work You Already Did? 

The scripts and prompts your team built hold real knowledge, like the tolerance on FX wires, which vendor gets special handling, and how intercompany differences are cleared. A platform that starts from its own templates asks you to set all that aside and rebuild it inside the vendor’s view of the process.

How Far Does It Reach? 

A close automation tool that stops at the close leaves tax running on spreadsheets and scripts. Sales tax filing prep, the income tax provision, and transfer pricing carry the same audit exposure as the reconciliations. Each extra point tool adds another AI and another audit trail to stitch together.

Buying answers the controls question by asking you to give up choices you would rather keep.

The Third Option: Make Your AI Audit-Ready

There’s a middle path between building and buying: audit-ready AI. Keep the AI your company already uses, whether that’s Claude, Microsoft Copilot, ChatGPT, or a private LLM, and run finance processes through Savant’s AI Control Layer, which sits around it.

The model itself stays non-deterministic, and that’s fine. It’s good at understanding what you want and proposing how to do it. With that understanding, the AI suggests the rules that will run your finance. What Savant’s control layer makes deterministic is the workflow the model helped define. An expert from your team signs off on anything new, and the control layer enforces the agreed rules and records every run.

Here’s what the control layer adds, the gap it closes in a DIY setup, and the relevant question to ask any provider.

The result keeps what’s good about building (your model and your team’s knowledge of the process), while adding what buying promises (governance and controls an auditor can test).

What It Looks Like In Practice

In Savant, the path from a conversation with your AI to a repeatable process an auditor can test has six steps.

The interview is where tribal knowledge becomes process documentation. The first time a process runs, Savvy, Savant’s AI copilot, pauses and asks for your rules, thresholds, and processes instead of assuming them. Savvy runs natively in the Savant app and plugs into Claude and ChatGPT. Nothing gets built until you certify the process documents it produces.

Take a monthly bank reconciliation for an operating account and three sub-accounts. In the interview, your process expert explains that outstanding checks carry across periods and FX-translated wires clear within the daily rate variance. Matching thresholds are $50 standard and $5 for FX, with a nightly summary at 11pm ET. The resulting workflow runs monthly, pulling data from SAP, applying the matching rules, flagging exceptions, and sending them to a named reviewer for approval.

If your team already has Python scripts built with Claude Code, they don’t go to waste. They become an input to the interview. Savvy reads them and converts them into a workflow the whole team can see, maintain, and audit.

When the auditor asks how a number was produced, the answer is the workflow and its run history, which covers the source data, every intermediate step, and the record count at each node. If they ask which version ran three years ago, every published version is archived, and each run records the version that produced it.

Questions To Ask Before You Choose

Whether you build, buy, or add a control layer, the test is the same. Before AI touches a core finance process, that process has to answer five questions:

1. Can it pass internal audit on process, evidence, and controls?
2. Can it withstand regulatory scrutiny under SOX, SEC rules, IFRS, and PCAOB standards?
3. Could it survive a liability lawsuit?
4. Can it operate across jurisdictions, including the EU AI Act, local privacy rules, and data residency requirements?
5. Can it produce the audit trail and documentation automatically, every time it runs?

Pick one recurring process your team already runs with AI, such as a bank reconciliation or a sales tax filing prep, and put it through these five questions. If the answers depend on one person’s memory or a chat history, that process isn’t ready for the close yet. The fix is a control layer around the model you already have.

Talk to Savant about putting a control layer around the AI your team already uses, so every workflow it runs is audit-ready.