Build, buy or use what's in your ERP: finance AI

When it comes up

When a finance team wants an AI capability (close, AP, forecasting, reporting) and the options are a specialist vendor, the AI features of your ERP or productivity suite, or something built on a model platform by IT.

Your options

Use the AI already in your ERP / suite

Fits when: The use case is standard (coding, matching, drafting) and your core platforms already offer it under existing contracts and controls.

Watch out: Features may lag specialists; pricing often moves to add-on seats or credits; you depend on the vendor's roadmap.

Buy a specialist finance AI vendor

Fits when: The process is complex and specialist tools are proven in it (AP, reconciliations, cash forecasting), and you need results fast.

Watch out: Integration cost, data leaving your platforms, vendor viability, lock-in, and overlap with features your ERP will ship next year.

Build on a model platform

Fits when: The use case is differentiating or unusual, you have data and engineering capacity, and you need control over data, models and logs.

Watch out: Run and maintenance costs, talent dependency, model change management and owning the controls end to end.

How to decide

Is it differentiating? Standard finance processes rarely justify building.
What do you already own? Check the AI features in your ERP and productivity licences before buying anything new.
Compare total cost over 3 years in the ROI builder: one-off, licences or tokens, people, integration and exit.
Test controls and evidence: can the option produce the audit trail your auditor needs?
Check the data path: where does finance data go, and under which contract terms?
Decide per use case, and revisit at each renewal: today's build may be tomorrow's commodity feature.

Trade-offs

Suite AI is cheapest to adopt but least differentiated; specialists deliver faster in complex processes but add integration and vendor risk; building gives control and fit at the cost of run effort, talent and owning every control.

Real examples

JPMorgan Chase built its own generative AI assistant, LLM Suite, and was rolling it out to 140,000 employees in 2024; President Daniel Pinto estimated the value of the bank's AI use cases at close to $2 billion, much of it related to fraud prevention. Source: Banking Dive — JPMorgan Chase rolls out LLM Suite generative AI tool to 140,000 employees (Sep 12, 2024) ↗
Microsoft launched Copilot for Finance in public preview on February 29, 2024, offering reconciliation, collections and variance-analysis scenarios inside Excel, Outlook and Teams, connected to systems such as Dynamics 365 and SAP: the 'use what's in your suite' option. Source: Microsoft — Introducing Microsoft Copilot for Finance (Feb 29, 2024) ↗

What to check first

Which AI features your ERP, close and productivity licences already include, and at what extra price.
Whether the use case is standard or genuinely differentiating for your company.
Three-year total cost of each option, including run costs and exit.
Whether each option can produce the audit trail and logs your auditor will require.

Learn and use

Sources

Last reviewed 2026-10-01.