Start with where the workflow lives
The first question is whether the finance variance notes workflow lives mostly inside Microsoft 365 or crosses several systems. Microsoft documentation on Microsoft 365 Copilot privacy and data controls explains how Copilot uses Microsoft 365 permissions and organizational data boundaries. That can be enough when the user needs help summarizing, drafting, searching, and preparing work inside the tenant.
The RSM middle-market AI survey shows middle-market leaders moving quickly on AI, but workflow fit still matters. If the workflow requires CRM, PSA, ERP, ticketing, finance, approval queues, or analytics beyond Microsoft 365, a custom workflow may be more appropriate.
Use the AI use-case scoring model to compare value, data access, system fit, risk, adoption effort, and measurement clarity.
Use Copilot for individual productivity and approved tenant context
The OECD report on AI adoption by small and medium-sized enterprises is a useful reminder that access to AI tools is not the same as business adoption. Copilot can improve individual preparation when the source content is already governed in Microsoft 365 and the user reviews the output.
A custom workflow is justified when the process needs durable queues, source-specific rules, review states, exception handling, or reporting. The NIST AI Risk Management Framework gives the operating pattern: map the context, measure risk, define controls, and keep accountability visible.
Finance should compare both paths with an AI ROI model that avoids fake savings. Copilot value may appear as better preparation; custom workflow value should appear in throughput, fewer handoff misses, cleaner review, or measurable cycle-time improvement.
Choose the path that can survive production
The Deloitte State of AI report reinforces that AI value comes from process change. For the finance variance notes workflow, production requires an owner, approved sources, review rules, exception handling, training, logs, and weekly value checks.
The Gartner agentic AI project forecast is relevant because agentic and custom AI projects can fail when cost, value, data quality, and controls are unclear. Do not build custom software because the demo is impressive. Build it when Copilot cannot own the workflow boundary.
The next step is the AI pilot versus production workflow guide to choose between Copilot adoption, lightweight automation, or a governed custom workflow.
