Here, enterprise FDE means a collaborative delivery model close to the operating environment. Engineers and business owners clarify outcomes and constraints, validate with real data, and integrate what works. It fits cross-functional problems whose requirements become clearer through evidence.
How it differs from common delivery models
| Model | Starting point | Typical output | Best fit |
|---|---|---|---|
| Consulting | Diagnosis and decision support | Research, roadmap, policy | Judgment and alignment |
| Outsourcing | Defined specification | Software within an agreed scope | Stable requirements |
| SaaS | Standard need | Configurable product | Process matches product |
| Enterprise FDE | Outcome and field constraints | Prototype, integration, evaluation, operating method | Cross-system uncertainty |
When FDE is a better fit
- The problem spans teams or systems.
- Data quality and permissions can only be verified in practice.
- The outcome is clear but the implementation needs discovery.
- The solution must change with real usage.
When specifications and acceptance criteria are already stable, a conventional product or software project may be more economical.
What an FDE engagement should leave behind
Beyond a demo, the engagement should produce a problem definition, data and permission inventory, evaluation set, interfaces, logs, human handoff rules, rollback steps, and an operating guide.
Control scope and risk
Agree on stage gates, owners, and stop conditions at the beginning. Reassess risk whenever permissions, data scope, or automated actions expand. High-impact actions should retain human confirmation and auditable recovery.
Frequently asked questions
Is FDE the same as on-site development?
No. On-site describes location; FDE describes collaborative discovery, validation, integration, and operation around a real business problem.
Is FDE always better than SaaS?
No. Use mature SaaS for standard needs; use FDE when field differences and uncertainty justify it.
How is success measured?
Measure business outcomes, quality, adoption, human workload, and risk—not only feature completion.
Sources and further reading
- NIST AI Risk Management Framework
Lifecycle governance and risk management reference.
- DeftGlow delivery method
Our four delivery stages.