Context
- Finance is shifting from retrospective reporting to real-time performance orchestration across product, operations, and go-to-market.
- AI creates value when benefits are measured and posted to the financial system, not just claimed in slideware.
- This case shows how Stratenity wires finance for AI: unit economics, benefits registers, governed data, and decision telemetry.
Challenge
- Soft Benefits: Value narratives don’t reconcile to the GL; ROI remains unproven.
- Fragmented Data: Revenue, cost, and operational signals live in silos; reconciliation is manual.
- Static Cycles: Monthly/quarterly cadences miss intra-period variance and opportunity.
- Opaque Unit Economics: Cost-to-serve and AI inference costs are unclear at product/customer/channel levels.
- Controls at the End: Model risk, policy, and access controls are applied late, slowing recognition.
Stratenity Approach — Finance as a Performance System
- Benefits Register to Books: Define value hypotheses; automate evidence capture; post realized benefits to GL/cost centers.
- Unit Economics Everywhere: Attribute training/inference/storage costs to products, customers, and channels; expose cost-to-serve.
- Signal-to-Decision Loop: Connect operational telemetry (pricing, conversion, churn, reliability) to financial outcomes.
- Data Products for Finance: Governed revenue, cost, and driver datasets with lineage, SLAs, and access policies.
- FP&A Copilots & Scenarios: In-flow planning, driver-based models, and scenario libraries with audit trails.
- Governance & Evidence Cadence: Model cards, approval logs, and quarterly evidence reviews across the portfolio.
Execution Journey
- Baseline & Design (Weeks 1–6): Map finance data landscape, benefits processes, and unit economics gaps; define service catalog and controls.
- Foundational Services (Weeks 6–12): Stand up benefits register service, finance data products (rev/cost drivers), lineage, and policy engine.
- Operationalization (Months 3–9): Instrument 3–5 value streams (e.g., pricing, collections, support deflection, supply); automate posting to GL.
- Institutionalization (Months 9–12): Introduce economics dashboards, scenario copilot, evidence cadence, and outcome-linked funding.
Stakeholder Insights (Interviews + Stratenity Case Study Insight)
| Role | Biggest Challenge | Frustration w/ Current State | If AI Could Solve One Thing… | Stratenity Case Study Insight |
|---|---|---|---|---|
| CFO | Proving ROI | Benefits don’t hit the books | Automated, auditable postings | Benefits register reconciled to GL and cost centers |
| Controller | Close accuracy & speed | Manual reconciliations | Traceable source→post | Lineage + policy engine for postings |
| Head of FP&A | Driver visibility | Static spreadsheets | Real-time driver models | Scenario libraries + in-flow planning copilot |
| RevOps Lead | Pipeline→Revenue fidelity | Attribution ambiguity | Clean conversion signals | Revenue data products with SLAs |
| Product Finance | Cost-to-serve clarity | Shared costs opaque | Unit economics by SKU | Training/inference/storage attributed by product |
| Procurement | Vendor economics | Contract sprawl | Total cost visibility | Economics dashboards for vendors & usage |
| Collections | DSO reduction | Fragmented AR signals | Risk-based workflows | AR data product + copilot outreach |
| Operations Leader | Throughput vs cost | Lagging metrics | In-period variance response | Signal-to-decision loops linked to P&L |
| Board/Audit Chair | Assurance | Opaque AI value | Evidence trail | Model cards + immutable logs + benefit evidence |
| Stratenity (Insight) | From claims to cash | Slideware value | GL-linked telemetry | Finance as performance OS with unit economics by default |
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Impact (Projected 2026+)
- Evidence-Backed ROI: 90–100% of benefits posted with audit trail; value moves from claims to cash.
- Unit Economics Transparency: Product/customer/channel profitability visible; smarter scaling and pricing.
- Faster Close, Better Forecast: Automated reconciliations and real-time drivers improve accuracy and speed.
- Working Capital Uplift: DSO reduction via AR copilots and risk scoring; inventory turns guided by signals.
Stratenity Insight — Vision of the Future
- Finance operates as a real-time performance system with evidence wired to the GL.
- Every initiative has value hypotheses, telemetry, and posting rules before funds are approved.
- Leaders steer with unit economics and scenario copilots, not static plans.
Stratenity POV: Finance leads AI value realization when data, governance, and economics are engineered into daily decisions.
Impact on the Consulting Industry
- Finance Platforms, Not Decks: Deliver benefits registers, economics dashboards, and governed datasets that clients run.
- Outcome-Linked Fees: Tie commercials to posted benefits, forecast accuracy, and DSO/GM improvements.
- Reusable Kits: Scenario libraries, allocation models, and policy packs published on Stratenity.
Engagement Projects (Recommended)
- Finance Performance Scan (6 weeks): Baseline benefits processes, data products, close/forecast cadence, and economics gaps.
- Benefits Register Service: Value hypotheses → evidence capture → automated posting with lineage, approvals, and audit logs.
- Unit Economics & Cost-to-Serve: Attribute AI costs (train/infer/storage) to SKUs/customers/channels; visibility for pricing and scaling.
- FP&A Copilot & Scenario Library: Driver-based planning, variance analysis, and what-if simulations in the flow of work.
- Revenue & AR Modernization: Clean pipeline→revenue signals, collections copilot, AR risk scoring, and DSO playbooks.
- Evidence & Governance: Model cards, policy engine, quarterly evidence reviews, and outcome-linked funding.
Solo Consultants vs Consulting Firms
- Solo Consultants: Deploy the benefits register and one unit-economics pack for a product line; prove reconciliation to GL.
- Boutique Firms: Package finance data products + FP&A copilot across business units; standardize posting rules and dashboards.
- Large Firms: Operate multi-tenant finance platforms with governance, vendor economics, and portfolio-level evidence cadences.
Appendix A — Full Interview Responses (Finance & Performance)
| Role | Q1: Biggest Challenge | Q2: Where Projects Derail | Q3: Current Practice | Q4: Tools / What's Missing | Q5: Success Metrics | Q6: Frustrations w/ Consulting | Q7: If AI Could Solve One Thing | Q8: Openness to AI | Q9: What Builds Trust | Q10: Stratenity Case Study Insight — Future Finance |
|---|---|---|---|---|---|---|---|---|---|---|
| CFO | ROI proof | Benefits not posted | Quarterly reviews | Benefits register | GM, ROI | Soft claims | Auto posting | High | Audit trail | Evidence-to-books |
| Controller | Close speed | Manual recs | Email tickets | Lineage engine | Close time | Ad-hoc data | Traceability | Selective | Provenance | Source→post clarity |
| Head of FP&A | Drivers | Static models | Spreadsheets | Scenario libs | Forecast error | Slow cycles | Real-time signals | High | Backtests | Copilot in flow |
| RevOps | Attribution | Pipeline noise | Manual merges | Revenue product | Win rate | Tool sprawl | Clean signals | High | SLA data | Lead→cash fidelity |
| Product Finance | Cost-to-serve | Shared cost haze | Allocations | Usage metering | SKU GM | Opaque LLM cost | Attribution | Very high | Telemetry | Train/infer by SKU |
| Procurement | Vendor ROI | Contract drift | Rate cards | Economics view | Savings, value | Renewal rush | True TCO | High | Benchmarks | Usage-linked terms |
| Collections | DSO | Low prioritization | Bulk outreach | Risk scoring | DSO, recovery | Blunt playbooks | Next-best action | High | Outcome logs | Copilot sequencing |
| Operations | Variance response | Lags | Weekly packs | Signal loop | Throughput | Detached finance | In-period fixes | High | Shared KPIs | Ops→P&L links |
| Board/Audit | Assurance | Opaque models | Policy docs | Model cards | Controls pass% | Late reviews | Explainability | Moderate | Logs | Governance by design |
| Stratenity (Insight) | From promises to postings | Slideware | Ad-hoc | Shared services | Posted value | Fragmentation | Platform effect | — | Transparency | Finance as performance OS with unit economics |
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Join Our Interviews — Shape Finance & Performance
Stratenity is interviewing finance and operating leaders to refine AI-enabled finance & performance patterns that move value from claims to cash.
- Who we’re speaking with: CFOs, Controllers, FP&A, RevOps, Product Finance, Procurement, Collections, Operations, Board/Audit.
- Why participate: Influence reference models, benchmark economics, and shape reusable services.
- What you gain: Early access to insights and optional feature in our case library.
- Commitment: 25–30 minutes on benefits posting, unit economics, and governance evidence.
- Confidentiality: Anonymized by default; named features by explicit approval only.
By contributing, you help establish finance as the operating system for AI value — with evidence posted, economics visible, and performance improved.