Sustaining Enterprise Value by Using Evidence Calculators

An AI Integration POV by Dr. Dodi Mossafer, DBA • MSF • MBA • MHA

Creating value is hard; keeping it is harder. Sustaining value requires evidence, governance, and signal loops that tie strategy, performance, and AI outcomes into measurable systems. This POV introduces two integrated engines; the Value Calculator for quantifying efficiency and savings, and the Applied AI Evidence Lab for logging, validating, and sustaining realized impact over time.

Read the Framework Explore the Calculators Sustainment Metrics

Summary

Sustained enterprise value is an operating outcome, not a one-time milestone. It is maintained through disciplined evidence, repeatable decision cadence, and quantifiable proof of value. This POV introduces two applied systems: the Value Calculator, which measures realized gains net of effort and cost, and the Applied AI Evidence Lab, which verifies model performance, drift, and governance quality. Together, they close the loop between financial impact, AI enablement, and enterprise execution, ensuring value is not just created, but continually evidenced, governed, and scaled.

1) The Framework

Value Governance

A benefits register owned by Finance and refreshed every cycle.
A single source of truth through integrated enterprise systems with controlled lineage.
Quarterly gates for stopping, continuing, or scaling projects tied directly to outcomes.

Capability and Ownership

Role-based skills with cross-training and succession planning for critical workflows.
Run-state playbooks and governance forums designed for decisions, not updates.
Embedded artificial intelligence and automation with defined human checkpoints.

Lifecycle Management

Scope control with quarterly plans for addressing technical debt.
Monitoring of model risk and performance drift with remediation triggers.
Retirement or replacement pathways supported by evidence packs.

2) Working Principles

Evidence over intent: every value claim reconciles to financial postings and operational performance indicators.
Few forums with real decisions: cadence and discipline sustain value more than one-time heroics.
Ownership of the run state: no value persists without accountable role-based ownership.
Design for audit: lineage and assumptions are fully documented and retraceable.

3) Use Cases

Finance and Capital Management

Projects that sustain value through disciplined financial governance.

Deploy a monthly benefits attestation with counterfactual analysis for every major initiative.
Run an integrated review across operating expenditures, capital expenditures, and workforce allocations.
Refresh return on investment assessments for capital projects and exit underperforming tracks.

Operations and Supply Chain

Projects that sustain throughput, inventory accuracy, and service reliability.

Establish backlog and lead-time variability signal loops for proactive issue detection.
Implement master data service-level agreements for bill of materials, routings, and pricing.
Deploy automation monitoring to ensure uptime, with exception handling owned by accountable leaders.

Artificial Intelligence in Daily Workflows

Projects that embed artificial intelligence into decision-making with accountability.

Track adoption by role and measure benefits per transaction or time saved.
Deploy drift alerts for models with retraining windows and formal approval processes.
Enforce bias, privacy, and data lineage guardrails through governance boards.

4) Sustainment Metrics

Lead Indicators

Decision-making speed and latency by forum and tier.
Adoption in daily workflows tracked by role and process.
Data quality scoring and automation uptime percentages.
Model performance drift alerts closed within service-level targets.

Lag Indicators

Changes in gross margin and controllable operating expenditure.
Working capital performance including receivables, payables, and inventory turns versus plan.
Service levels, reliability scores, and customer outcome measures.
Audit exceptions and financial restatement trends.

5) Value Evidence Lab (Interactive)

Use these micro-tools to capture counterfactual evidence, define signal loops, and log decisions. Exports create auditable artifacts that sustain value over time.

1) Counterfactual Benefits Calculator

Initiative / Use Case

Owner

Period

Ledger Ref

Baseline (counterfactual)

Observed (actual)

Unit

Confidence (%)

Computed: —

Initiative	Owner	Period	Baseline	Observed	Delta	Δ%	Unit	Confidence	Ledger Ref	Timestamp

2) Signal Loop Tracker

Metric

Target

Threshold

Source System

Escalation Path

Refresh Cadence

Owner

Metric	Target	Threshold	Source	Owner	Cadence	Escalation	Timestamp

3) Decision & Ownership Log

Forum

Decision

Scope/Subject

Next Review

Forum	Decision	Scope/Subject	Next Review	Timestamp

Summary & Closing

Use the Benefits Calculator to quantify counterfactual value and tie claims to the ledger. Capture your Signal Loops with explicit owners, refresh cadences, and escalation paths so exceptions are actionable, not anecdotal. Record Start / Stop / Scale decisions with next review dates to make the operating rhythm durable. Export CSVs monthly, attach them to your evidence pack, and reconcile the totals to Finance. This is how value is sustained, not just announced.

6) Applied AI Evidence Lab

Capture net value from AI (after inference cost), log run telemetry, monitor drift, and document decisions to enable AI benefits beyond pilots.

A) AI Benefits Calculator (Net)

Use Case

Owner

Volume / month

Baseline minutes / task

With AI minutes / task

Loaded rate (USD/hr)

Avg tokens in

Avg tokens out

Cost /1k tokens (USD)

Confidence (%)

Computed (monthly): —

Use Case	Owner	Volume	Baseline mins	AI mins	Labor $ Saved	Model $ Cost	Net $ Benefit	Confidence	Timestamp

B) Model Run Telemetry

Model

Version / Hash

Prompt Template ID

Latency (ms)

Tokens In

Tokens Out

$/1k tokens

Human Approval

Quality Score (1–5)

Data Source / Lineage

Model	Version	Prompt ID	Latency	Tok In	Tok Out	Cost (USD)	HIL	Quality	Lineage	Timestamp

C) Drift & Risk Register

Monitored Metric

Baseline

Current

Alert Threshold

Owner

Retrain Due Date

Notes / Remediation

Metric	Baseline	Current	Δ	Alert?	Owner	Retrain Due	Notes	Timestamp

D) Prompt & Policy Exceptions

Prompt ID

Exception Type

Remediation

Owner

Prompt ID	Type	Remediation	Owner	Timestamp

E) Model Card (Printable)

Model

Intended Use

Owner

Risk Class

Data Sources / Lineage

Controls

Summary & Closing

Use the Net Benefits calculator to show real value after token costs. Log telemetry with latency, tokens, cost, quality, and lineage so each run is auditable. For drift and risk, set an alert threshold, assign an owner, and give every entry a retrain due date so remediation does not drift. Export each table as CSV and attach to your monthly evidence pack; reconcile totals with Finance. This is how applied AI value moves from pilot headlines to sustained operating outcomes.

7) Operating Rhythm and Signal Loops

Cadence

Weekly operational forums: issues assigned to owners with deadlines.
Monthly finance forums: variance, benefits, and cash cycle reviews.
Quarterly strategy reviews: stop, continue, or scale initiatives with evidence packs.

Signal Mechanics

Lead and lag dashboards sourced directly from enterprise systems and artificial intelligence logs.
Counterfactual analysis preserved for every major value claim.
Ownership logs that capture who decided, what changed, and the impact observed.

8) Common Failure Modes

Governance without decisions: many meetings but no accountable outcomes.
Shadow systems: spreadsheets and logic outside the ledger break lineage and trust.
Feature creep: expansion of scope displaces efforts to fix defects and burn down technical debt.
No succession planning: value delivery depends on individuals, not institutionalized roles.

9) Practical Artifacts

A benefits register template linking owner, driver, evidence, and confidence score.
Forum charters defining decision rights and service-level agreements.
Data lineage and reconciliation maps showing movement from source to ledger to report.
A model lifecycle checklist covering risk, drift monitoring, retraining, and retirement.
Run-state playbooks by process with responsibilities, accountabilities, and checkpoints.

10) About the Author

Dr. Dodi Mossafer is a corporate strategy and transformation advisor. His experience spans financial governance, artificial intelligence operating models, and enterprise value sustainment across multiple industries. His academic work includes decision sciences, finance digitalization, and artificial intelligence readiness.

11) Use and Citation

Cite as: “Dr. Dodi Mossafer, DBA — Sustaining Enterprise Value: Integrating the Value Calculator and Applied AI Evidence Lab (Advisory Point of View), 2025.” Independent perspective; designed for academic, consulting, and enterprise reference with attribution.

The accompanying Value Calculator and Applied AI Evidence Lab are practical tools released as a framework to help organizations measure, validate, and sustain impact across digital and AI-enabled transformations.