Model ML now supports a finance workflow built around GPT 5.6 Sol. The focus is not just generating answers. It carries finance work from early research and analysis through to output formats finance teams actually use, including editable and traceable PowerPoint decks and Excel workbooks.
What changed
The workflow is designed end to end for finance tasks. Instead of stopping at a draft or a summary, it produces deliverables that can be edited and audited, with traceability built into the generated PowerPoint and Excel outputs.
Why this matters for finance operations
For business teams, the practical value is tighter handoff between analysis and reporting. Editable decks and workbooks reduce the gap between what analysts discover and what finance leaders need to review and update. Traceability also supports internal review workflows where teams want to understand how outputs were produced.
What to do next
- Start with a single recurring finance deliverable where teams currently copy from notes into PowerPoint and Excel, then test the Model ML workflow for research to deck and workbook generation.
- Define what traceability means for your process, such as how reviewers will check generated outputs before they are finalised.
- Measure time saved from draft production to editable deliverable readiness, and track rework rate after human review.
If your biggest bottleneck is turning analysis into editable reporting, test this workflow on one template first, then scale only after your review and traceability checks pass.