Fragmented requirements
Important details are often distributed across design documents, mapping workbooks, decision logs, and sample files.
Sumeet RastogiEnterprise Applications Leader
Executive profile ↗Personal leadership experiment · 2026
AI-Enabled
Enterprise Delivery
Exploring how AI can strengthen delivery quality, governance, and decision-making across enterprise application programs.
As part of my personal learning journey, I designed an AI-enabled delivery review prototype that analyzes fictional solution designs, mapping workbooks, decision logs, file layouts, and delivery standards. The objective is to identify delivery risks earlier and improve the quality of decisions before development begins.
01 / Business challenge
The delivery challenge
Enterprise delivery artifacts tell one story across many files. Risk often sits in the gaps between them.
Late risk discovery
Missing mappings and contradictory requirements may only become visible during development or testing.
Leadership bottlenecks
Senior reviewers spend time on repetitive document comparison rather than architecture, risk, and decisions.
Inconsistent review quality
Review depth may depend on individual experience, available time, and documentation quality.
02 / Operating model
AI first pass.
Human judgment. Better outcomes.
- 01Project documentation→
- 02Custom GPT first-pass review→
- 03Risk and gap identification→
- 04Human validation→
- 05Delivery readiness decision→
- 06Continuous learning
The goal is not autonomous delivery. The goal is stronger decision quality, earlier risk visibility, and more consistent governance.
Prototype / Custom GPT
AI-Enabled Workday Delivery Review Assistant
A focused assistant configured to review entirely fictional delivery documentation, surface risks and development blockers, draft critical clarification questions, and generate early test scenarios for human review.
Fictional dataSource-groundedHuman validation
03 / Capabilities
What the prototype explores
Delivery-readiness assessment
Cross-document comparison
Mapping-gap detection
Clarification-question generation
Test-scenario generation
Executive readiness summaries
04 / Accountability
Clear accountability by design
AI supportsHumans remain accountable for
First-pass document comparisonUnderstanding business context
Identifying potential gapsConfirming the accuracy of findings
Drafting clarification questionsCommunicating with stakeholders
Generating test scenariosApproving expected outcomes
Recommending readiness statusMaking the final delivery decision
Summarizing risksCustomer and production commitments
AI recommends. Leaders decide. Teams deliver.
Responsible by design
Guardrails are part of the architecture.
- Entirely fictional data
- No customer information
- No production connectivity
- No autonomous system updates
- Human validation required
- Source-grounded findings
- Transparent, reviewable outputs
This is a personal learning project created entirely with fictional information. It is not connected to a customer tenant, production environment, or confidential project material.
05 / Learning & roadmap
What I learned
- 01AI can create significant value before development begins.
- 02Governance is more important than automation.
- 03Strong documentation becomes even more important in AI-enabled delivery.
- 04Human accountability cannot be delegated to a model.
- 05Accuracy, false positives, and human overrides matter as much as time saved.
Where this exploration could go next
Delivery Design Review Assistant
Completed prototype
Test Planning Assistant
Concept validated
AMS Incident Triage
Future exploration
Delivery Knowledge Assistant
Future exploration
AI-Enabled Delivery Governance Model
Future exploration