Shared finance AI foundation
Shared identity, retrieval, logging and evaluations for one approved vendor-hosted product.
Evidence: Architecture/work evidence, no calibrated reuse percentage
Shared work
- Shared access controls, document search, and testing tools can reduce repeated setup across finance projects.
- Each project still needs its own data, finance rules, and tests. The shared tools also need maintenance as systems and models change.
- Count shared setup and maintenance once across the projects that use them.
No direct operating workflow is replaced by this project. A delivery-effort reuse estimate would need a separate baseline.
Citations
How Amazon Finance streamlines regulatory inquiries by using generative AI on AWS
First-party finance deployment architecture · 2026-05-12
Amazon Finance's 2026 account shows teams building document retrieval, access controls, conversation history and monitoring for regulatory inquiries. It illustrates foundation work and team upkeep; it gives no setup-hours estimate or reuse saving.
Evidence period: 2026 architecture/deployment account; implementation and measurement periods unreported.
Use here: No directly measured foundation labor or reuse percentage.
Primary source ↗Enterprise-grade controls for AI apps and agents built with Azure AI Foundry and Copilot Studio
Product/security capability announcement · 2025-05-19
Microsoft's May 2025 controls announcement describes shared identity, security and evaluation capabilities for enterprise agents. It supports the foundation's task list; it provides no implementation-hour or percentage-saving result.
Evidence period: 2025 product capability announcement; no deployment or labor experiment.
Use here: None.
Primary source ↗Estimation method
Conditional planning scenarios, not measured population bounds or confidence intervals.
The same work is completed manually using existing systems and approved inputs. Sources and calculation models are usable. These are workflow reference cases, not company types.
- The workflow is in steady-state use and passes checks for equivalent output quality.
- Volume, coverage and output remain unchanged. Saved capacity is not assumed to reduce staffing.
- Human review, corrections, exceptions and existing approvals are included.
- Baseline task shares and transferred task-saving rates are analyst assumptions, not measured industry allocations.
Savings include conventional extraction, matching, retrieval and integration delivered with each scoped project. The separate marginal contribution of GenAI is not established.
Separate costs: Implementation and training, Separate project/platform upkeep, Upstream remediation outside the project, Additional output and newly worthwhile work.
Already-automated work, low adoption, difficult exceptions or unreliable output can produce smaller savings, zero or additional work. The positive lower endpoint is not a guaranteed minimum.
Convert to monthly hours: Monthly operating hours saved = baseline monthly workflow hours × saving percentage. Net capacity released = operating hours saved − annual project upkeep / 12 − allocated shared upkeep / 12. Shared upkeep is allocated once across the portfolio.
What would improve the estimate
- A delivery-effort reuse estimate would require logs for common setup versus application-specific implementation across multiple comparable deployments; it is a separate metric from operating workflow savings.
Reviewed 2026-10-25 · Reference model data · Research notes