Sustaining Enterprise Value with AI — POV by Dr. Dodi Mossafer

Summary

Sustaining value with AI requires more than pilots and dashboards. It means embedding AI in processes, aligning ownership, and ensuring transparency. The outcome: durable improvements in decision quality, efficiency, and customer outcomes that stand up to leadership change and audit scrutiny.

1) The Framework

Artificial Intelligence Value Governance

Extend the benefits register to include artificial intelligence outcomes with clear ownership.
Trace each model to financial results, operating performance, and customer outcomes.
Set quarterly adoption and value gates that determine continuation, scaling, or retirement.

Adoption in the Flow of Work

Deploy role-based artificial intelligence copilots that embed directly into daily tasks.
Require human oversight checkpoints for critical decisions that affect financial and reputational outcomes.
Measure adoption not by licenses issued but by daily transactions influenced.

Lifecycle and Risk Management

Monitor bias, fairness, and model drift with scheduled retraining and sign-offs.
Define clear retirement or replacement pathways for underperforming artificial intelligence systems.
Maintain complete lineage of model inputs, outputs, and usage evidence for audit readiness.

2) Working Principles

Evidence over intent: no artificial intelligence benefit is real unless measured and validated.
Adoption equals value: sustained value comes only from daily, repeatable use across roles.
Guardrails protect trust: fairness, privacy, and transparency must be designed into each system.
Lifecycle discipline: artificial intelligence assets require governance similar to financial assets.

3) Use Cases

Finance and Capital Management

Artificial intelligence projects that sustain financial discipline and protect enterprise value.

Project: Automated variance analysis to identify deviations and enforce corrective action.
Project: Artificial intelligence–driven cash flow forecasting with scenario planning to protect liquidity.
Project: Capital allocation engine that scores investment requests against expected return and risk.

Operations and Supply Chain

Artificial intelligence projects that sustain resilience and operational efficiency.

Project: Predictive maintenance scheduling to minimize costly downtime.
Project: Demand forecasting linked directly to procurement and production planning.
Project: Inventory optimization system to reduce waste and maintain service levels.

Workforce and Service Quality

Artificial intelligence projects that sustain employee productivity and customer trust.

Project: Employee support copilots to reduce administrative workload and sustain engagement.
Project: Customer service copilots that provide consistent answers with audit trails.
Project: Workforce planning models to align talent supply with long-term strategies.

4) Artificial Intelligence Sustainment Metrics

Lead Indicators

Adoption by role and process measured daily.
Time to detect and resolve model drift or errors.
User trust and satisfaction scores in artificial intelligence outputs.
Cycle time reduction in decision processes influenced by artificial intelligence.

Lag Indicators

Profit and loss impact including gross margin and cost to serve.
Operational performance including uptime, throughput, and delivery accuracy.
Customer satisfaction and loyalty trends linked to artificial intelligence adoption.
Audit and compliance findings related to artificial intelligence systems.

5) Operating Rhythm and Signal Loops

Cadence

Weekly reviews of artificial intelligence adoption rates and incident logs.
Monthly value realization reviews with Finance and Operations leadership.
Quarterly lifecycle reviews to approve continuation, scaling, or retirement.

Signal Mechanics

Dashboards linking artificial intelligence use to financial and operational metrics.
Counterfactual analysis comparing outcomes with and without artificial intelligence.
Ownership logs tracking who acted on recommendations and the observed results.

6) Common Failure Modes

Pilot purgatory: artificial intelligence systems remain in testing and never scale into operations.
Opaque systems: no explainability or evidence trails, leading to loss of trust.
Bias blind spots: insufficient safeguards against fairness, privacy, and compliance risks.
Lack of ownership: artificial intelligence is treated as a technology initiative, not a business accountability.

7) Implementation Playbook — First 90 Days

Mobilize (Weeks 0–2)

Confirm executive sponsor, product owner, finance partner, and risk lead.
Define top three value hypotheses and counterfactual baselines.
Stand up a benefits register and adoption telemetry pipeline.
Select one flow-of-work copilot and one decisioning model to start.

Embed (Weeks 3–6)

Integrate into the target workflow with least-friction UX and approvals.
Institute human-in-the-loop checkpoints for high-impact decisions.
Enable role-based training and “show your work” explainability patterns.
Begin weekly adoption / incident reviews; track drift alerts.

Scale & Prove (Weeks 7–12)

Run controlled rollouts; expand to adjacent roles or sites.
Publish first counterfactual results vs. baseline with finance sign-off.
Decide continue/scale/retire using value gates and risk posture.
Lock a quarterly sustainment plan (retraining, audits, refresh backlog).

8) Operating Model & Accountabilities

Clear ownership keeps value durable. Assign named leaders and measured outcomes.

Roles

Executive Sponsor: sets outcome targets, clears roadblocks, owns enterprise narrative.
Product Owner: prioritizes backlog, ensures flow-of-work fit, tracks adoption.
Finance Partner: validates counterfactuals, signs off on benefit realization.
Risk & Compliance: enforces policy, manages model risk, audit readiness.
Data/ML Lead: performance, drift, retraining, and data lineage.
Change & Enablement: role-based training, communications, playbooks.

Success Signals

Adoption > 70% in target roles within 90 days.
Value realization tracked monthly with finance sign-off.
Zero critical incidents without explainability or override path.
Quarterly lifecycle reviews completed with actions closed.

9) Practical Artifacts

Artificial intelligence benefits register with owners, baselines, and evidence trails.
Model lifecycle tracker documenting bias checks, drift alerts, retraining, and retirement.
Adoption dashboards segmented by role and process area.
Counterfactual templates for proving realized value.
Governed playbook for artificial intelligence including privacy, security, and audit protocols.

10) About the Author

Dr. Dodi Mossafer is a corporate strategy and transformation advisor. Experience spans financial governance, artificial intelligence integration, and enterprise value sustainment across multiple industries. Academic work includes decision sciences, finance digitalization, and readiness for artificial intelligence adoption.

11) Use and Citation

Cite as: “Dr. Dodi Mossafer, DBA — Sustaining Enterprise Value with Artificial Intelligence (Advisory Point of View), 2025.” Independent perspective; suitable for academic and industry reference with attribution.