Embedding AI into Workflows — POV by Dr. Dodi Mossafer

Summary

Embedding AI means redesigning the workflow around AI decision points, not parking assistants at the edge. This POV outlines how to identify high-leverage decisions, insert AI checkpoints, define ownership, and measure adoption in the flow of work without compromising safety or auditability.

1) The Framework

Map the Decision Points

Identify decisions in each workflow that are high friction or highly variable.
Define inputs, outputs, accountable owners, and required service level agreements.
Choose the Artificial Intelligence assist type: retrieve, predict, generate, or classify.

Design the Checkpoints (Human in the Loop)

Specify where human judgment gates outcomes and how exceptions are handled.
Set the control baseline: data lineage, explainability, and a clear rollback path.
Place the assistant inside the tool where work already happens (for example, enterprise resource planning, enterprise performance management, customer relationship management, or manufacturing execution systems).

Measure & Iterate

Track adoption by role and impact on cycle time, quality, and accuracy.
Map value to financial results, cash impacts, and service outcomes.
Hold quarterly continue or stop gates and schedule model refresh with evidence.

2) Working Principles

In the flow: embed the assistant where work already happens; avoid switching between systems.
Small, then scale: start with one decision and one checkpoint; expand based on evidence.
Design for audit: keep reproducible inputs, outputs, and full lineage.
Guardrails over restrictions: enable speed with clear boundaries and reliable rollbacks.

3) Use Cases & Applications

Airlines and Aviation

Embed assistants in crew scheduling, maintenance planning, and disruption recovery.

Schedule suggestions that respect union rules and aircraft rotation constraints with a human approval gate.
Maintenance predictions that create prioritized work orders inside the maintenance execution system.
Day-of-operations recovery recommendations that balance on-time performance and cost with evidence trails.

Hospitality and Hotels

Assist front-office and back-office teams inside the property management and revenue tools.

Guest service assistants that guide resolution steps and document decisions in the case record.
Housekeeping and maintenance routing that adapts to arrivals, departures, and service requests.
Revenue suggestions that propose price changes within guardrails and require manager sign-off.

Ports and Shipping Logistics

Place assistants in yard, berth, and gate operations to speed flow while preserving safety.

Container stacking and retrieval suggestions with visibility to customs holds and hazardous materials rules.
Berth scheduling recommendations that consider tides, crane availability, and labor windows.
Gate congestion forecasts with dispatch prompts and documented overrides by supervisors.

3a) ERP Examples — Oracle Fusion, SAP S/4HANA, Workday

Embed assistants at high-leverage decision checkpoints inside the ERP screens (not at the edge). Design human-in-the-loop gates, capture lineage, and measure adoption by role. Below are concrete placements that preserve auditability and service levels.

Oracle Fusion Cloud ERP

Focus on Procure-to-Pay, Order-to-Cash, and Record-to-Report.

Decision point — POs: Suggest supplier, terms, and price breaks in the Purchase Order page; buyer approves/edits with a required comment trail.
AP exceptions: Classify invoice exceptions; propose GL coding; route to the AP analyst for a one-click accept/override with reason logging.
Collections: Recommend contact cadence and promise-to-pay offers; collections agent accepts, defers, or escalates; actions write back to Collections workbench.
Close & forecast: Surface variance explanations in Account Reconciliation and Narrative Reporting; controller signs off via a checkpoint checklist.

Checkpoints & lineage: all AI suggestions saved as draft objects; approvals use existing workflow; evidence stored in attachment/notes with user/time/model hash.

Signals: cycle time (PO approval, AP exception), % auto-coded invoices, forecast error/bias, DSO change.

SAP S/4HANA

Focus on MM/Ariba (P2P), SD (O2C), and FI/CO (R2R/FP&A).

Material sourcing: In Manage Purchase Requisitions, suggest source-of-supply and delivery windows; buyer confirms in-app; change log captures deltas.
ATP & pricing: In Create Sales Order (VA01/Fiori), propose sub-stitution, split-ship, or price breaks; sales ops approves within tolerance bands.
CO-PA insights: Generate variance narratives for contribution margin; controller accepts edits into profit analysis; posting requires documented rationale.
Ariba exceptions: Classify and route policy exceptions with recommended remediation; category manager acknowledgment required.

Checkpoints & lineage: use standard workflow (Flexible Workflow/Fiori My Inbox); store AI artifacts in document flow and change documents with user stamps.

Signals: PR-to-PO conversion time, first-pass match rate, order cycle time, price realization, close duration.

Workday (Financials & HCM)

Focus on Expense/AP, Planning, and HCM Workforce.

Expense audit: Flag out-of-policy items with suggested corrections; manager approves in the review task; rationale logged on the expense line.
Supplier invoice: Propose default company/account/project; AP specialist confirms; exceptions auto-create a task in the Business Process with notes.
Workforce planning: Recommend req prioritization and internal mobility candidates; recruiter/HRBP accepts/declines; actions update Req/Move transactions.
Planning (Adaptive): Generate driver-based forecast suggestions; FP&A owner promotes to version after ties-out; justification captured in the narrative field.

Checkpoints & lineage: embed decisions in Workday tasks; approvals via Business Process steps; evidence in audit trail and attachments with model/version tags.

Signals: expense exception rate, AP touchless %, time-to-fill/transfer, forecast bias and MAPE, plan cycle time.

Integration Notes (non-disruptive)

In-tool first: surface suggestions in the native screen (Fiori tiles, Fusion pages, Workday tasks) to avoid context switching.
HITL by default: require accept/edit/override with reason codes; enforce tolerance bands and rollback paths.
Evidence: store inputs/outputs, user, timestamp, and model/version hash as part of the transaction or attached document.
Security: inherit ERP role-based access; restrict training data to approved fields; mask PII where not required.

3b) Realities vs. Hype — What Works Now

Focus on embedded assistants that improve a specific decision and write evidence back into the system of work. Avoid edge pilots that cannot be audited or measured.

What is Real Today

Retrieval + summarization in-tool: policies, contracts, SOPs surfaced inline with citations.
Classification & coding: invoice GL suggestions, case routing, exception triage with reason codes.
Forecast assist: driver-based hints for demand, cash, or staffing with human sign-off.
Narrative generation: variance explanations and close notes from structured data.
Next-best action: collector call steps, sourcing alternatives, service playbooks within tolerance bands.

What to Treat with Caution

Autonomous posting: fully automated journal entries or pricing changes without HITL.
Model sprawl: many small assistants with no ownership, lineage, or rollback plan.
Chat over workflow: free-form chatbots that bypass controls, SLAs, or approval chains.
Unbounded data use: training on sensitive fields without masking or consent.
One-click “transformation” claims: results without baseline, counterfactuals, or adoption tracking.

Make It Work in Production

Place the checkpoint: choose the exact screen and step; define accept, edit, override.
Guardrails: tolerance bands, required reason codes, and clear rollback.
Evidence: store inputs, outputs, user, time, and model/version hash against the record.
Security: inherit ERP roles; restrict fields; mask PII not needed for the decision.
Measurement: track adoption by role and link impact to cycle time, quality, and cash.

Where to Start (90-Day Track)

Finance

Invoice auto-coding with HITL and audit trail.
Variance narratives tied to forecast drivers.
Collections playbooks with acceptance logging.

Operations

Work order prioritization with reason codes.
Schedule suggestions within SLA bands.
Spare parts risk flags with override capture.

CX / HR

Case triage and knowledge answers with citations.
Expense audit suggestions with in-task corrections.
Req prioritization and internal mobility prompts.

4) Possible Metrics to Track (by domain)

Finance

Total close time; forecast error and forecast bias.
Percentage of variance actions closed on time.
Days sales outstanding, days payable outstanding, and inventory turns.

Operations

Schedule adherence, throughput, and scrap rate or defect rate.
Mean time between failures and mean time to repair; unplanned downtime.
Right-first-time rate and percentage of rework.

Customer Experience

First contact resolution and average handle time.
Customer satisfaction and net promoter score trends.
Escalation rate, case reopen rate, renewal and retention changes, and churn.

5) Measurement Cadence & Signal Loops

Cadence

Weekly adoption review that tracks usage by role and removes blockers.
Monthly results review that compares outcome changes with the baseline.
Quarterly model review for drift, continue or stop decisions, and roadmap updates.

Signal Loop Design

Checkpoint to assistant suggestion to decision to log to evidence, all captured in the system of work.
Automated alerts for exceptions and service level agreement breaches with named owners.
An audit package generated each quarter with sources, assumptions, and outcomes.

6) Common Failure Modes

Edge assistants: the assistant sits outside the tool of work, adoption suffers, and evidence is lost.
No judgment gate: high-risk steps lack human review and a reliable rollback plan.
Feature-first pilots: experiments proceed without lineage, controls, or service level agreements.
Unmeasured change: there is no role-based adoption tracking or link to outcomes.

7) Practical Artifacts

A workflow map that marks decision checkpoints, owners, and evidence capture.
A human-in-the-loop design specification covering explainability, thresholds, and rollback rules.
An integration blueprint for the enterprise resource planning, enterprise performance management, customer relationship management, or manufacturing execution system screens.
An adoption dashboard that shows usage, assist rate, and exception rate by role.
A benefits register with baselines, counterfactuals, and mapping to financial results and cash.

8) About the Author

Dr. Dodi Mossafer is a corporate strategy and transformation advisor. His work centers on embedding artificial intelligence into enterprise decision workflows with auditable outcomes, sustained adoption, and practical safeguards. Academic contributions include decision sciences, finance digitalization, and artificial intelligence readiness frameworks.

9) Use & Citation

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